{ "docId": "019dd923-5fec-763b-95ce-69567becbfe7", "docSlug": "sgk9CpbOIjChNXkCSwL0", "documentTitle": "We got an exclusive look at the pitch deck AI training startup Edgify used to raise $6.5 million", "authorId": "Pitchdecks", "authorName": "Edgify", "documentKindSlug": "pitchdeck", "documentKindLabel": "Pitch deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 10, "pageCount": 13, "prevPage": 9, "nextPage": 11, "slideType": "comparison_table", "function": "compare_peers", "density": "overcrowded", "nDataPoints": 1, "notes": "Uses a hub-and-spoke vs. decentralized/edge architecture comparison.", "elementsJson": [ "headline_text", "action_title", "process_diagram", "bullet_list" ], "metadataConfidence": 1, "imagePath": null, "slideHref": "/slides/019dd923-5fec-763b-95ce-69567becbfe7/10", "deckHref": "/decks/019dd923-5fec-763b-95ce-69567becbfe7", "deckJsonHref": "/decks/019dd923-5fec-763b-95ce-69567becbfe7.json", "deckAnchorHref": "/decks/019dd923-5fec-763b-95ce-69567becbfe7#slide-10", "components": [ { "bbox": null, "kind": "callout", "text": "Allows to keep an accuracy of 99.9% without ever loosing a %.", "attrs": null, "subkind": null, "toolName": "Visual emphasis", "toolSlug": "visual-emphasis", "confidence": null, "componentId": "019dd952-d525-735b-8931-75cfd72cba7c", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.45, "w": 0.28, "x": 0.58, "y": 0.175 }, "kind": "diagram", "text": "Edgify edge architecture", "attrs": null, "subkind": "hub-spoke", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "755f102d-bfcc-4c69-9928-c80364de5aaf", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.45, "w": 0.28, "x": 0.16, "y": 0.175 }, "kind": "diagram", "text": "Cloud-based architecture", "attrs": null, "subkind": "hub-spoke", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "a620bf72-894f-44fe-9159-a5fbbad42145", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.25, "w": 0.38, "x": 0.58, "y": 0.7 }, "kind": "list", "text": "No need to collect images and send them to the cloud. Continuously train every time a purchase is made, locally on the device, so that it is best suited for the lighting conditions of that store. New produce can be introduced seamlessly as its ongoingly trained on the actual machine. The computer vision is trained everyday, so it learns the different stages of the produce from day 1 to day 5 on the shelf. Allows to keep an accuracy of 99.9% without ever loosing a %.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "7a708403-033e-44c2-a5c5-3ce122d1f89d", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.25, "w": 0.42, "x": 0.07, "y": 0.7 }, "kind": "list", "text": "In order to have a computer vision model at the store, you need to collect millions of data points and transfer them to the cloud. A computer vision solution trained in a lab on the cloud doesn't account for different lighting and angles in each store. Every time you want to introduce a new produce you need to train a new model. Produce from one store doesn't look exactly like the produce in a separate store. Produce on day one on the shelf doesn't always look like produce on day 5. To maintain high accuracy of detection you need to continuously send images to the cloud for retraining.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "e31db918-5022-46c5-92cb-05edd7217562", "frameworkName": null, "frameworkSlug": null }, { "bbox": null, "kind": "metric", "text": "accuracy: 99.9%", "attrs": null, "subkind": "primary", "toolName": "Quantification", "toolSlug": "quantification", "confidence": null, "componentId": "019dd952-d525-735b-8931-78b5cf109f77", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.06, "w": 0.58, "x": 0.21, "y": 0.03 }, "kind": "title", "text": "IT'S A NEW WAY OF THINKING ABOUT AI", "attrs": null, "subkind": "headline", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "9c76df06-a46c-48ee-ac77-6494df7347ed", "frameworkName": null, "frameworkSlug": null } ], "metrics": [], "tools": [ { "name": "Comparison frame", "slug": "comparison-frame", "agent": null, "layer": "slide", "matchId": "b043f0e0-9d21-4a8c-a02f-69b8f3942928", "evidence": "list/bullet: In order to have a computer vision model at the store, you need to collect millions of data points and transfer them to the cloud. A computer vision solution trained in a lab on the cloud doesn't account for; list/bullet: No need to collect images and send them to the cloud.", "confidence": 0.7 } ], "frameworks": [ { "name": "comparison-frame", "slug": null, "matchId": "0dbbccc3-5275-45d2-a538-48179347fa4d", "evidence": "Side-by-side comparison of traditional vs. new approach", "confidence": 1 } ], "arcBeats": [ { "to": 12, "from": 10, "beatId": "75128ae5-f2ec-49db-8854-2e7668b56d7a", "arcName": "The Sparkline", "arcSlug": "sparkline", "beatName": "Evidence", "beatSlug": "evidence", "evidence": "Providing additional information and unfair advantage", "position": 5, "confidence": 0.8, "parentBeatName": null, "parentBeatSlug": null } ], "loops": [ { "to": 12, "from": 10, "name": "Quick Win Big Bet", "slug": "47-quick-win-big-bet", "bestFor": "Transformation planning, 100-day plans, resource allocation", "matchId": "192baac6-3c78-47d7-8508-16935bfa853a", "evidence": "The comparison table and unfair advantage sections highlight the benefits and unique value proposition", "position": 2, "objective": "What are the benefits of Edgify's solution?", "structure": "The Full List -> Quick Wins (Low effort, High impact) -> Big Bets (High effort, High impact) -> Sequenced Roadmap", "confidence": 0.6, "description": "Separate initiatives into immediate wins and longer-term strategic bets" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }