{
  "docId": "019dd923-5e88-73ef-bd5c-f812573a947a",
  "docSlug": "eea7524c557036f4",
  "documentTitle": "2020 Air Street Capital The State of AI Report 2020",
  "authorId": "AirStreetCapital",
  "authorName": "Air Street Capital",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "vc_research",
  "sourceTypeLabel": "VC research",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.777,
  "pageNumber": 117,
  "pageCount": 177,
  "prevPage": 116,
  "nextPage": 118,
  "slideType": "client_example",
  "function": "illustrate_case",
  "density": "dense",
  "nDataPoints": 5,
  "notes": "The slide highlights the value proposition of Tractable's AI in the insurance industry, specifically for auto claims.",
  "elementsJson": [
    "headline_text",
    "action_title",
    "bullet_list",
    "screenshot"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f812573a947a/117",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a#slide-117",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Tractable's AI captures and processes imagery of the damage to automatically predict its repair costs.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-47c5-73ac-aa6e-e6960a1a383b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.55,
        "w": 0.35,
        "x": 0.62,
        "y": 0.25
      },
      "kind": "image",
      "text": "Tractable mobile app and web dashboard interface showing AI-based assessment.",
      "attrs": null,
      "subkind": "screenshot",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "827c6a98-55e0-4f1a-8fe5-62febab7a0dd",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.65,
        "w": 0.55,
        "x": 0.05,
        "y": 0.22
      },
      "kind": "list",
      "text": "Tractable's AI captures and processes imagery of the damage to automatically predict its repair costs.\nAccidents and disasters drive $1T of damage globally/year.\nRecovery always begins with visual damage appraisal.\nFor vehicle repair, Tractable's AI automates damage appraisal to accelerate recovery from 30 days to 1 week.\nThe system is trained using tens of millions of auto damage photos and the expert appraiser-approved repairs that ensued.\nUsers can now photograph damage on their phone and immediately obtain complete repair estimates, enabling near-instant decisions on next steps (total loss, repair, settlement etc) that would previously take days to weeks to reach.\nTractable has processed $1B+ in auto claims and is used today by the world's leading insurers, including Tokio Marine (Japan), Covea (France), Talanx-Warta (Central Europe), Admiral Seguros (Spain).",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "8ccba02e-4245-474f-9cbc-f633d923c314",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "recovery time: 1 week",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd952-47c5-73ac-aa6e-eba1e61e2414",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.8,
        "x": 0.05,
        "y": 0.14
      },
      "kind": "title",
      "text": "Computer vision unlocks faster accident and disaster recovery intervention",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "91385e18-3821-4e65-b929-6d3dbb2c1232",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Concrete Language",
      "slug": "concrete-language",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-0fd5-7148-8ecf-f29bd8756312",
      "evidence": "1 week intervention timeline.",
      "confidence": 75
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 129,
      "from": 10,
      "beatId": "019dd95a-0682-776c-8e34-ed2a8f38a754",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Research, Talent, Industry sections inventory what happened in AI in 2020.",
      "position": 1,
      "confidence": 60,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    },
    {
      "to": 129,
      "from": 10,
      "beatId": "019dd95a-0682-776c-8e34-fc384f439bba",
      "arcName": "The Mountain",
      "arcSlug": "mountain",
      "beatName": "Rising Action",
      "beatSlug": "mountain-rising-action",
      "evidence": "Research, Talent and Industry sections build momentum of AI progress.",
      "position": 2,
      "confidence": 40,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    }
  ],
  "loops": [
    {
      "to": 128,
      "from": 116,
      "name": "Pattern Hunter",
      "slug": "02-pattern-hunter",
      "bestFor": "Time-pressed audiences, building consensus, when data is strong",
      "matchId": "019dd95a-07fe-70ce-8d3d-2076d04ec9c2",
      "evidence": "Thirteen consecutive case studies: dialogue, claims, fraud, ESG, robots, NLP.",
      "position": 15,
      "objective": "Parade real-world AI deployments across verticals",
      "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion",
      "confidence": 75,
      "description": "Group multiple pieces of evidence that together point to a pattern or conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}