{
  "docId": "019de070-82f9-721e-a504-cf6c4a22e75d",
  "docSlug": "b01276dc1b29e24d648e5641f652a8af",
  "documentTitle": "Innoviz | SPAC Presentation Deck | 35 slides",
  "authorId": "innoviz",
  "authorName": "Innoviz Technologies",
  "documentKindSlug": "pitchdeck",
  "documentKindLabel": "Pitch deck",
  "sourceTypeSlug": "investment_bank",
  "sourceTypeLabel": "Investment bank",
  "presentationDate": "2020-12-01 00:00:00",
  "orientation": "landscape",
  "aspectRatio": 1.7777778,
  "pageNumber": 17,
  "pageCount": 35,
  "prevPage": 16,
  "nextPage": 18,
  "slideType": "solution",
  "function": "present_solution",
  "density": "dense",
  "nDataPoints": 2,
  "notes": "The slide uses a split layout with a list of key value propositions on the left and a visual demonstration of the software's object detection capabilities on the right.",
  "elementsJson": null,
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019de070-82f9-721e-a504-cf6c4a22e75d/17",
  "deckHref": "/decks/019de070-82f9-721e-a504-cf6c4a22e75d",
  "deckJsonHref": "/decks/019de070-82f9-721e-a504-cf6c4a22e75d.json",
  "deckAnchorHref": "/decks/019de070-82f9-721e-a504-cf6c4a22e75d#slide-17",
  "components": [
    {
      "bbox": {
        "h": 0.42,
        "w": 0.312,
        "x": 0.587,
        "y": 0.248
      },
      "kind": "image",
      "text": "Visual representation of LiDAR perception software detecting a truck and car on a highway.",
      "attrs": null,
      "subkind": "illustration",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c1560d9d-f6db-457e-b4e0-7976a53aa2ed",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.48,
        "w": 0.495,
        "x": 0.06,
        "y": 0.275
      },
      "kind": "list",
      "text": "Most Mature LiDAR-based perception software – with over 4 years of development\nAutomotive-Grade – automotive spice implementation\nMillions of object data are collected\nAbility To Leverage Existing Partners – automotive Tier 1s and OEMs\nLean Algorithms – optimized to run on lean low-cost automotive-grade chip\nEnables L3 Driving – best-in-class accuracy",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c47ccf1b-2225-4eff-bd56-a9a00f4c5fc2",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.53,
        "x": 0.05,
        "y": 0.85
      },
      "kind": "paragraph",
      "text": "*Offered at market price as benchmarked by the camera sector (~$50)",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "460b0588-d39a-47ee-9ecc-d4fffdfd2d95",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.06,
        "w": 0.65,
        "x": 0.03,
        "y": 0.04
      },
      "kind": "title",
      "text": "Industry-Leading LiDAR-Based Perception Software",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "05695f17-3a4f-4917-ad4b-132ebddc2e19",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.066,
        "w": 0.384,
        "x": 0.179,
        "y": 0.145
      },
      "kind": "title",
      "text": "Built on Proprietary Building Blocks",
      "attrs": null,
      "subkind": "subtitle",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "806bb3cf-2634-47ff-b470-afb06a9f1fa9",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "2x2 matrix",
      "slug": "matrix-2x2",
      "agent": null,
      "layer": "slide",
      "matchId": "a4f13ddf-7b9e-4135-96f6-bd402fbd0b66",
      "evidence": "Although not explicitly stated, the comparison table on the slide could be interpreted as a 2x2 matrix, but it's not a clear match.",
      "confidence": 0.5
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 17,
      "from": 16,
      "beatId": "0a3f7124-bcf2-4aef-8ca6-8e0a38872bb2",
      "arcName": "The Sequoia Pitch",
      "arcSlug": "sequoia-pitch",
      "beatName": "Solution",
      "beatSlug": "sequoia-pitch-solution",
      "evidence": "The solution is presented on pages 16-17",
      "position": 1,
      "confidence": 0.8,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    }
  ],
  "loops": [
    {
      "to": 17,
      "from": 16,
      "name": "Quick Win Big Bet",
      "slug": "47-quick-win-big-bet",
      "bestFor": "Transformation planning, 100-day plans, resource allocation",
      "matchId": "a0680c86-bf96-4ac6-bd09-f3648ded5770",
      "evidence": "The solution presented on pages 16-17 implies a big bet on LiDAR technology",
      "position": 2,
      "objective": "What is the potential impact of Innoviz'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
}