{ "schemaVersion": "deck-intelligence.v1", "id": "019dd923-5fec-763b-95d0-451a96d1edee", "slug": "bi-7495499d6a6908b8", "title": "AgileRL raised $7.5M with this pitch deck to help firms train AI", "subtitle": "AgileRL", "author": "AgileRL", "pageCount": 12, "kind": "pitchdeck", "orientation": "landscape", "aspectRatio": 1.6, "document": { "id": "019dd923-5fec-763b-95d0-451a96d1edee", "slug": "bi-7495499d6a6908b8", "title": "AgileRL raised $7.5M with this pitch deck to help firms train AI", "rawTitle": "AgileRL raised $7.5M with this pitch deck to help firms train AI", "authorId": "Pitchdecks", "authorName": "AgileRL", "authorDisplay": "AgileRL", "kind": { "slug": "pitchdeck", "label": "Pitch deck" }, "sourceType": { "slug": "strategy_consulting", "label": "Strategy consulting" }, "fileName": "Agilerl-bi-74954.pdf", "sourceUrl": "https://www.businessinsider.com/agilerl-funding-pitch-deck-ai-training-reinforcement-learning-2026-1", "sourceDate": "2026-01-07 00:00:00", "presentationDate": null, "ingestedAt": "2026-04-27 09:51:31.752+00", "status": "ready", "pageCount": 12, "orientation": "landscape", "aspectRatio": 1.6, "targetCompany": null, "targetTicker": null, "metadata": { "local_root": "/tmp/legacy-images/bi-7495499d6a6908b8", "origin_corpus": "consulting" } }, "coverage": { "pageCount": 12, "pagesIndexed": 12, "pagesWithImages": 12, "pagesWithMetadata": 12, "imageCoveragePct": 100, "metadataCoveragePct": 100, "componentCount": 83, "chartCount": 3, "metricSlides": 0, "toolMatches": 7, "documentToolMatches": 0, "frameworkMatches": 5, "arcMatches": 1, "beatMatches": 5, "loopMatches": 3, "patternMatches": 0, "storymakersMatch": false, "scoreAvailable": false, "reviewAvailable": false, "activistThesisAvailable": false, "pitchdeckMetadataAvailable": true, "pitchdeckProfileAvailable": true }, "blocks": [], "matches": { "arcs": [ { "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "evidence": "The deck follows a structure similar to Sequoia's pitch format, with sections on problem, solution, market sizing, competitive analysis, and team", "isPrimary": true, "confidence": 0.8, "extraction": { "at": "2026-07-16 21:41:16.160725+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "beats": [ { "to": 3, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 3, "name": "Problem", "beatId": "c26c6206-7d76-4bec-a6f8-b889b3059df4", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Problem", "slug": "sequoia-pitch-problem", "status": "active", "canonId": "019dd9b8-1ed4-767b-8e05-40e733bd2311", "version": 1, "description": null }, "evidence": "The problem statement is presented on page 3", "position": 0, "isPrimaryArc": true, "parentBeatType": { "name": "Complication", "slug": "complication", "status": "active", "canonId": "019dd9b8-0275-7709-b2d6-4f77c3fc7579", "version": 1, "description": "The point in the narrative where the audience should feel discomfort. Required for argumentative arcs." }, "alignedBlockIds": null, "matchConfidence": 0.8 }, { "to": 4, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 4, "name": "Solution", "beatId": "0fe803bc-5440-4751-a0d7-4dbf68b044a9", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Solution", "slug": "sequoia-pitch-solution", "status": "active", "canonId": "019dd9b8-1fb1-71d9-a260-07738d7f9c2f", "version": 1, "description": null }, "evidence": "The solution is presented on page 4", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 0.8 }, { "to": 8, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 8, "name": "Market Size", "beatId": "2dc8d091-a265-420c-a817-5e5c0cbac86a", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Market Size", "slug": "sequoia-pitch-market-size", "status": "active", "canonId": "019dd9b8-20e6-729c-bc28-05442906d032", "version": 1, "description": null }, "evidence": "Market sizing is presented on page 8", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 0.8 }, { "to": 9, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 9, "name": "Competitive Analysis", "beatId": "ede65a75-39eb-4679-bb03-a02e96f46656", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Competition", "slug": "sequoia-pitch-competition", "status": "active", "canonId": "019dd9b8-21bd-7407-8707-bdf217ed9c76", "version": 1, "description": null }, "evidence": "Competitive analysis is presented on page 9", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 0.8 }, { "to": 11, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 11, "name": "Team", "beatId": "6ce1e4e8-cab9-48dc-9284-815b3e5eb912", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Team", "slug": "sequoia-pitch-team", "status": "active", "canonId": "019dd9b8-23c9-746f-aaf9-2f5d83892cf3", "version": 1, "description": null }, "evidence": "The team is presented on page 11", "position": 4, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 0.8 } ], "loops": [ { "to": 2, "from": 2, "loop": { "name": "15_why_now", "slug": "15-why-now", "status": "active", "bestFor": "Sales pitches, fundraising, requesting immediate budget approval", "canonId": "019dd956-6c00-70cd-bd42-6bacc4af726f", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Context (Trends) -> The Trigger Event -> The Window of Opportunity", "description": "Create temporal urgency by proving that the window of opportunity is opening or closing", "familyLabel": null, "categoryName": "Urgency", "categorySlug": "urgency" }, "matchId": "f90ebde1-64a3-41bf-8c26-7e7733f10c61", "evidence": "The 'why now' section on page 2 sets up the urgency for investment", "position": 0, "objective": "Explain why now is the right time for investment", "confidence": 0.8, "extraction": { "at": "2026-07-16 21:41:16.392346+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } }, { "to": 4, "from": 3, "loop": { "name": "27_cost_of_inaction", "slug": "27-cost-of-inaction", "status": "active", "bestFor": "Urgent budget requests, compliance, risk mitigation", "canonId": "019dd956-70d1-7395-a15b-857ba858b394", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point", "description": "Quantify what happens if the audience does nothing", "familyLabel": null, "categoryName": "Urgency", "categorySlug": "urgency" }, "matchId": "e86cd633-ee04-4745-9bd7-5e4a1a9da9c6", "evidence": "The problem statement and solution sections imply the cost of inaction", "position": 1, "objective": "Highlight the cost of not investing in AI model training", "confidence": 0.6, "extraction": { "at": "2026-07-16 21:41:16.431657+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } }, { "to": 6, "from": 5, "loop": { "name": "47_quick_win_big_bet", "slug": "47-quick-win-big-bet", "status": "active", "bestFor": "Transformation planning, 100-day plans, resource allocation", "canonId": "019dd956-790d-708d-983e-ef9bbc68f741", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Full List -> Quick Wins (Low effort, High impact) -> Big Bets (High effort, High impact) -> Sequenced Roadmap", "description": "Separate initiatives into immediate wins and longer-term strategic bets", "familyLabel": null, "categoryName": "Consulting Framework", "categorySlug": "consulting-framework" }, "matchId": "6ade56bd-f936-445e-8273-afc965b9dfa0", "evidence": "The traction and solution sections suggest a plan for quick wins and big bets", "position": 2, "objective": "Present a clear plan for quick wins and big bets", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:41:16.471087+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "tools": [ { "tool": { "name": "Slide recipe: Cover", "slug": "cover-slide", "status": "active", "bestFor": null, "canonId": "019df269-2a08-767d-9128-e7431e6fe8a3", "version": 1, "bodyDocId": "019df269-29e5-7738-b5fa-6892e5dc8e65", "description": "The first ten seconds. Build it deliberately.", "familyLabel": "slide-recipe", "categoryName": "Slide", "categorySlug": "slide" }, "agent": "designer", "layer": "slide", "agents": [ "designer" ], "matchId": "a878b9b1-6dd9-4cea-a4f9-9117c1fd9874", "evidence": "AgileRL", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-17 03:24:55.654234+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 1, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "AIDA Model", "slug": "aida-model", "status": "active", "bestFor": null, "canonId": "019dd956-86fc-773c-813f-d416714b8413", "version": 1, "bodyDocId": null, "description": "Attention, Interest, Desire, Action - classic persuasion sequence", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "bdb12a6d-1b1f-456c-940f-0fa6d0647b30", "evidence": "The slide presents a clear problem (achieving superhuman performance in AI models), a solution (reinforcement learning), and a call to action (leveraging this technology).", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:24:55.382764+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 2, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Problem Statement Canvas", "slug": "problem-statement-canvas", "status": "active", "bestFor": null, "canonId": "019dd956-839d-744b-ad11-9bf3e3642a18", "version": 1, "bodyDocId": null, "description": "Structured definition of the problem: What? Who? Where? When? Scale?", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "c023df1a-b26c-4cc4-b962-2a1868ea18f9", "evidence": "The slide clearly states a problem (existing large AI models are not good enough for businesses) and provides context.