{ "schemaVersion": "deck-intelligence.v1", "id": "019dd923-5f69-752d-a9b7-9b572aef1b8d", "slug": "QByJanEPVGZSjQFgAyhk", "title": "We got an exclusive look at the pitch deck AI startup Kili Technology used to raise $25 million to clean up messy data", "subtitle": "Kili Technology", "author": "Kili Technology", "pageCount": 7, "kind": "pitchdeck", "orientation": "landscape", "aspectRatio": 1.33, "document": { "id": "019dd923-5f69-752d-a9b7-9b572aef1b8d", "slug": "QByJanEPVGZSjQFgAyhk", "title": "We got an exclusive look at the pitch deck AI startup Kili Technology used to raise $25 million to clean up messy data", "rawTitle": "We got an exclusive look at the pitch deck AI startup Kili Technology used to raise $25 million to clean up messy data", "authorId": "Pitchdecks", "authorName": "Kili Technology", "authorDisplay": "Kili Technology", "kind": { "slug": "pitchdeck", "label": "Pitch deck" }, "sourceType": { "slug": "strategy_consulting", "label": "Strategy consulting" }, "fileName": "Kili-QByJanEP.pdf", "sourceUrl": "https://www.businessinsider.com/kili-pitch-deck-ai-french-artificial-intelligence-technology-25-million-2021-7", "sourceDate": "2021-07-27 00:00:00", "presentationDate": null, "ingestedAt": "2026-04-27 08:57:08.081+00", "status": "ready", "pageCount": 7, "orientation": "landscape", "aspectRatio": 1.33, "targetCompany": null, "targetTicker": null, "metadata": { "local_root": "/tmp/legacy-images/QByJanEPVGZSjQFgAyhk", "origin_corpus": "consulting" } }, "coverage": { "pageCount": 7, "pagesIndexed": 7, "pagesWithImages": 7, "pagesWithMetadata": 7, "imageCoveragePct": 100, "metadataCoveragePct": 100, "componentCount": 36, "chartCount": 0, "metricSlides": 0, "toolMatches": 4, "documentToolMatches": 0, "frameworkMatches": 4, "arcMatches": 1, "beatMatches": 3, "loopMatches": 1, "patternMatches": 0, "storymakersMatch": false, "scoreAvailable": false, "reviewAvailable": false, "activistThesisAvailable": false, "pitchdeckMetadataAvailable": true, "pitchdeckProfileAvailable": true }, "blocks": [], "matches": { "arcs": [ { "arc": { "name": "Problem-Agitate-Solution", "slug": "problem-agitate-solution", "status": "active", "bestFor": "Sales pitches, urgency-driven, competitive displacement", "canonId": "019dd956-5ec6-70f9-9fd8-fdbaaff39acf", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem (Identify pain) -> Agitate (Make it worse) -> Solution (Provide relief)", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "matchId": "678d59ad-5d71-4d6e-9247-acca04319923", "evidence": "Problem statement, why now, solution", "isPrimary": true, "confidence": 0.8, "extraction": { "at": "2026-07-16 21:34:40.783021+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "beats": [ { "to": 2, "arc": { "name": "Problem-Agitate-Solution", "slug": "problem-agitate-solution", "status": "active", "bestFor": "Sales pitches, urgency-driven, competitive displacement", "canonId": "019dd956-5ec6-70f9-9fd8-fdbaaff39acf", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem (Identify pain) -> Agitate (Make it worse) -> Solution (Provide relief)", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 2, "name": "Problem", "beatId": "43067e69-9ce7-46c0-b53b-5465c6e3c98e", "matchId": "678d59ad-5d71-4d6e-9247-acca04319923", "beatType": { "name": "Problem (Identify pain)", "slug": "problem-agitate-solution-problem-identify-pain", "status": "active", "canonId": "019dd9b8-0e78-74bb-b8f6-54348367a131", "version": 1, "description": null }, "evidence": "THE 80% BLINDSPOT: ENTERPRISES MASSIVELY INVEST IN AI, BUT STRUGGLE LEVERAGE IT...", "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": 3, "arc": { "name": "Problem-Agitate-Solution", "slug": "problem-agitate-solution", "status": "active", "bestFor": "Sales pitches, urgency-driven, competitive displacement", "canonId": "019dd956-5ec6-70f9-9fd8-fdbaaff39acf", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem (Identify pain) -> Agitate (Make it worse) -> Solution (Provide relief)", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 3, "name": "Agitate", "beatId": "25d4a852-bc8e-473e-aa01-7f85fe36afb1", "matchId": "678d59ad-5d71-4d6e-9247-acca04319923", "beatType": { "name": "Agitate (Make it worse)", "slug": "problem-agitate-solution-agitate-make-it-worse", "status": "active", "canonId": "019dd9b8-0f5a-71dd-bae7-8fadb0d09456", "version": 1, "description": null }, "evidence": "Tech leaders started tackling annotation at scale a decade ago, having experienced poor performance when not iterating on their data", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Development", "slug": "development", "status": "active", "canonId": "019dd9b8-0332-7008-b3aa-11b0840a57b1", "version": 1, "description": "The middle stretch in arcs that have one. Often where length problems creep in." }, "alignedBlockIds": null, "matchConfidence": 0.8 }, { "to": 6, "arc": { "name": "Problem-Agitate-Solution", "slug": "problem-agitate-solution", "status": "active", "bestFor": "Sales pitches, urgency-driven, competitive displacement", "canonId": "019dd956-5ec6-70f9-9fd8-fdbaaff39acf", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem (Identify pain) -> Agitate (Make it worse) -> Solution (Provide relief)", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 6, "name": "Solution", "beatId": "7e1d6f81-8129-4ccc-89b3-47bbcfd3d576", "matchId": "678d59ad-5d71-4d6e-9247-acca04319923", "beatType": { "name": "Solution (Provide relief)", "slug": "problem-agitate-solution-solution-provide-relief", "status": "active", "canonId": "019dd9b8-0fee-73e9-9ae5-799c7f3895cd", "version": 