{
  "docId": "019dd923-605c-759f-b6af-5a140f0f403b",
  "docSlug": "bi-637b035d1cfdc5cb",
  "documentTitle": "The pitch deck AI PDF startup Reducto used to raise $8.4 million",
  "authorId": "Pitchdecks",
  "authorName": "Reducto",
  "documentKindSlug": "pitchdeck",
  "documentKindLabel": "Pitch deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.778,
  "pageNumber": 4,
  "pageCount": 13,
  "prevPage": 3,
  "nextPage": 5,
  "slideType": "problem_statement",
  "function": "frame_problem",
  "density": "balanced",
  "nDataPoints": 0,
  "notes": "Uses a visual metaphor of 'Accuracy Drift' to illustrate the impact of ingestion issues.",
  "elementsJson": [
    "action_title",
    "bullet_list",
    "callout_box",
    "icon_grid"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-605c-759f-b6af-5a140f0f403b/4",
  "deckHref": "/decks/019dd923-605c-759f-b6af-5a140f0f403b",
  "deckJsonHref": "/decks/019dd923-605c-759f-b6af-5a140f0f403b.json",
  "deckAnchorHref": "/decks/019dd923-605c-759f-b6af-5a140f0f403b#slide-4",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Accuracy Drift",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-bb7f-7799-a419-e2d99b5a47c1",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.02,
        "w": 0.11,
        "x": 0.86,
        "y": 0.96
      },
      "kind": "disclaimer",
      "text": "Proprietary & Confidential | 4",
      "attrs": null,
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a28ffb3d-ff16-4cd0-8d9f-898e7aac4df7",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.2,
        "w": 0.39,
        "x": 0.53,
        "y": 0.61
      },
      "kind": "image",
      "text": "Accuracy Drift",
      "attrs": null,
      "subkind": "infographic",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "83c1f16e-4805-4085-87cf-5974501d05b0",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.18,
        "w": 0.33,
        "x": 0.07,
        "y": 0.615
      },
      "kind": "list",
      "text": "Scanned content\nComplicated tables, checkboxes\nNon-standard formatting",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "eb0d2c22-064b-47a1-b4b2-ecef83051511",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.18,
        "w": 0.69,
        "x": 0.07,
        "y": 0.27
      },
      "kind": "title",
      "text": "It's not a model problem, it's an ingestion problem",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a64679b4-fc2b-4e93-acb2-0c7b8a794316",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 5,
      "from": 3,
      "beatId": "524ba0b3-7614-4384-b02a-12912fc9a692",
      "arcName": "Problem-Agitate-Solution",
      "arcSlug": "problem-agitate-solution",
      "beatName": "Problem",
      "beatSlug": "problem-agitate-solution-problem-identify-pain",
      "evidence": "Slides 3-5 identify the problem with bad inputs in ML models",
      "position": 0,
      "confidence": 0.8,
      "parentBeatName": "Complication",
      "parentBeatSlug": "complication"
    }
  ],
  "loops": [
    {
      "to": 5,
      "from": 3,
      "name": "Cost Of Inaction",
      "slug": "27-cost-of-inaction",
      "bestFor": "Urgent budget requests, compliance, risk mitigation",
      "matchId": "5b8e2071-2b89-4dfc-9ad3-7d76f08352da",
      "evidence": "Slides 3-5 imply the costs of inaction by highlighting the consequences of bad inputs",
      "position": 0,
      "objective": "What are the costs of not addressing bad inputs in ML models?",
      "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point",
      "confidence": 0.6,
      "description": "Quantify what happens if the audience does nothing"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}