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:24:55.464767+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "AIDA Model", "slug": "aida-model", "status": "active", "bestFor": null, "canonId": "019dd956-86fc-773c-813f-d416714b8413", "version": 1, "bodyDocId": null, "description": "Attention, Interest, Desire, Action - classic persuasion sequence", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "a87bf074-ba8d-403d-96b0-8c9a433d28af", "evidence": "The slide presents a clear attention-grabbing title, and lists key metrics and features, attempting to generate interest and desire for AgileRL's dev-tools.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 22:33:07.948686+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 5, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Social Proof", "slug": "social-proof", "status": "active", "bestFor": null, "canonId": "019dd956-990d-7620-82b3-6dd286f34568", "version": 1, "bodyDocId": null, "description": "People adapt behavior based on what others do - show adoption and testimonials", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "78d80f25-0898-42ce-a2e0-ddba1118a144", "evidence": "The slide mentions 'Widely used by businesses in robotics, finance, logistics and other industries' to establish credibility.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 22:33:07.990576+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 5, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Problem-Agitate-Solve (PAS)", "slug": "problem-agitate-solve-pas", "status": "active", "bestFor": null, "canonId": "019dd956-869f-7564-8d42-de54ed29ab90", "version": 1, "bodyDocId": null, "description": "Identify problem, agitate the pain, present the solution", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "4f592c05-4c6a-4049-bde1-b32a2cd9177c", "evidence": "The slide presents a solution (Arena) to a problem (RL development workflow challenges), but does not explicitly agitate the problem.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 22:33:08.072797+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Social Proof", "slug": "social-proof", "status": "active", "bestFor": null, "canonId": "019dd956-990d-7620-82b3-6dd286f34568", "version": 1, "bodyDocId": null, "description": "People adapt behavior based on what others do - show adoption and testimonials", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "73ac3741-7687-489f-9adf-b451d86bf46a", "evidence": "The slide showcases logos of various universities and companies that trust AgileRL, establishing credibility.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.8, "extraction": { "at": "2026-07-16 22:33:08.1557+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [ { "matchId": "196a311f-50d8-41e2-bf09-4684e7b3441d", "evidence": "Uses the trend of RL-trained models to argue for the necessity of RL in future AI development.", "framework": null, "confidence": 0.8, "extraction": { "at": "2026-05-02 16:17:55.895+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 2, "frameworkName": "argument-from-analogy" }, { "matchId": "56e17c72-4c74-46f0-b8de-dd1fe04b61c3", "evidence": "Frames the current problem (lack of RL tooling) and bridges to the company's solution (AgileRL infrastructure).", "framework": null, "confidence": 0.8, "extraction": { "at": "2026-05-02 16:17:53.244+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "frameworkName": "before-after-bridge" }, { "matchId": "6d0d5da4-9448-40bd-aa2a-de52e7a6e5af", "evidence": "The slide explicitly labels market segments as TAM, SAM, and SOM.", "framework": null, "confidence": 1, "extraction": { "at": "2026-05-02 16:17:53.606+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 8, "frameworkName": "TAM-SAM-SOM" }, { "matchId": "669ba307-2891-4b66-bf41-2f1cf652be60", "evidence": "Feature comparison matrix used to differentiate product capabilities.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 9, "frameworkName": "comparison-frame" }, { "matchId": "d408a4e5-6498-48c8-a632-931505070766", "evidence": "Three-pillar approach to market entry and growth", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "frameworkName": "go-to-market-strategy" } ], "patterns": [] }, "storymakers": null, "score": null, "review": null, "activistThesis": null, "pitchdeck": { "metadata": { "author": "Robert Scammell", "fireId": "bi-7495499d6a6908b8", "sector": null, "deckKind": "fundraise", "roundRaw": null, "amountRaw": null, "amountUsd": null, "scrapedAt": "2026-04-27 09:51:31.752+00", "sectorRaw": null, "sourceUrl": "https://www.businessinsider.com/agilerl-funding-pitch-deck-ai-training-reinforcement-learning-2026-1", "deckKindAt": "2026-04-28 07:19:06+00", "highlights": "AgileRL has created a platform for engineers to train AI using reinforcement learning, a decades-old technique that's having a comeback.", "slideCount": 12, "companyName": "Agilerl", "description": "AgileRL has created a platform for engineers to train AI using reinforcement learning, a decades-old technique that's having a comeback.", "displayName": "AgileRL", "publishDate": "2026-01-07", "roundSeries": null, "articleTitle": "AgileRL raised $7.5M with this pitch deck to help firms train AI", "leadInvestors": null, "deckKindSignal": "raised", "metaExtractedAt": "2026-04-28 07:16:02+00" }, "profile": { "scores": { "yc": 0.51, "a16z": 0.53, "docsend": 0.3424, "hoffman": 0.3734, "mcclure": 0.61, "sequoia": 0.6766, "kawasaki": 0.35, "nextview": 0.3974, "slidebean": 0.3333, "founder_institute": 0.425 }, "nSlides": 12, "sequence": [ "cover", "why_now", "problem_statement", "solution", "traction", "solution", "customer_logos", "market_sizing", "competitive_analysis", "go_to_market", "team_bio", "vision" ], "breakdown": { "yc": { "order": 0.6, "signal": 0.08, "jaccard": 0.5 }, "a16z": { "order": 0.6, "signal": 0.1, "jaccard": 0.5 }, "docsend": { "order": 0.3636, "signal": 0, "jaccard": 0.4667 }, "hoffman": { "order": 0.3636, "signal": 0.05, "jaccard": 0.4286 }, "mcclure": { "order": 0.7, "signal": 0.15, "jaccard": 0.5 }, "sequoia": { "order": 0.6364, "signal": 0.2, "jaccard": 0.5714 }, "kawasaki": { "order": 0.5, "signal": 0, "jaccard": 0.4 }, "nextview": { "order": 0.5556, "signal": 0, "jaccard": 0.4615 }, "slidebean": { "order": 0.3333, "signal": 0, "jaccard": 0.4667 }, "founder_institute": { "order": 0.5833, "signal": 0, "jaccard": 0.5 } }, "matchedAt": "2026-04-27 21:51:54+00", "noveltyScore": 0.5234, "primaryScore": 0.6766, "noveltySignals": [ "rare slide types: vision, why_now", "non-canonical types: customer_logos" ], "primaryProfile": "sequoia", "secondaryScore": 0.61, "secondaryProfile": "mcclure" } }, "slides": [ { "page": 1, "type": "setup", "function": "front_matter", "imagePath": "https://imgproxy.kitesheet.com/115MaUCn7Ik75NIVk16kLKQUlkY7hEVCvZwg9fpHGJs/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy9iaS03NDk1NDk5ZDZhNjkwOGI4L3AwMDEuanBn", "rawType": "cover", "block": null, "metadata": { "slideType": "cover", "slideTypeCanon": { "name": "Cover", "slug": "cover", "status": "active", "canonId": "019de52d-05f2-729d-9f93-e0504f0a2410", "version": 1, "description": null }, "function": "front_matter", "functionCanon": { "name": "Front matter", "slug": "front_matter", "status": "active", "canonId": "019de52d-133d-73f0-9e26-201eda96b098", "version": 1, "description": null }, "density": "sparse", "densityScore": 8, "componentCount": 2, "textChars": 98, "nDataPoints": 0, "notes": null, "elementsJson": [ "headline_text", "subtitle_text" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:51.99+00", "seconds": 2.445, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/1", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-1", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": { "h": 0.08, "w": 0.5, "x": 0.25, "y": 0.55 }, "kind": "title", "text": "Accelerating reinforcement learning for building superhuman artificial intelligence systems", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:51.99+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8bc44d37-19f0-42d0-bb49-c7677d4560c3", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.36, "x": 0.32, "y": 0.315 }, "kind": "title", "text": "AgileRL", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:51.99+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e979d53d-850a-43af-a353-0b97aaf50f36", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Slide recipe: Cover", "slug": "cover-slide", "status": "active", "bestFor": null, "canonId": "019df269-2a08-767d-9128-e7431e6fe8a3", "version": 1, "bodyDocId": "019df269-29e5-7738-b5fa-6892e5dc8e65", "description": "The first ten seconds. Build it deliberately.", "familyLabel": "slide-recipe", "categoryName": "Slide", "categorySlug": "slide" }, "agent": "designer", "layer": "slide", "agents": [ "designer" ], "matchId": "a878b9b1-6dd9-4cea-a4f9-9117c1fd9874", "evidence": "AgileRL", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-17 03:24:55.654234+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 1, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [], "loops": [], "thumbSrc": "https://imgproxy.kitesheet.com/xeg95ubA2MTdZSD-ARHMD8ijMq1bposA57KzwSlpE0s/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy9iaS03NDk1NDk5ZDZhNjkwOGI4L3AwMDEuanBn", "imagePathAlt": "https://imgproxy.kitesheet.com/k7I6YZjk8fmFEDC1pKYGGHbUoNdYWwefcI9iGIzpE1Q/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy9iaS03NDk1NDk5ZDZhNjkwOGI4L3AwMDEucG5n", "thumbSrcAlt": "https://imgproxy.kitesheet.com/SDValeIn8liCgLBobK9M8bkG8XUmTmLIy6KMDx2y40w/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy9iaS03NDk1NDk5ZDZhNjkwOGI4L3AwMDEucG5n" }, { "page": 