1, "description": null }, "evidence": "ONE PLATFORM TO ORCHESTRATE AI IN PRODUCTION", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Resolution", "slug": "resolution", "status": "active", "canonId": "019dd9b8-045d-703b-8dd7-e92aba3ba91b", "version": 1, "description": "How the deck ends. Required in 19/20 arcs. The exception (Sparkline) ends with new bliss which is functionally a resolution." }, "alignedBlockIds": null, "matchConfidence": 0.8 } ], "loops": [ { "to": 6, "from": 2, "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": "f8fcc917-6c1d-4ce3-af79-db57902e5dc6", "evidence": "Poor data quality hinders AI performance, Kili Technology's platform solves this problem", "position": 0, "objective": "Highlight the cost of not solving the data quality problem", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:34:40.918819+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": "7d9734a1-d0d2-4be4-ac6c-ea4ce829d719", "evidence": "The slide is labeled as 'cover' and features a title, subtitle, and company logo.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-17 03:24:14.036047+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": "Annotation", "slug": "annotation", "status": "active", "bestFor": null, "canonId": "019dd956-b5b9-7668-bf0e-3af99823ec5c", "version": 1, "bodyDocId": null, "description": "Adding explanatory labels to highlight key data points", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "f082d417-0bc7-4d22-8b5d-3db6b0d1bde1", "evidence": "Tech leaders started tackling annotation at scale a decade ago", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:24:14.194656+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": "Data-Ink Ratio", "slug": "data-ink-ratio", "status": "active", "bestFor": null, "canonId": "019dd956-afc8-70da-b378-92834d36fde6", "version": 1, "bodyDocId": null, "description": "Maximize data, minimize non-data ink - remove chartjunk and decoration", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "aa980371-fcdc-40ff-84db-bc63317686d7", "evidence": "paragraph/paragraph: model performance: 10%", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.5, "extraction": { "at": "2026-07-17 03:24:14.276053+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "2x2 matrix", "slug": "matrix-2x2", "status": "active", "bestFor": null, "canonId": "019dd9e1-50ab-72d1-8afb-3ab0a38e32a8", "version": 1, "bodyDocId": null, "description": "BCG matrix, importance/urgency, any axes-and-quadrants frame.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "5098dae6-7f7d-48e4-9679-f717539f9483", "evidence": "list/bullet: Label, Collaborate, Monitor quality, Automate labeling, Data pipelines", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.5, "extraction": { "at": "2026-07-17 03:24:14.385758+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [ { "matchId": "e293474a-3fa8-4e60-8cd2-a5d525949003", "evidence": "Contrasts the '80% blindspot' problem with the 'DNA' root cause.", "framework": null, "confidence": 0.8, "extraction": { "at": "2026-05-02 16:33:22.39+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 2, "frameworkName": "before-after-framing" }, { "matchId": "67803418-00fb-4b3c-8255-692a56e6da76", "evidence": "Citing Google, Facebook, Amazon, etc. to validate the necessity of annotation platforms.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 16:33:20.037+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "frameworkName": "authority-citation" }, { "matchId": "c9f7e735-aa70-48a6-a923-88f2c83a6248", "evidence": "Data is a diamond", "framework": null, "confidence": 0.009, "extraction": { "at": "2026-05-02 16:33:21.417+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "frameworkName": "metaphor-analogy" }, { "matchId": "b9fe2ada-a133-412a-a49a-9ed31ba302a0", "evidence": "Explicitly labeled in the diagram flow", "framework": null, "confidence": 1, "extraction": { "at": "2026-05-02 16:33:20.911+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "frameworkName": "human-in-the-loop" } ], "patterns": [] }, "storymakers": null, "score": null, "review": null, "activistThesis": null, "pitchdeck": { "metadata": { "author": "Jeff Elder", "fireId": "QByJanEPVGZSjQFgAyhk", "sector": "ai", "deckKind": "fundraise", "roundRaw": "Series A", "amountRaw": "$25 million", "amountUsd": "25000000", "scrapedAt": "2026-04-27 08:57:08.081+00", "sectorRaw": "AI startup", "sourceUrl": "https://www.businessinsider.com/kili-pitch-deck-ai-french-artificial-intelligence-technology-25-million-2021-7", "deckKindAt": "2026-04-28 07:19:06+00", "highlights": "Kili Technology;; a French enterprise AI startup;; has closed a $25 million Series A funding round. Rather than tuning up algorithms;; Kili helps companies prepare messy data for use in AI. See the pitch deck that lays out Kili's approach to \"focusing on the data first.\" See more stories on Insider's business page.", "slideCount": 7, "companyName": "Kili", "description": "Kili Technology;; a French enterprise AI startup;; has closed a $25 million Series A funding round. Rather than tuning up algorithms;; Kili helps companies prepare messy data for use in AI. See the pitch deck that lays out Kili's approach to \"focusing on the data first.