2, "type": "setup", "title": "The chart shows a trend of AI model performance over time, highlighting models trained with RL (specifically o1-pro and o3) as the top performers.", "function": "argue_timing", "rawType": "why_now", "block": null, "metadata": { "slideType": "why_now", "slideTypeCanon": { "name": "Why now", "slug": "why_now", "status": "active", "canonId": "019de52d-0f2e-718d-b8ad-256dd87582d0", "version": 1, "description": null }, "function": "argue_timing", "functionCanon": { "name": "Argue timing (why now)", "slug": "argue_timing", "status": "active", "canonId": "019de52d-16f8-74de-9ec2-900be07d90dd", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 80, "componentCount": 7, "textChars": 682, "nDataPoints": 15, "notes": "The chart shows a trend of AI model performance over time, highlighting models trained with RL (specifically o1-pro and o3) as the top performers.", "elementsJson": [ "headline_text", "action_title", "line_chart", "paragraph", "callout_box" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:55.895+00", "seconds": 6.175, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/2", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-2", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": { "h": 0.15, "w": 0.72, "x": 0.14, "y": 0.1 }, "kind": "callout", "text": "Reinforcement learning is the only way for AI models to acquire reasoning, planning and true agency", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:55.895+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1237383c-0c21-4251-8d9e-d15d8f08d836", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.72, "x": 0.14, "y": 0.85 }, "kind": "callout", "text": "However, very few companies have the expertise, time or money to leverage this powerful technology.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:55.895+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "dd27dcdf-2abe-4648-86a1-a30b15319165", "parentComponentId": null }, { "bbox": null, "kind": "callout", "text": "Modern LLMs can only achieve advanced reasoning capabilities through the use of reinforcement learning.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-7910775579a3", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.56, "x": 0.04, "y": 0.315 }, "kind": "chart", "text": "Model Performance: GPQA Score over time", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Line chart", "slug": "line", "status": "active", "canonId": "019de52c-ff62-778f-8f6c-a952ceae8677", "version": 1, "description": null }, "framework": null }, "subkind": "line", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:55.895+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f17ec422-9631-46d2-bd8f-3bd677fa6864", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "GPQA Score", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-7dbccaba833d", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.33, "x": 0.63, "y": 0.32 }, "kind": "paragraph", "text": "Beyond human knowledge. To achieve superhuman performance, AI models cannot rely solely on human-created examples. Reinforcement learning allows models to go beyond that by learning through trial and error. Modern LLMs can only achieve advanced reasoning capabilities through the use of reinforcement learning.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:55.895+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5b94e8a1-3294-4c3d-a225-25a4c01c2ddc", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.15, "x": 0.02, "y": 0.03 }, "kind": "title", "text": "Reinforcement learning", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:55.895+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "799f5d50-f5cb-4418-b189-70af2eb4c4e5", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "AIDA Model", "slug": "aida-model", "status": "active", "bestFor": null, "canonId": "019dd956-86fc-773c-813f-d416714b8413", "version": 1, "bodyDocId": null, "description": "Attention, Interest, Desire, Action - classic persuasion sequence", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "bdb12a6d-1b1f-456c-940f-0fa6d0647b30", "evidence": "The slide presents a clear problem (achieving superhuman performance in AI models), a solution (reinforcement learning), and a call to action (leveraging this technology).", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:24:55.382764+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 2, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [ { "matchId": "196a311f-50d8-41e2-bf09-4684e7b3441d", "evidence": "Uses the trend of RL-trained models to argue for the necessity of RL in future AI development.", "framework": null, "confidence": 0.8, "extraction": { "at": "2026-05-02 16:17:55.895+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 2, "frameworkName": "argument-from-analogy" } ], "arcBeats": [], "loops": [ { "to": 2, "from": 2, "loop": { "name": "15_why_now", "slug": "15-why-now", "status": "active", "bestFor": "Sales pitches, fundraising, requesting immediate budget approval", "canonId": "019dd956-6c00-70cd-bd42-6bacc4af726f", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Context (Trends) -> The Trigger Event -> The Window of Opportunity", "description": "Create temporal urgency by proving that the window of opportunity is opening or closing", "familyLabel": null }, "matchId": "f90ebde1-64a3-41bf-8c26-7e7733f10c61", "evidence": "The 'why now' section on page 2 sets up the urgency for investment", "position": 0, "objective": "Explain why now is the right time for investment", "confidence": 0.8, "extraction": { "at": "2026-07-16 21:41:16.392346+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 3, "type": "tension", "title": "Includes a testimonial/quote from a Wipro Director to validate the problem.", "function": "frame_problem", "rawType": "problem_statement", "block": null, "metadata": { "slideType": "problem_statement", "slideTypeCanon": { "name": "Problem statement", "slug": "problem_statement", "status": "active", "canonId": "019de52d-0879-768d-bba8-8da8a5a121d4", "version": 1, "description": null }, "function": "frame_problem", "functionCanon": { "name": "Frame problem", "slug": "frame_problem", "status": "active", "canonId": "019de52d-13db-753e-ad70-e9b2d0dab398", "version": 1, "description": null }, "density": "dense", "densityScore": 58, "componentCount": 5, "textChars": 715, "nDataPoints": 0, "notes": "Includes a testimonial/quote from a Wipro Director to validate the problem.", "elementsJson": [ "callout_box", "quote_block", "paragraph" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:53.244+00", "seconds": 3.377, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/3", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-3", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": { "h": 0.15, "w": 0.72, "x": 0.14, "y": 0.105 }, "kind": "callout", "text": "As AI adoption accelerates, businesses lack models tailored to their unique needs, and the ability to harness RL to build them", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.244+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "618f3ffe-cac9-499e-80b7-9486ba4ac9b6", "parentComponentId": null }, { "bbox": { "h": 0.35, "w": 0.46, "x": 0.09, "y": 0.318 }, "kind": "list", "text": "Existing large AI models (OpenAI, DeepSeek) are not good enough to be used by businesses for their critical tasks because they have not been trained on them. Reinforcement learning is required for specialising models to unique business use-cases, but current tooling is inadequate.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.244+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "058ac921-04c8-4ab5-95cc-d2e1d761aada", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.72, "x": 0.14, "y": 0.845 }, "kind": "paragraph", "text": "AgileRL has been solving this problem for the last 2 years, and has built the infrastructure required to support RL - already in use by MIT and Decision Lab.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.244+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c9eb2eb0-6735-404a-98ca-4e24c481449a", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.26, "x": 0.648, "y": 0.428 }, "kind": "quote", "text": "\"We cannot build agents today that understand business processes, and adapt to them in a reliable and autonomous fashion.\" Director of AI, Wipro", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": null, "subkind": { "name": "Testimonial", "slug": "testimonial", "status": "active", "canonId": "019de52c-fc21-71b6-9ac3-8266a64945e8", "version": 1, "description": null }, "framework": null }, "subkind": "testimonial", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.244+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f52d9ab4-e733-4e3e-92dc-76b8afdd377c", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.06, "x": 0.03, "y": 0.03 }, "kind": "title", "text": "Problem", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.244+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e0867cfb-2509-442c-b32b-28931a95a78c", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Problem Statement Canvas", "slug": "problem-statement-canvas", "status": "active", "bestFor": null, "canonId": "019dd956-839d-744b-ad11-9bf3e3642a18", "version": 1, "bodyDocId": null, "description": "Structured definition of the problem: What? Who? Where? When? Scale?", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "c023df1a-b26c-4cc4-b962-2a1868ea18f9", "evidence": "The slide clearly states a problem (existing large AI models are not good enough for businesses) and provides context.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:24:55.464767+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [ { "matchId": "56e17c72-4c74-46f0-b8de-dd1fe04b61c3", "evidence": "Frames the current problem (lack of RL tooling) and bridges to the company's solution (AgileRL infrastructure).", "framework": null, "confidence": 0.8, "extraction": { "at": "2026-05-02 16:17:53.244+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "frameworkName": "before-after-bridge" } ], "arcBeats": [ { "to": 3, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null }, "from": 3, "name": "Problem", "beatId": "c26c6206-7d76-4bec-a6f8-b889b3059df4", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Problem", "slug": "sequoia-pitch-problem", "status": "active", "canonId": "019dd9b8-1ed4-767b-8e05-40e733bd2311", "version": 1, "description": null }, "evidence": "The problem statement is presented on page 3", "position": 0, "isPrimaryArc": true, "parentBeatType": { "name": "Complication", "slug": "complication", "status": "active", "canonId": "019dd9b8-0275-7709-b2d6-4f77c3fc7579", "version": 1, "description": "The point in the narrative where the audience should feel discomfort. Required for argumentative arcs." }, "alignedBlockIds": null, "matchConfidence": 0.8 } ], "loops": [ { "to": 4, "from": 3, "loop": { "name": "27_cost_of_inaction", "slug": "27-cost-of-inaction", "status": "active", "bestFor": "Urgent budget requests, compliance, risk mitigation", "canonId": "019dd956-70d1-7395-a15b-857ba858b394", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point", "description": "Quantify what happens if the audience does nothing", "familyLabel": null }, "matchId": "e86cd633-ee04-4745-9bd7-5e4a1a9da9c6", "evidence": "The problem statement and solution sections imply the cost of inaction", "position": 1, "objective": "Highlight the cost of not investing in AI model training", "confidence": 0.6, "extraction": { "at": "2026-07-16 21:41:16.431657+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 4, "type": "resolution", "function": "present_solution", "rawType": "solution", "block": null, "metadata": { "slideType": "solution", "slideTypeCanon": { "name": "Solution", "slug": "solution", "status": "active", "canonId": "019de52d-0eb5-72ff-8b70-ba70a4199282", "version": 1, "description": null }, "function": "present_solution", "functionCanon": { "name": "Present solution", "slug": "present_solution", "status": "active", "canonId": "019de52d-15b8-71da-bd23-2b767850cbce", "version": 1, "description": null }, "density": "dense", "densityScore": 69, "componentCount": 6, "textChars": 669, "nDataPoints": 1, "notes": null, "elementsJson": [ "headline_text", "callout_box", "icon_grid" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:52.788+00", "seconds": 2.72, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/4", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-4", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": { "h": 0.15, "w": 0.72, "x": 0.14, "y": 0.105 }, "kind": "callout", "text": "AgileRL is critical for the AI revolution, arming businesses with the tools to create task-specific superintelligent and autonomous agents", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:52.788+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e0491307-878b-465c-bdae-60d9d6db3923", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.35, "x": 0.12, "y": 0.42 }, "kind": "list", "text": "Expert models: Excel far beyond existing foundation models with our novel reinforcement learning advancements and 10x faster training", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:52.788+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1240d0a5-f0f8-4d44-a74e-5569f4a226be", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.35, "x": 0.12, "y": 0.74 }, "kind": "list", "text": "Adaptable agents: Continuous improvement by learning from real-world interaction, with built-in monitoring, evaluation and retraining", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:52.788+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5f64a81f-d942-4909-aacd-b273951ab92d", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.35, "x": 0.53, "y": 0.74 }, "kind": "list", "text": "Trust: Full control and observability over agents solving critical business tasks, with safety and reliability guardrails", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:52.788+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "94f80719-203a-42ce-888e-dc9aba7bc2c2", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.35, "x": 0.53, "y": 0.42 }, "kind": "list", "text": "Capitalise on data: Agents that can do any task by training on existing business data and tools, and automatically discovering solutions", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:52.788+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e5344e0d-e4f1-4087-8163-fb98a7e7d645", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.06, "x": 0.03, "y": 0.03 }, "kind": "title", "text": "Solution", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:52.788+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fe6b027e-c985-4ff9-8dfe-a1c447f93f88", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 4, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null }, "from": 4, "name": "Solution", "beatId": "0fe803bc-5440-4751-a0d7-4dbf68b044a9", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Solution", "slug": "sequoia-pitch-solution", "status": "active", "canonId": "019dd9b8-1fb1-71d9-a260-07738d7f9c2f", "version": 1, "description": null }, "evidence": "The solution is presented on page 4", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 0.8 } ], "loops": [ { "to": 4, "from": 3, "loop": { "name": "27_cost_of_inaction", "slug": "27-cost-of-inaction", "status": "active", "bestFor": "Urgent budget requests, compliance, risk mitigation", "canonId": "019dd956-70d1-7395-a15b-857ba858b394", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point", "description": "Quantify what happens if the audience does nothing", "familyLabel": null }, "matchId": "e86cd633-ee04-4745-9bd7-5e4a1a9da9c6", "evidence": "The problem statement and solution sections imply the cost of inaction", "position": 1, "objective": "Highlight the cost of not investing in AI model training", "confidence": 0.6, "extraction": { "at": "2026-07-16 21:41:16.431657+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 5, "type": "evidence", "title": "The slide uses performance charts to demonstrate superiority over Optuna and lists key community metrics.", "function": "show_traction", "rawType": "traction", "block": null, "metadata": { "slideType": "traction", "slideTypeCanon": { "name": "Traction", "slug": "traction", "status": "active", "canonId": "019de52d-0edd-72ab-820f-3ebc71a9c542", "version": 1, "description": null }, "function": "show_traction", "functionCanon": { "name": "Show traction", "slug": "show_traction", "status": "active", "canonId": "019de52d-16a8-756a-9b38-a7d35420561d", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 79, "componentCount": 7, "textChars": 720, "nDataPoints": 6, "notes": "The slide uses performance charts to demonstrate superiority over Optuna and lists key community metrics.", "elementsJson": [ "action_title", "line_chart", "bullet_list", "callout_box" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:54.008+00", "seconds": 3.485, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/5", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-5", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Widely used by businesses in robotics, finance, logistics and other industries to speed up experimentation and training of models for production systems.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-fdf197b1ce2b", "parentComponentId": null }, { "bbox": { "h": 0.433, "w": 0.281, "x": 0.058, "y": 0.336 }, "kind": "chart", "text": "Test environment: LunarLander-v2 performance comparison", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Line chart", "slug": "line", "status": "active", "canonId": "019de52c-ff62-778f-8f6c-a952ceae8677", "version": 1, "description": null }, "framework": null }, "subkind": "line", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.008+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5cd49022-76aa-4837-bc34-a25f1cea4914", "parentComponentId": null }, { "bbox": { "h": 0.433, "w": 0.281, "x": 0.359, "y": 0.336 }, "kind": "chart", "text": "Test environment: Acrobot-v1 performance comparison", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Line chart", "slug": "line", "status": "active", "canonId": "019de52c-ff62-778f-8f6c-a952ceae8677", "version": 1, "description": null }, "framework": null }, "subkind": "line", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.008+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fbe5ac62-7212-4474-b4c9-4516698ee61b", "parentComponentId": null }, { "bbox": { "h": 0.458, "w": 0.355, "x": 0.605, "y": 0.311 }, "kind": "list", "text": "800+ GitHub stars, 220,000+ downloads, 320+ Discord members, 10x faster training, Single/multi-agent algorithms, Evolutionary GRPO, Contributions from leading AI institutions", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.008+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "619009bc-22b6-4fed-9fc4-1f57c12dd61f", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "GitHub stars: 800+", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e1-00ceeb765a60", "parentComponentId": null }, { "bbox": { "h": 0.083, "w": 0.706, "x": 0.147, "y": 0.864 }, "kind": "paragraph", "text": "Widely used by businesses in robotics, finance, logistics and other industries to speed up experimentation and training of models for production systems.