\" See more stories on Insider's business page. Just six months after closing a seed round;; the French artificial intelligence startup Kili Technology is announcing a $25 million Series A on Tuesday as it pursues a new approach to dramatically speed up enterprise AI. Kili (which derives its name from Mount Kilimanjaro) focuses on preparing data to be analyzed;; rather than tuning up the AI algorithms which ultimately process it. The company's software takes \"raw\" data — that is;; data pulled straight from disparate sources;; including contracts;; images;; recordings;; and more — and helps users annotate and \"clean\" it to use in AI systems. Pouring messy data into AI uses cases slows processes way down so even though cleaning the data adds a step;; Kili's product ultimately shaves down time it takes for customers to go from idea to insights. François-Xavier Leduc;; CEO of Kili Technology;; says the data is like the gas you put into the engine of the algorithm that processes it. For a decade companies have been tuning up engines;; and then dumping in gas without thinking much about it. But that means putting in funky gas and getting a clunky performance. \"Developing AI by focusing on the data first is a totally new paradigm;;\" Leduc told Insider. \"It helps to select the right data in the first place and filters out bias that can be the worst result of an AI process.\" Biased data might incorrectly skew results. For example;; if the data used to train an AI system has signals that leads it to assume that data from the US is more important than data from Europe;; that will affect the kind of insights it spits out. Tuesday's Series A round pulls together some of the biggest names in Europe's AI startups sector. The lead investor;; Balderton Capital;; previously backed London AI powerhouse DarkTrace's recent IPO;; and continuing investor Serena Capital backed $1.4 billion Dataiku;; which has offices in NYC and Amsterdam. Dimitri Sirota;; founder of BigID;; and Chris Schagen;; founder of Contentful;; are advisors. \"Enterprise AI really started in 2010 with a huge amount of data;; and companies like DataRobot and Dataiku were very successful. They were super good for structured data. But today unstructured data is 80% of enterprise data;;\" lead investor Maxime Le Dantec;; principal at Balderton Capital;; told Insider. Le Dantec said his firm was impressed by how much the bootstrapped Kili was able to accomplished with a small staff. The company now has about a dozen employees. Prior to Tuesday's round;; the company had raised a total of $6.85 million in funding at a valuation of $25 million;; in PitchBook's December estimate. The company declined to disclose new valuation or revenue. Kili says the new funds will be used to accelerate growth through recruitment and tech investment and to move quickly into US markets. Here is the pitchdeck Kili Technology used to raise its new $25 million Series A round.", "displayName": "Kili Technology", "publishDate": "2021-07-27", "roundSeries": "series_a", "articleTitle": "We got an exclusive look at the pitch deck AI startup Kili Technology used to raise $25 million to clean up messy data", "leadInvestors": "Balderton Capital", "deckKindSignal": "raise", "metaExtractedAt": "2026-04-28 07:16:02+00" }, "profile": { "scores": { "yc": 0.4332, "a16z": 0.3532, "docsend": 0.3381, "hoffman": 0.3714, "mcclure": 0.3036, "sequoia": 0.4952, "kawasaki": 0.2536, "nextview": 0.2786, "slidebean": 0.3381, "founder_institute": 0.3143 }, "nSlides": 7, "sequence": [ "cover", "problem_statement", "why_now", "problem_statement", "solution", "solution", "closing_ask" ], "breakdown": { "yc": { "order": 0.5714, "signal": 0.08, "jaccard": 0.3636 }, "a16z": { "order": 0.5714, "signal": 0, "jaccard": 0.3636 }, "docsend": { "order": 0.5714, "signal": 0, "jaccard": 0.3333 }, "hoffman": { "order": 0.5714, "signal": 0, "jaccard": 0.4 }, "mcclure": { "order": 0.4286, "signal": 0.05, "jaccard": 0.25 }, "sequoia": { "order": 0.4286, "signal": 0.2, "jaccard": 0.3333 }, "kawasaki": { "order": 0.4286, "signal": 0, "jaccard": 0.25 }, "nextview": { "order": 0.4286, "signal": 0, "jaccard": 0.3 }, "slidebean": { "order": 0.5714, "signal": 0, "jaccard": 0.3333 }, "founder_institute": { "order": 0.5714, "signal": 0, "jaccard": 0.2857 } }, "matchedAt": "2026-04-27 21:51:54+00", "noveltyScore": 0.5548, "primaryScore": 0.4952, "noveltySignals": [ "rare slide types: why_now" ], "primaryProfile": "sequoia", "secondaryScore": 0.4332, "secondaryProfile": "yc" } }, "slides": [ { "page": 1, "type": "setup", "function": "front_matter", "imagePath": "https://imgproxy.kitesheet.com/A34dwSi_IN7JGVtBZ8lQZMlzEy_IV0gYZ5G4DGWyNsg/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy9RQnlKYW5FUFZHWlNqUUZnQXloay9wMDAxLmpwZw", "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": 14, "componentCount": 3, "textChars": 105, "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 16:33:18.457+00", "seconds": 5.829, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5f69-752d-a9b7-9b572aef1b8d/1", "deck": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d", "deckAnchor": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d#slide-1", "deckJson": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d.json" }, "components": [ { "bbox": { "h": 0.05, "w": 0.322, "x": 0.072, "y": 0.242 }, "kind": "image", "text": "KILI TECHNOLOGY", "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", "slug": "logo", "status": "active", "canonId": "019de52c-fdaf-743d-94c4-d7c6a0e55302", "version": 1, "description": null }, "framework": null }, "subkind": "logo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:33:18.457+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0c9db9bd-70f4-4471-b800-3710402b606d", "parentComponentId": null }, { "bbox": { "h": 0.018, "w": 0.601, "x": 0.08, "y": 0.521 }, "kind": "subtitle", "text": "THE TRAINING DATA MANAGEMENT PLATFORM FOR DATA-CENTRIC ORGANIZATIONS.", "attrs": null, "canon": { "kind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "82d821ee-2793-42e7-8f16-c6131385a3ce", "version": 1, "description": "Header-zone supporting text near the title (= ex body.description with bbox)." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:33:18.457+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7b3c8f8d-cd8f-473e-b863-9d4d89bd425b", "parentComponentId": null }, { "bbox": { "h": 0.048, "w": 0.493, "x": 0.08, "y": 0.443 }, "kind": "title", "text": "BUILD AI THAT MATTERS", "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 16:33:18.457+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9f96b758-5a1d-4906-ace9-61e57548ecc8", "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": "7d9734a1-d0d2-4be4-ac6c-ea4ce829d719", "evidence": "The slide is labeled as 'cover' and features a title, subtitle, and company logo.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-17 03:24:14.036047+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/__p-YQzRQsR7JTDslJM0AEBW_qj2sR4z2t5JpxP0auM/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy9RQnlKYW5FUFZHWlNqUUZnQXloay9wMDAxLmpwZw", "imagePathAlt": "https://imgproxy.kitesheet.com/2IYfgknQa4l4OdQFUOsTCyOkfPi8pjreMyhex1c6i_s/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy9RQnlKYW5FUFZHWlNqUUZnQXloay9wMDAxLnBuZw", "thumbSrcAlt": "https://imgproxy.kitesheet.com/RBk6CPQW_G734G7IIAV9NMhW2WRetZU7REQ_ijlgpKM/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy9RQnlKYW5FUFZHWlNqUUZnQXloay9wMDAxLnBuZw" }, { "page": 2, "type": "tension", "title": "Uses a 'before-after' or 'problem-solution' framing where the top half is the market problem and the bottom half is the root cause.", "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": "overcrowded", "densityScore": 79, "componentCount": 9, "textChars": 479, "nDataPoints": 4, "notes": "Uses a 'before-after' or 'problem-solution' framing where the top half is the market problem and the bottom half is the root cause.", "elementsJson": [ "headline_text", "icon_grid", "big_number", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:33:22.39+00", "seconds": 9.463, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5f69-752d-a9b7-9b572aef1b8d/2", "deck": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d", "deckAnchor": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d#slide-2", "deckJson": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "...WHILE TRAINING DATA IS MODELS' DNA", "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 14:43:29+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-a28d-7358-81db-abfa957c18e8", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.9, "x": 0.05, "y": 0.7 }, "kind": "list", "text": "Labeling is mandatory to leverage unstructured data; Labeling is human intensive; Labeling generates bias", "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 16:33:22.39+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8a633511-e35c-4db2-9bcb-0ad1ca16609a", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.3, "x": 0.05, "y": 0.32 }, "kind": "metric", "text": "80% of enterprises are investing in AI", "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 16:33:22.39+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0d5eac06-edba-4d89-a891-267b9cd1727c", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.3, "x": 0.65, "y": 0.32 }, "kind": "metric", "text": "80% unstructured data by 2025 ($733bn market size)", "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 16:33:22.39+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "75f1bb17-8ff8-4c2f-82d5-534ec1b1cc23", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.3, "x": 0.35, "y": 0.32 }, "kind": "metric", "text": "80% of projects not deployed in production", "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 16:33:22.39+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e761e594-c54f-4417-8074-f67b5dfd0dc3", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "AI project failure rate: 80%", "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 14:43:29+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-a28d-7358-81db-acdb4ffbcd03", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.3, "x": 0.05, "y": 0.95 }, "kind": "source-note", "text": "Sources : Markets & Markets, Grandview Research, GM Insights", "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 16:33:22.39+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a556e132-e4d1-44f7-bf12-3d7a1a37394c", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.9, "x": 0.05, "y": 0.62 }, "kind": "title", "text": "...WHILE TRAINING DATA IS MODELS' DNA", "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 16:33:22.39+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0a03c45d-1640-42ee-9d56-286670aa273a", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.9, "x": 0.05, "y": 0.15 }, "kind": "title", "text": "THE 80% BLINDSPOT: ENTERPRISES MASSIVELY INVEST IN AI, BUT STRUGGLE LEVERAGE IT...", "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 16:33:22.39+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a45c8a8a-3685-42de-969d-fdad8d66aac7", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [ { "matchId": "e293474a-3fa8-4e60-8cd2-a5d525949003", "evidence": "Contrasts the '80% blindspot' problem with the 