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.008+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "966680f4-8aaa-4d62-8676-a5750c0e4b32", "parentComponentId": null }, { "bbox": { "h": 0.125, "w": 0.706, "x": 0.147, "y": 0.103 }, "kind": "title", "text": "AgileRL's dev-tools revolutionise the entire RLOps pipeline, with monumental advancements in science and engineering", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.008+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ff3a6cda-4b95-4d3f-8dff-56f7df6c3a7e", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "AIDA Model", "slug": "aida-model", "status": "active", "bestFor": null, "canonId": "019dd956-86fc-773c-813f-d416714b8413", "version": 1, "bodyDocId": null, "description": "Attention, Interest, Desire, Action - classic persuasion sequence", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "a87bf074-ba8d-403d-96b0-8c9a433d28af", "evidence": "The slide presents a clear attention-grabbing title, and lists key metrics and features, attempting to generate interest and desire for AgileRL's dev-tools.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 22:33:07.948686+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 5, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Social Proof", "slug": "social-proof", "status": "active", "bestFor": null, "canonId": "019dd956-990d-7620-82b3-6dd286f34568", "version": 1, "bodyDocId": null, "description": "People adapt behavior based on what others do - show adoption and testimonials", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "78d80f25-0898-42ce-a2e0-ddba1118a144", "evidence": "The slide mentions 'Widely used by businesses in robotics, finance, logistics and other industries' to establish credibility.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 22:33:07.990576+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 5, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [], "loops": [ { "to": 6, "from": 5, "loop": { "name": "47_quick_win_big_bet", "slug": "47-quick-win-big-bet", "status": "active", "bestFor": "Transformation planning, 100-day plans, resource allocation", "canonId": "019dd956-790d-708d-983e-ef9bbc68f741", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Full List -> Quick Wins (Low effort, High impact) -> Big Bets (High effort, High impact) -> Sequenced Roadmap", "description": "Separate initiatives into immediate wins and longer-term strategic bets", "familyLabel": null }, "matchId": "6ade56bd-f936-445e-8273-afc965b9dfa0", "evidence": "The traction and solution sections suggest a plan for quick wins and big bets", "position": 2, "objective": "Present a clear plan for quick wins and big bets", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:41:16.471087+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 6, "type": "resolution", "title": "The slide features a product screenshot collage and a testimonial from a Machine Learning Engineer at Decision Lab.", "function": "present_solution", "rawType": "solution", "block": null, "metadata": { "slideType": "solution", "slideTypeCanon": { "name": "Solution", "slug": "solution", "status": "active", "canonId": "019de52d-0eb5-72ff-8b70-ba70a4199282", "version": 1, "description": null }, "function": "present_solution", "functionCanon": { "name": "Present solution", "slug": "present_solution", "status": "active", "canonId": "019de52d-15b8-71da-bd23-2b767850cbce", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 83, "componentCount": 9, "textChars": 1168, "nDataPoints": 0, "notes": "The slide features a product screenshot collage and a testimonial from a Machine Learning Engineer at Decision Lab.", "elementsJson": [ "headline_text", "action_title", "screenshot", "bullet_list", "paragraph", "quote_block" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:57.292+00", "seconds": 6.713, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/6", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-6", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Arena: Enterprise RLOps platform", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-c4de7c18465f", "parentComponentId": null }, { "bbox": { "x": 0.05, "y": 0.3 }, "kind": "image", "text": "Collage of the Arena platform interface", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Screenshot", "slug": "screenshot", "status": "active", "canonId": "019de52c-fd87-7509-8391-3d79fb339379", "version": 1, "description": null }, "framework": null }, "subkind": "screenshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:57.292+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fdc4a474-2003-4bae-a55e-2d988b856d18", "parentComponentId": null }, { "bbox": { "x": 0.52, "y": 0.36 }, "kind": "list", "text": "Built on our proven open-source framework to support production RL systems\nGuided business use-case setup with auto validation\nRL-optimized distributed training for larger workloads\nReal-time monitoring for performance insights\nOne-click model deployment with instant API access", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:57.292+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "808716bb-2e84-4243-816c-04931ae53ebd", "parentComponentId": null }, { "bbox": { "x": 0.52, "y": 0.63 }, "kind": "paragraph", "text": "Expanding to include LLM based use-cases, starting with pilots in insurance claim settlement and customer service.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:57.292+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "280bba08-0ee2-4382-8122-9bcca1f9f6be", "parentComponentId": null }, { "bbox": { "x": 0.34, "y": 0.89 }, "kind": "paragraph", "text": "Machine Learning Engineer, Decision Lab", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:57.292+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e74eac69-6e37-407c-a2ac-420d0b2e6fb1", "parentComponentId": null }, { "bbox": { "x": 0.05, "y": 0.77 }, "kind": "quote", "text": "Arena has significantly streamlined our RL development workflow making training and deploying agents a breeze. The platform's hyperparameter tuning capabilities have dramatically accelerated our experimentation and improved the performance of our models.", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": null, "subkind": { "name": "Testimonial", "slug": "testimonial", "status": "active", "canonId": "019de52c-fc21-71b6-9ac3-8266a64945e8", "version": 1, "description": null }, "framework": null }, "subkind": "testimonial", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:57.292+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "aa5465a5-2ba3-469b-a4e9-7f76168e0a7f", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "Arena has significantly streamlined our RL development workflow making training and deploying agents a breeze. The platform's hyperparameter tuning capabilities have dramatically accelerated our experimentation and improved the performance of our models. — Machine Learning Engineer, Decision Lab", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-cbc6afce1b89", "parentComponentId": null }, { "bbox": { "x": 0.52, "y": 0.3 }, "kind": "title", "text": "Arena: Enterprise RLOps platform", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:57.292+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "82dae81a-ef63-4676-8180-27ec0217bc3a", "parentComponentId": null }, { "bbox": { "x": 0.14, "y": 0.13 }, "kind": "title", "text": "Enterprise grade tooling to create production ready agents for any business use-case", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:57.292+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e1814dc0-e168-4828-a16f-12a69e90dd1b", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Problem-Agitate-Solve (PAS)", "slug": "problem-agitate-solve-pas", "status": "active", "bestFor": null, "canonId": "019dd956-869f-7564-8d42-de54ed29ab90", "version": 1, "bodyDocId": null, "description": "Identify problem, agitate the pain, present the solution", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "4f592c05-4c6a-4049-bde1-b32a2cd9177c", "evidence": "The slide presents a solution (Arena) to a problem (RL development workflow challenges), but does not explicitly agitate the problem.