'DNA' root cause.", "framework": null, "confidence": 0.8, "extraction": { "at": "2026-05-02 16:33:22.39+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 2, "frameworkName": "before-after-framing" } ], "arcBeats": [ { "to": 2, "arc": { "name": "Problem-Agitate-Solution", "slug": "problem-agitate-solution", "status": "active", "bestFor": "Sales pitches, urgency-driven, competitive displacement", "canonId": "019dd956-5ec6-70f9-9fd8-fdbaaff39acf", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem (Identify pain) -> Agitate (Make it worse) -> Solution (Provide relief)", "description": null, "familyLabel": null }, "from": 2, "name": "Problem", "beatId": "43067e69-9ce7-46c0-b53b-5465c6e3c98e", "matchId": "678d59ad-5d71-4d6e-9247-acca04319923", "beatType": { "name": "Problem (Identify pain)", "slug": "problem-agitate-solution-problem-identify-pain", "status": "active", "canonId": "019dd9b8-0e78-74bb-b8f6-54348367a131", "version": 1, "description": null }, "evidence": "THE 80% BLINDSPOT: ENTERPRISES MASSIVELY INVEST IN AI, BUT STRUGGLE LEVERAGE IT...", "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": 6, "from": 2, "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": "f8fcc917-6c1d-4ce3-af79-db57902e5dc6", "evidence": "Poor data quality hinders AI performance, Kili Technology's platform solves this problem", "position": 0, "objective": "Highlight the cost of not solving the data quality problem", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:34:40.918819+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 3, "type": "setup", "title": "Uses historical examples (Facebook tagging, Google/Bighead architectures) to establish that data-centric AI is not a new concept.", "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": 78, "componentCount": 8, "textChars": 592, "nDataPoints": 1, "notes": "Uses historical examples (Facebook tagging, Google/Bighead architectures) to establish that data-centric AI is not a new concept.", "elementsJson": [ "headline_text", "action_title", "screenshot", "logo_grid", "paragraph" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:33:20.037+00", "seconds": 7.027, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5f69-752d-a9b7-9b572aef1b8d/3", "deck": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d", "deckAnchor": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d#slide-3", "deckJson": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Tech leaders started tackling annotation at scale a decade ago, having experienced poor performance when not iterating on their data", "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 14:43:28+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-a28d-7358-81db-1067a275ef8c", "parentComponentId": null }, { "bbox": { "h": 0.26, "w": 0.29, "x": 0.05, "y": 0.348 }, "kind": "image", "text": "Autonomous driving object detection screenshot", "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 16:33:20.037+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "857f12db-a2a4-4ad4-a45c-33e360b45768", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.76, "x": 0.12, "y": 0.7 }, "kind": "image", "text": "Google, Facebook, Apple, Lyft, Amazon, Uber 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 16:33:20.037+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "dce0462e-e719-4f55-aa3b-1470a811bb02", "parentComponentId": null }, { "bbox": { "h": 0.42, "w": 0.26, "x": 0.37, "y": 0.25 }, "kind": "image", "text": "Facebook photo tagging UI", "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 16:33:20.037+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "6799873c-a5a2-4c65-a297-f2c795ae21df", "parentComponentId": null }, { "bbox": { "h": 0.28, "w": 0.3, "x": 0.65, "y": 0.32 }, "kind": "image", "text": "Bighead architecture diagram", "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 16:33:20.037+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e58ba05e-d458-4705-acce-c82722533dbe", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.8, "x": 0.1, "y": 0.78 }, "kind": "list", "text": "Google started annotation in 2013; Facebook leveraged users to annotate at scale; Apple, Lyft, Amazon, Uber all built their own annotation platform", "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 16:33:20.037+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7538a4c7-b3cc-4009-9077-62185664f939", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.7, "x": 0.15, "y": 0.88 }, "kind": "paragraph", "text": "Tech leaders started tackling annotation at scale a decade ago, having experienced poor performance when not iterating on their data", "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 16:33:20.037+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9361aac9-b895-48ef-b908-5c4cf25b1757", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.55, "x": 0.225, "y": 0.135 }, "kind": "title", "text": "LABELING IS NOT AN EMERGING TREND", "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 16:33:20.037+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a8a2afde-058b-4806-b534-1964abba4af7", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Annotation", "slug": "annotation", "status": "active", "bestFor": null, "canonId": "019dd956-b5b9-7668-bf0e-3af99823ec5c", "version": 1, "bodyDocId": null, "description": "Adding explanatory labels to highlight key data points", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "f082d417-0bc7-4d22-8b5d-3db6b0d1bde1", "evidence": "Tech leaders started tackling annotation at scale a decade ago", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:24:14.194656+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": "67803418-00fb-4b3c-8255-692a56e6da76", "evidence": "Citing Google, Facebook, Amazon, etc. to validate the necessity of annotation platforms.