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 22:33:08.072797+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [], "loops": [ { "to": 6, "from": 5, "loop": { "name": "47_quick_win_big_bet", "slug": "47-quick-win-big-bet", "status": "active", "bestFor": "Transformation planning, 100-day plans, resource allocation", "canonId": "019dd956-790d-708d-983e-ef9bbc68f741", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Full List -> Quick Wins (Low effort, High impact) -> Big Bets (High effort, High impact) -> Sequenced Roadmap", "description": "Separate initiatives into immediate wins and longer-term strategic bets", "familyLabel": null }, "matchId": "6ade56bd-f936-445e-8273-afc965b9dfa0", "evidence": "The traction and solution sections suggest a plan for quick wins and big bets", "position": 2, "objective": "Present a clear plan for quick wins and big bets", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:41:16.471087+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 7, "type": "evidence", "title": "The slide uses a timeline-like structure to categorize partners by engagement type (publications, technology partners, trials, pilots).", "function": "show_traction", "rawType": "customer_logos", "block": null, "metadata": { "slideType": "customer_logos", "slideTypeCanon": { "name": "Customer logos", "slug": "customer_logos", "status": "active", "canonId": "019de52d-101d-705c-8956-65f1b315e58a", "version": 1, "description": null }, "function": "show_traction", "functionCanon": { "name": "Show traction", "slug": "show_traction", "status": "active", "canonId": "019de52d-16a8-756a-9b38-a7d35420561d", "version": 1, "description": null }, "density": "balanced", "densityScore": 41, "componentCount": 5, "textChars": 288, "nDataPoints": 0, "notes": "The slide uses a timeline-like structure to categorize partners by engagement type (publications, technology partners, trials, pilots).", "elementsJson": [ "headline_text", "action_title", "logo_grid", "callout_box", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:55.728+00", "seconds": 4.995, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/7", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-7", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "AgileRL is trusted by industry leaders for mission-critical reinforcement learning applications", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e1-2e476df048a1", "parentComponentId": null }, { "bbox": { "h": 0.3, "w": 0.72, "x": 0.14, "y": 0.645 }, "kind": "image", "text": "Three case study cards with images and text", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Illustration", "slug": "illustration", "status": "active", "canonId": "019de52c-fe76-7772-a5ac-c0b7294d7615", "version": 1, "description": null }, "framework": null }, "subkind": "illustration", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:55.728+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b6fd5c2e-9841-4e36-9b4d-c2628cfb3ee1", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.9, "x": 0.05, "y": 0.3 }, "kind": "image", "text": "Logos of various universities and companies", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Logo grid", "slug": "logo-grid", "status": "active", "canonId": "019de52c-fdd7-7409-93e5-81280a8ca215", "version": 1, "description": null }, "framework": null }, "subkind": "logo-grid", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:55.728+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2aa17758-f7a1-43dc-95bf-58ef04fe5964", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.72, "x": 0.14, "y": 0.1 }, "kind": "title", "text": "AgileRL is trusted by industry leaders for mission-critical reinforcement learning applications", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:55.728+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0e5f6d48-270f-4974-830e-15ca19300c63", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.1, "x": 0.45, "y": 0.595 }, "kind": "title", "text": "Case studies", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:55.728+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ec6f93d3-a27d-4270-a333-2bf22afbaf97", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Social Proof", "slug": "social-proof", "status": "active", "bestFor": null, "canonId": "019dd956-990d-7620-82b3-6dd286f34568", "version": 1, "bodyDocId": null, "description": "People adapt behavior based on what others do - show adoption and testimonials", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "73ac3741-7687-489f-9adf-b451d86bf46a", "evidence": "The slide showcases logos of various universities and companies that trust AgileRL, establishing credibility.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.8, "extraction": { "at": "2026-07-16 22:33:08.1557+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 8, "type": "resolution", "title": "The slide uses a stepped layout to represent the market hierarchy.", "function": "size_opportunity", "rawType": "market_sizing", "block": null, "metadata": { "slideType": "market_sizing", "slideTypeCanon": { "name": "Market sizing", "slug": "market_sizing", "status": "active", "canonId": "019de52d-08c8-756c-8664-733b73a7ee7b", "version": 1, "description": null }, "function": "size_opportunity", "functionCanon": { "name": "Size opportunity", "slug": "size_opportunity", "status": "active", "canonId": "019de52d-147b-7568-8828-a74a27697618", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 84, "componentCount": 8, "textChars": 739, "nDataPoints": 5, "notes": "The slide uses a stepped layout to represent the market hierarchy.", "elementsJson": [ "headline_text", "action_title", "callout_box", "quote_block" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:53.606+00", "seconds": 2.776, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/8", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-8", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Reinforcement learning is the crucial ingredient for agentic AI's multi-billion dollar market", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-454d4afb0f31", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.3, "x": 0.345, "y": 0.705 }, "kind": "list", "text": "SOM: $2M average spend across businesses with RL systems in production ($10.4 BN - 28.9% CAGR)", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.606+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4e23deb0-91ad-4c8e-8e45-5b4280194936", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.3, "x": 0.49, "y": 0.49 }, "kind": "list", "text": "SAM: Convergence of agent and RL markets by 2028 ($45 BN)", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.606+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8dfc6f5d-3962-4d78-95f6-1dd27cf398aa", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.3, "x": 0.635, "y": 0.282 }, "kind": "list", "text": "TAM: Annual businesses' spending on specialised agents by 2033 ($139 BN - 43.88% CAGR)", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.606+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fa3c76cf-7925-4191-b743-a5fffab1e7a0", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "TAM: $139 BN", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-4ceecb3aeae9", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.38, "x": 0.08, "y": 0.35 }, "kind": "quote", "text": "\"It took us 3 years and we spent over $2 million to optimise global shipping using reinforcement learning.\" Research Engineer, Tata Consultancy Services", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": null, "subkind": { "name": "Testimonial", "slug": "testimonial", "status": "active", "canonId": "019de52c-fc21-71b6-9ac3-8266a64945e8", "version": 1, "description": null }, "framework": null }, "subkind": "testimonial", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.606+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "cbcb259b-2da9-4ac5-9524-793b881f740a", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "It took us 3 years and we spent over $2 million to optimise global shipping using reinforcement learning. — Research Engineer, Tata Consultancy Services", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-48baebe762c5", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.72, "x": 0.14, "y": 0.1 }, "kind": "title", "text": "Reinforcement learning is the crucial ingredient for agentic AI's multi-billion dollar market", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:53.606+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1272607e-df46-41e3-b87f-e0525831393e", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [ { "matchId": "6d0d5da4-9448-40bd-aa2a-de52e7a6e5af", "evidence": "The slide explicitly labels market segments as TAM, SAM, and SOM.", "framework": null, "confidence": 1, "extraction": { "at": "2026-05-02 16:17:53.606+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 8, "frameworkName": "TAM-SAM-SOM" } ], "arcBeats": [ { "to": 8, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null }, "from": 8, "name": "Market Size", "beatId": "2dc8d091-a265-420c-a817-5e5c0cbac86a", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Market Size", "slug": "sequoia-pitch-market-size", "status": "active", "canonId": "019dd9b8-20e6-729c-bc28-05442906d032", "version": 1, "description": null }, "evidence": "Market sizing is presented on page 8", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 0.8 } ], "loops": [], "locked": true }, { "page": 9, "type": "setup", "title": "The slide uses a checkmark-based comparison matrix to highlight the unique value proposition of AgileRL's autonomous, RL-based approach.", "function": "compare_peers", "rawType": "competitive_analysis", "block": null, "metadata": { "slideType": "competitive_analysis", "slideTypeCanon": { "name": "Competitive analysis", "slug": "competitive_analysis", "status": "active", "canonId": "019de52d-093e-776b-94c6-c1573f5f1a31", "version": 1, "description": null }, "function": "compare_peers", "functionCanon": { "name": "Compare peers", "slug": "compare_peers", "status": "active", "canonId": "019de52d-1453-72ba-a367-9ba3d34c1630", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 82, "componentCount": 9, "textChars": 1049, "nDataPoints": 0, "notes": "The slide uses a checkmark-based comparison matrix to highlight the unique value proposition of AgileRL's autonomous, RL-based approach.", "elementsJson": [ "action_title", "comparison_table", "callout_box" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:54.184+00", "seconds": 3.348, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/9", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-9", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": { "h": 0.13, "w": 0.72, "x": 0.14, "y": 0.09 }, "kind": "callout", "text": "Our proven team is ready to compete and win in the booming AI agents space, with the right investment", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1ee4edcb-02a8-4a30-b044-97695d813237", "parentComponentId": null }, { "bbox": null, "kind": "callout", "text": "AgileRL agents learn to do tasks by discovering strategies themselves, and then becoming superhuman at them. We provide the infrastructure needed to cover the entire RL development pipeline.