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 16:33:20.037+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "frameworkName": "authority-citation" } ], "arcBeats": [ { "to": 3, "arc": { "name": "Problem-Agitate-Solution", "slug": "problem-agitate-solution", "status": "active", "bestFor": "Sales pitches, urgency-driven, competitive displacement", "canonId": "019dd956-5ec6-70f9-9fd8-fdbaaff39acf", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem (Identify pain) -> Agitate (Make it worse) -> Solution (Provide relief)", "description": null, "familyLabel": null }, "from": 3, "name": "Agitate", "beatId": "25d4a852-bc8e-473e-aa01-7f85fe36afb1", "matchId": "678d59ad-5d71-4d6e-9247-acca04319923", "beatType": { "name": "Agitate (Make it worse)", "slug": "problem-agitate-solution-agitate-make-it-worse", "status": "active", "canonId": "019dd9b8-0f5a-71dd-bae7-8fadb0d09456", "version": 1, "description": null }, "evidence": "Tech leaders started tackling annotation at scale a decade ago, having experienced poor performance when not iterating on their data", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Development", "slug": "development", "status": "active", "canonId": "019dd9b8-0332-7008-b3aa-11b0840a57b1", "version": 1, "description": "The middle stretch in arcs that have one. Often where length problems creep in." }, "alignedBlockIds": null, "matchConfidence": 0.8 } ], "loops": [ { "to": 6, "from": 2, "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": "f8fcc917-6c1d-4ce3-af79-db57902e5dc6", "evidence": "Poor data quality hinders AI performance, Kili Technology's platform solves this problem", "position": 0, "objective": "Highlight the cost of not solving the data quality problem", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:34:40.918819+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 4, "type": "tension", "title": "Uses a metaphor of a diamond to represent data value, contrasted with the problem of label errors.", "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": 66, "componentCount": 6, "textChars": 489, "nDataPoints": 4, "notes": "Uses a metaphor of a diamond to represent data value, contrasted with the problem of label errors.", "elementsJson": [ "headline_text", "action_title", "callout_box", "icon_grid" ], "confidence": 0.95, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:33:21.417+00", "seconds": 8.348, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5f69-752d-a9b7-9b572aef1b8d/4", "deck": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d", "deckAnchor": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d#slide-4", "deckJson": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "a 10% reduction in label accuracy typically results in a 2-5% model performance decrease", "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 15:59:41+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-d9f4-71ca-9728-fcdf4a199607", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "model performance: 10%", "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 15:59:41+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-d9f4-71ca-9729-03efac122961", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.4, "x": 0.55, "y": 0.1 }, "kind": "paragraph", "text": "AI is made of 3 things: Computing power, Models, Data", "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 16:33:21.417+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "88e0cd69-fb5b-4101-9db2-b4de2f6de946", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.4, "x": 0.55, "y": 0.45 }, "kind": "paragraph", "text": "Up to 10% of errors found in most-used open-source datasets: Mislabeled images, Mislabeled sentiment, Mislabeled audio", "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 16:33:21.417+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a7ae362f-d2d3-4861-8ba4-3c2f77922bba", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.4, "x": 0.55, "y": 0.75 }, "kind": "paragraph", "text": "Model performance deteriorates with decreasing label quality: a 10% reduction in label accuracy typically results in a 2-5% model performance decrease", "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 16:33:21.417+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b2f50619-603e-4c1c-801c-8313606e2453", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.4, "x": 0.06, "y": 0.44 }, "kind": "title", "text": "DATA, AS ENTERPRISES' FIRST STRATEGIC ASSETS, IS A DIAMOND", "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 16:33:21.417+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "481d93b2-b764-4291-a4ea-ca268cf83664", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Data-Ink Ratio", "slug": "data-ink-ratio", "status": "active", "bestFor": null, "canonId": "019dd956-afc8-70da-b378-92834d36fde6", "version": 1, "bodyDocId": null, "description": "Maximize data, minimize non-data ink - remove chartjunk and decoration", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "aa980371-fcdc-40ff-84db-bc63317686d7", "evidence": "paragraph/paragraph: model performance: 10%", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.5, "extraction": { "at": "2026-07-17 03:24:14.276053+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [ { "matchId": "c9f7e735-aa70-48a6-a923-88f2c83a6248", "evidence": "Data is a diamond", "framework": null, "confidence": 0.009, "extraction": { "at": "2026-05-02 16:33:21.417+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "frameworkName": "metaphor-analogy" } ], "arcBeats": [], "loops": [ { "to": 6, "from": 2, "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": "f8fcc917-6c1d-4ce3-af79-db57902e5dc6", "evidence": "Poor data quality hinders AI performance, Kili Technology's platform solves this problem", "position": 0, "objective": "Highlight the cost of not solving the data quality problem", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:34:40.918819+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 5, "type": "setup", "function": "inspire_vision", "rawType": "mission", "block": null, "metadata": { "slideType": "mission", "slideTypeCanon": { "name": "Mission", "slug": "mission", "status": "active", "canonId": "019de52d-0fce-7496-b34c-18fd6a6e3310", "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": "balanced", "densityScore": 27, "componentCount": 4, "textChars": 194, "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 16:33:19.439+00", "seconds": 6.143, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5f69-752d-a9b7-9b572aef1b8d/5", "deck": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d", "deckAnchor": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d#slide-5", "deckJson": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "WITH AN INDUSTRIAL GRADE TRAINING AND MANAGEMENT DATA 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 14:43:29+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-a28d-7358-81db-54a77d3fac4e", "parentComponentId": null }, { "bbox": { "h": 0.035, "w": 0.24, "x": 0.075, "y": 0.06 }, "kind": "image", "text": "KILI TECHNOLOGY", "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", "slug": "logo", "status": "active", "canonId": "019de52c-fdaf-743d-94c4-d7c6a0e55302", "version": 1, "description": null }, "framework": null }, "subkind": "logo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:33:19.439+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "24143cfe-4098-4754-88b5-4899c0a5e722", "parentComponentId": null }, { "bbox": { "h": 0.088, "w": 0.75, "x": 0.125, "y": 0.698 }, "kind": "paragraph", "text": "WITH AN INDUSTRIAL GRADE TRAINING AND MANAGEMENT DATA PLATFORM", "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 16:33:19.439+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a8224db1-aab4-45c8-a742-db8d4c6cf746", "parentComponentId": null }, { "bbox": { "h": 0.108, "w": 0.75, "x": 0.125, "y": 0.318 }, "kind": "title", "text": "WE HELP COMPANIES TURN RAW DATA INTO THEIR GROUND TRUTH", "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 16:33:19.439+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "424d9e90-13ae-4e93-b773-0a15de7f6b2d", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [ { "to": 6, "from": 2, "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": "f8fcc917-6c1d-4ce3-af79-db57902e5dc6", "evidence": "Poor data quality hinders AI performance, Kili Technology's platform solves this problem", "position": 0, "objective": "Highlight the cost of not solving the data quality problem", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:34:40.918819+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 uses a process flow diagram to illustrate the orchestration of AI production.", "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": "balanced", "densityScore": 26, "componentCount": 4, "textChars": 170, "nDataPoints": 0, "notes": "The slide uses a process flow diagram to illustrate the orchestration of AI production.", "elementsJson": [ "action_title", "process_diagram", "icon_grid" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 16:33:20.911+00", "seconds": 7.241, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5f69-752d-a9b7-9b572aef1b8d/6", "deck": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d", "deckAnchor": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d#slide-6", "deckJson": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d.json" }, "components": [ { "bbox": { "h": 0.4, "w": 0.86, "x": 0.07, "y": 0.51 }, "kind": "diagram", "text": "Monitoring and Computation process flow", "attrs": null, "canon": { "kind": { "name": "Diagram", "slug": "diagram", "status": "active", "canonId": "019de52c-f9f0-734a-81de-15382ff28c65", "version": 1, "description": "Schematic or relational diagram." }, "tool": null, "subkind": { "name": "Process flow", "slug": "process", "status": "active", "canonId": "019de52d-039c-718f-ad3a-efe9c4b3c838", "version": 1, "description": null }, "framework": null }, "subkind": "process", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:33:20.911+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c6b5a3aa-337f-485c-b68f-36470fc33411", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.15, "x": 0.45, "y": 0.93 }, "kind": "legend", "text": "HUMAN-IN-THE-LOOP", "attrs": null, "canon": { "kind": { "name": "Legend", "slug": "legend", "status": "active", "canonId": "31361933-3d22-4c70-bbad-0801e9fffc0c", "version": 1, "description": "Color/symbol decoder placed adjacent to a chart or diagram." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:33:20.911+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d32a03ff-b44e-49c2-87d4-687ff85ec7ae", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.15, "x": 0.25, "y": 0.68 }, "kind": "list", "text": "Label, Collaborate, Monitor quality, Automate labeling, Data pipelines", "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 16:33:20.911+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ae93918c-f751-46dc-820c-6a9ef491f2c6", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.76, "x": 0.12, "y": 0.36 }, "kind": "title", "text": "ONE PLATFORM TO ORCHESTRATE AI IN PRODUCTION", "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 16:33:20.911+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fb17793e-82b1-4ee3-8a84-3a889d7c66ec", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "2x2 matrix", "slug": "matrix-2x2", "status": "active", "bestFor": null, "canonId": "019dd9e1-50ab-72d1-8afb-3ab0a38e32a8", "version": 1, "bodyDocId": null, "description": "BCG matrix, importance/urgency, any axes-and-quadrants frame.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "5098dae6-7f7d-48e4-9679-f717539f9483", "evidence": "list/bullet: Label, Collaborate, Monitor quality, Automate labeling, Data pipelines", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.5, "extraction": { "at": "2026-07-17 03:24:14.385758+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [ { "matchId": "b9fe2ada-a133-412a-a49a-9ed31ba302a0", "evidence": "Explicitly labeled in the diagram flow", "framework": null, "confidence": 1, "extraction": { "at": "2026-05-02 16:33:20.911+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "frameworkName": "human-in-the-loop" } ], "arcBeats": [ { "to": 6, "arc": { "name": "Problem-Agitate-Solution", "slug": "problem-agitate-solution", "status": "active", "bestFor": "Sales pitches, urgency-driven, competitive displacement", "canonId": "019dd956-5ec6-70f9-9fd8-fdbaaff39acf", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Problem (Identify pain) -> Agitate (Make it worse) -> Solution (Provide relief)", "description": null, "familyLabel": null }, "from": 6, "name": "Solution", "beatId": "7e1d6f81-8129-4ccc-89b3-47bbcfd3d576", "matchId": "678d59ad-5d71-4d6e-9247-acca04319923", "beatType": { "name": "Solution (Provide relief)", "slug": "problem-agitate-solution-solution-provide-relief", "status": "active", "canonId": "019dd9b8-0fee-73e9-9ae5-799c7f3895cd", "version": 1, "description": null }, "evidence": "ONE PLATFORM TO ORCHESTRATE AI IN PRODUCTION", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Resolution", "slug": "resolution", "status": "active", "canonId": "019dd9b8-045d-703b-8dd7-e92aba3ba91b", "version": 1, "description": "How the deck ends. Required in 19/20 arcs. The exception (Sparkline) ends with new bliss which is functionally a resolution." }, "alignedBlockIds": null, "matchConfidence": 0.8 } ], "loops": [ { "to": 6, "from": 2, "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": "f8fcc917-6c1d-4ce3-af79-db57902e5dc6", "evidence": "Poor data quality hinders AI performance, Kili Technology's platform solves this problem", "position": 0, "objective": "Highlight the cost of not solving the data quality problem", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:34:40.918819+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 7, "type": "setup", "function": "state_next_steps", "rawType": "closing_ask", "block": null, "metadata": { "slideType": "closing_ask", "slideTypeCanon": { "name": "Closing ask", "slug": "closing_ask", "status": "active", "canonId": "019de52d-0aa4-750f-8aa1-8d622dd99f5c", "version": 1, "description": null }, "function": "state_next_steps", "functionCanon": { "name": "State next steps", "slug": "state_next_steps", "status": "active", "canonId": "019de52d-14ca-70ac-b534-971f458cdf29", "version": 1, "description": null }, "density": "sparse", "densityScore": 3, "componentCount": 2, "textChars": 21, "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 16:33:18.973+00", "seconds": 5.222, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5f69-752d-a9b7-9b572aef1b8d/7", "deck": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d", "deckAnchor": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d#slide-7", "deckJson": "/decks/019dd923-5f69-752d-a9b7-9b572aef1b8d.json" }, "components": [ { "bbox": { "h": 0.05, "w": 0.22, "x": 0.07, "y": 0.24 }, "kind": "image", "text": "KILI TECHNOLOGY", "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", "slug": "logo", "status": "active", "canonId": "019de52c-fdaf-743d-94c4-d7c6a0e55302", "version": 1, "description": null }, "framework": null }, "subkind": "logo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 16:33:18.973+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "177bced2-09dd-418b-8a3c-f916bcc24cbf", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.12, "x": 0.07, "y": 0.44 }, "kind": "title", "text": "THANKS", "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 16:33:18.973+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f3b8d0ae-9baa-422a-8549-51c09e71cc6c", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true } ], "arcBeats": [ { "from": 2, "to": 2, "label": "Problem", "description": "THE 80% BLINDSPOT: ENTERPRISES MASSIVELY INVEST IN AI, BUT STRUGGLE LEVERAGE IT..." }, { "from": 3, "to": 3, "label": "Agitate", "description": "Tech leaders started tackling annotation at scale a decade ago, having experienced poor performance when not iterating on their data" }, { "from": 6, "to": 6, "label": "Solution", "description": "ONE PLATFORM TO ORCHESTRATE AI IN PRODUCTION" } ], "loops": [ { "from": 2, "to": 6, "label": "Cost Of Inaction", "description": "Highlight the cost of not solving the data quality problem" } ] }