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:00+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-1c6352fb8113", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.16, "x": 0.48, "y": 0.76 }, "kind": "paragraph", "text": "Open-source solutions on their own do not provide the engineering infrastructure required to train and deploy agents for production.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "20484724-f950-4b33-ada4-3c6b1d5a800d", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.16, "x": 0.66, "y": 0.76 }, "kind": "paragraph", "text": "Base LLMs are useful for one-off tasks, but do not have agency to do work for businesses.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2202ac81-89e5-4c98-82b9-67b2f8c7fc86", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.16, "x": 0.3, "y": 0.76 }, "kind": "paragraph", "text": "Solutions that rely on meticulously defined tasks, and are inflexible, cannot achieve expert performance at new challenges.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4eeb4984-03d7-4aa7-a2eb-2b4b40bed738", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.14, "x": 0.84, "y": 0.76 }, "kind": "paragraph", "text": "Most 'agentic' solutions are just platforms for integrations i.e. Zapier with buzzwords.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a1be268e-6fb5-4454-8d8c-741e0755fcc7", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.22, "x": 0.06, "y": 0.76 }, "kind": "paragraph", "text": "AgileRL agents learn to do tasks by discovering strategies themselves, and then becoming superhuman at them. We provide the infrastructure needed to cover the entire RL development pipeline.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e23f714e-135e-44ce-9e7a-4199e61eca40", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.92, "x": 0.04, "y": 0.28 }, "kind": "table", "text": "Comparison matrix showing features: specialized tasks, no human instructions, autonomous improvement, RL engineering hurdles.", "attrs": null, "canon": { "kind": { "name": "Table", "slug": "table", "status": "active", "canonId": "019de52c-f9c8-7148-8119-eaac9594cd28", "version": 1, "description": "Tabular data layout." }, "tool": null, "subkind": null, "framework": null }, "subkind": "comparison", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "84631cd2-cae4-48e8-9a37-0843d196e0b5", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.08, "x": 0.02, "y": 0.03 }, "kind": "title", "text": "Competition", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2d3c5933-fd7a-4a9d-966e-20e53e4ac69d", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [ { "matchId": "669ba307-2891-4b66-bf41-2f1cf652be60", "evidence": "Feature comparison matrix used to differentiate product capabilities.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 16:17:54.184+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 9, "frameworkName": "comparison-frame" } ], "arcBeats": [ { "to": 9, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null }, "from": 9, "name": "Competitive Analysis", "beatId": "ede65a75-39eb-4679-bb03-a02e96f46656", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Competition", "slug": "sequoia-pitch-competition", "status": "active", "canonId": "019dd9b8-21bd-7407-8707-bdf217ed9c76", "version": 1, "description": null }, "evidence": "Competitive analysis is presented on page 9", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 0.8 } ], "loops": [], "locked": true }, { "page": 10, "type": "resolution", "title": "Includes a supporting quote from IDC's 2024 AI opportunity study.", "function": "plan_implementation", "rawType": "go_to_market", "block": null, "metadata": { "slideType": "go_to_market", "slideTypeCanon": { "name": "Go-to-market", "slug": "go_to_market", "status": "active", "canonId": "019de52d-0ff6-713a-bd55-53d4e90b2304", "version": 1, "description": null }, "function": "plan_implementation", "functionCanon": { "name": "Plan implementation", "slug": "plan_implementation", "status": "active", "canonId": "019de52d-14a2-77ea-b210-d6a95eed4823", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 89, "componentCount": 10, "textChars": 1061, "nDataPoints": 1, "notes": "Includes a supporting quote from IDC's 2024 AI opportunity study.", "elementsJson": [ "action_title", "callout_box", "icon_grid", "big_number", "quote_block" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:54.694+00", "seconds": 3.715, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/10", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-10", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "AgileRL grows through bottom-up adoption and strategic top-down sales", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-970e07ae7da4", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.3, "x": 0.67, "y": 0.28 }, "kind": "list", "text": "Channel partnerships: Ongoing engagements with strategic partners with complementary solutions (cloud, simulation, consulting, MLOps). Joint solutions for industry-specific use cases.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2b3a5fe8-cc57-415d-a7b8-80e38560ec5a", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.3, "x": 0.03, "y": 0.28 }, "kind": "list", "text": "Community-led growth: Continue open-source community initiative to create more advocates and evangelists. Further establish AgileRL as the industry standard and go-to resource for RL expertise.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4a14f4ed-f49e-4a78-b625-169fcd134c50", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.3, "x": 0.35, "y": 0.28 }, "kind": "list", "text": "Enterprise sales: Early adoption shows positive response, double-down on targeting enterprise use-cases. Dedicated customer success team for high-impact engagement with users.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8738a77e-3865-40b9-8284-7ff0fc7ff9b3", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.1, "x": 0.15, "y": 0.8 }, "kind": "metric", "text": "24 Months", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": null, "subkind": { "name": "Big number", "slug": "big-number", "status": "active", "canonId": "019de52c-fc70-70c8-b965-0584548a9204", "version": 1, "description": null }, "framework": null }, "subkind": "big-number", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "42d6a7b3-fba4-421a-9cf1-bfaa46490bdd", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.6, "x": 0.3, "y": 0.8 }, "kind": "quote", "text": "Within this time, most organisations plan to expand beyond pre-built AI solutions to advanced AI workloads that are customised or custom-built.", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": null, "subkind": { "name": "Pull quote", "slug": "pull-quote", "status": "active", "canonId": "019de52c-fbf9-76fe-97e1-a0b7540445c9", "version": 1, "description": null }, "framework": null }, "subkind": "pull-quote", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "32464448-8a26-4817-846c-1c3b0167e6e3", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "Within this time, most organisations plan to expand beyond pre-built AI solutions to advanced AI workloads that are customised or custom-built. — IDC’s 2024 AI opportunity study", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-27 15:13:01+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-bb81-722c-82e0-9877affe0c5c", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.45, "y": 0.9 }, "kind": "source-note", "text": "IDC's 2024 AI opportunity study", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. Numbered footnotes use attrs.numbered." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "97c570a2-c4e1-4d6e-ac7a-0b5e49f00b57", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.7, "x": 0.15, "y": 0.1 }, "kind": "title", "text": "AgileRL grows through bottom-up adoption and strategic top-down sales", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e19ae317-3aa6-40cb-be4b-3d1e4389c32d", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.1, "x": 0.03, "y": 0.03 }, "kind": "title", "text": "Go-to-market", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "df2aacca-aa0b-44ef-ada0-41d626faa497", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [ { "matchId": "d408a4e5-6498-48c8-a632-931505070766", "evidence": "Three-pillar approach to market entry and growth", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 16:17:54.694+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "frameworkName": "go-to-market-strategy" } ], "arcBeats": [], "loops": [], "locked": true }, { "page": 11, "type": "setup", "title": "Includes logos of academic institutions, previous employers, investors, and advisors.", "function": "other", "rawType": "team_bio", "block": null, "metadata": { "slideType": "team_bio", "slideTypeCanon": { "name": "Team bio", "slug": "team_bio", "status": "active", "canonId": "019de52d-0a7d-7394-ae5b-037478e71f1e", "version": 1, "description": null }, "function": "other", "functionCanon": { "name": "Other (fallback)", "slug": "other", "status": "active", "canonId": "019de52d-15e1-75bc-99dc-07732380e2ce", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 91, "componentCount": 12, "textChars": 868, "nDataPoints": 2, "notes": "Includes logos of academic institutions, previous employers, investors, and advisors.", "elementsJson": [ "headline_text", "action_title", "headshot", "bullet_list", "logo_grid" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:18:00.162+00", "seconds": 9.15, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/11", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-11", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": { "h": 0.13, "w": 0.72, "x": 0.14, "y": 0.09 }, "kind": "callout", "text": "Our expertise is backed by RL research and deployment success across multiple industries", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "88b39d97-9674-468a-931c-0183739bb320", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.25, "x": 0.45, "y": 0.3 }, "kind": "image", "text": "Nick Ustaran-Anderegg", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Headshot", "slug": "headshot", "status": "active", "canonId": "019de52c-fd5f-765c-9282-38b54f61bbbf", "version": 1, "description": null }, "framework": null }, "subkind": "headshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c1a80daf-cd17-4f3d-a404-682f517cfaaf", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.25, "x": 0.11, "y": 0.3 }, "kind": "image", "text": "Param Kumar", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Headshot", "slug": "headshot", "status": "active", "canonId": "019de52c-fd5f-765c-9282-38b54f61bbbf", "version": 1, "description": null }, "framework": null }, "subkind": "headshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ea48d00c-8e28-4e7d-b0d0-4215417d5cf1", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.25, "x": 0.73, "y": 0.7 }, "kind": "image", "text": "Investor logos", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Logo grid", "slug": "logo-grid", "status": "active", "canonId": "019de52c-fdd7-7409-93e5-81280a8ca215", "version": 1, "description": null }, "framework": null }, "subkind": "logo-grid", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2e69fd8a-3bab-4424-a446-7d7c735005e5", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.25, "x": 0.73, "y": 0.87 }, "kind": "image", "text": "Advisor logos", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Logo grid", "slug": "logo-grid", "status": "active", "canonId": "019de52c-fdd7-7409-93e5-81280a8ca215", "version": 1, "description": null }, "framework": null }, "subkind": "logo-grid", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3ba926c3-e1a9-42e7-9f99-c91e7f09c5e9", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.25, "x": 0.73, "y": 0.45 }, "kind": "image", "text": "Institutional affiliations", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Logo grid", "slug": "logo-grid", "status": "active", "canonId": "019de52c-fdd7-7409-93e5-81280a8ca215", "version": 1, "description": null }, "framework": null }, "subkind": "logo-grid", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8bf0c599-886a-4f32-a236-1fe3f080964d", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.25, "x": 0.73, "y": 0.82 }, "kind": "list", "text": "Advisors from:", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "391cebbe-276b-46e3-a5b6-0974ec0a0543", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.25, "x": 0.73, "y": 0.28 }, "kind": "list", "text": "Team: RL specialists from the world's leading institutions. 2x RL Engineers. 2x Full-stack Engineers. 1x Front-end Engineer.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "57988f3f-3114-491f-b793-1d887c5ce309", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.25, "x": 0.73, "y": 0.65 }, "kind": "list", "text": "Pre-seed investors:", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "922435b9-0395-4ae1-97d9-6e094a738081", "parentComponentId": null }, { "bbox": { "h": 0.35, "w": 0.35, "x": 0.06, "y": 0.55 }, "kind": "paragraph", "text": "Param Kumar CEO. AI SaaS product leader. Ex-PolyAI, Ex-Just Eat. Managed teams of engineers building £100 million ARR SaaS platform including RL recommendation engine at Just Eat. Scaled AI platform for PolyAI across global hotel chains.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4cdd6ace-e417-4e40-af1c-00c388831336", "parentComponentId": null }, { "bbox": { "h": 0.35, "w": 0.35, "x": 0.37, "y": 0.55 }, "kind": "paragraph", "text": "Nick Ustaran-Anderegg CTO. Machine learning engineer. MEng, Imperial College London. Led ML team creating facial recognition models used to prevent voter fraud 625,000+ times. Developed reinforcement learning models for conversational agents (pre-ChatGPT) and delivery route planning optimisation.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e81d5ee6-2b2a-486e-912d-9e54384c8443", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.05, "x": 0.02, "y": 0.03 }, "kind": "title", "text": "Team", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:18:00.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9dfeb52b-9be3-40ef-a41a-d1935a2e1596", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 11, "arc": { "name": "The Sequoia Pitch", "slug": "sequoia-pitch", "status": "active", "bestFor": "Investor pitches, Series A/B, board presentations", "canonId": "019dd956-5e69-75a7-a0a4-895ac1bc0444", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem -> Solution -> Why Now -> Market Size -> Competition -> Product -> Business Model -> Team -> Financials -> The Ask", "description": null, "familyLabel": null }, "from": 11, "name": "Team", "beatId": "6ce1e4e8-cab9-48dc-9284-815b3e5eb912", "matchId": "2fe270e3-fdd5-49db-8c52-098553180938", "beatType": { "name": "Team", "slug": "sequoia-pitch-team", "status": "active", "canonId": "019dd9b8-23c9-746f-aaf9-2f5d83892cf3", "version": 1, "description": null }, "evidence": "The team is presented on page 11", "position": 4, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 0.8 } ], "loops": [], "locked": true }, { "page": 12, "type": "setup", "function": "inspire_vision", "rawType": "vision", "block": null, "metadata": { "slideType": "vision", "slideTypeCanon": { "name": "Vision", "slug": "vision", "status": "active", "canonId": "019de52d-0f7e-7689-8010-992b1a43b6b9", "version": 1, "description": null }, "function": "inspire_vision", "functionCanon": { "name": "Inspire vision", "slug": "inspire_vision", "status": "active", "canonId": "019de52d-16d0-7756-83de-376eeac6fa40", "version": 1, "description": null }, "density": "sparse", "densityScore": 15, "componentCount": 3, "textChars": 132, "nDataPoints": 0, "notes": null, "elementsJson": [ "headline_text" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:17:54.282+00", "seconds": 3.146, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5fec-763b-95d0-451a96d1edee/12", "deck": "/decks/019dd923-5fec-763b-95d0-451a96d1edee", "deckAnchor": "/decks/019dd923-5fec-763b-95d0-451a96d1edee#slide-12", "deckJson": "/decks/019dd923-5fec-763b-95d0-451a96d1edee.json" }, "components": [ { "bbox": { "h": 0.07, "w": 0.16, "x": 0.42, "y": 0.68 }, "kind": "paragraph", "text": "Param Kumar CEO\nparam@agilerl.com", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.282+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4c4456f5-b65d-4557-99ba-e4da3cc7f0c9", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.16, "x": 0.42, "y": 0.85 }, "kind": "paragraph", "text": "https://agilerl.com", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.282+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "78a26e88-2755-429a-867b-03e85414c938", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.62, "x": 0.19, "y": 0.35 }, "kind": "title", "text": "Pioneering a world where every business has specialised agents working for them.", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:17:54.282+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9a1e71b8-bf4b-4680-9c82-90b0b8e19071", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true } ], "arcBeats": [ { "from": 3, "to": 3, "label": "Problem", "description": "The problem statement is presented on page 3" }, { "from": 4, "to": 4, "label": "Solution", "description": "The solution is presented on page 4" }, { "from": 8, "to": 8, "label": "Market Size", "description": "Market sizing is presented on page 8" }, { "from": 9, "to": 9, "label": "Competitive Analysis", "description": "Competitive analysis is presented on page 9" }, { "from": 11, "to": 11, "label": "Team", "description": "The team is presented on page 11" } ], "loops": [ { "from": 2, "to": 2, "label": "Why Now", "description": "Explain why now is the right time for investment" }, { "from": 3, "to": 4, "label": "Cost Of Inaction", "description": "Highlight the cost of not investing in AI model training" }, { "from": 5, "to": 6, "label": "Quick Win Big Bet", "description": "Present a clear plan for quick wins and big bets" } ] }