{
  "docId": "019dd923-605c-759f-b6af-bd7fbb24ec0b",
  "docSlug": "bi-0507489c772ca4c3",
  "documentTitle": "Pitch deck: Arize AI raises $38 million Series B from TCV, Battery",
  "authorId": "Pitchdecks",
  "authorName": "Arize AI",
  "documentKindSlug": "pitchdeck",
  "documentKindLabel": "Pitch deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.764,
  "pageNumber": 12,
  "pageCount": 16,
  "prevPage": 11,
  "nextPage": 13,
  "slideType": "comparison_table",
  "function": "compare_peers",
  "density": "balanced",
  "nDataPoints": 0,
  "notes": "The slide uses a three-column layout to differentiate ML observability from existing IT and data observability stacks.",
  "elementsJson": [
    "headline_text",
    "action_title",
    "comparison_table",
    "logo_grid",
    "headshot"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-605c-759f-b6af-bd7fbb24ec0b/12",
  "deckHref": "/decks/019dd923-605c-759f-b6af-bd7fbb24ec0b",
  "deckJsonHref": "/decks/019dd923-605c-759f-b6af-bd7fbb24ec0b.json",
  "deckAnchorHref": "/decks/019dd923-605c-759f-b6af-bd7fbb24ec0b#slide-12",
  "components": [
    {
      "bbox": {
        "h": 0.1,
        "w": 0.8,
        "x": 0.1,
        "y": 0.6
      },
      "kind": "image",
      "text": "Datadog, New Relic, Dynatrace, Monte Carlo, Bigeye, Arize",
      "attrs": null,
      "subkind": "logo-grid",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "62337245-0da5-4811-b177-75901416c233",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.65,
        "w": 0.84,
        "x": 0.08,
        "y": 0.25
      },
      "kind": "list",
      "text": "System / Infrastructure, Data, Machine Learning",
      "attrs": null,
      "subkind": "comparison",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "cc515bf3-7834-4fa2-a9a2-823171380232",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.8,
        "x": 0.1,
        "y": 0.05
      },
      "kind": "title",
      "text": "AI Needs Its Own ML Observability Solution",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "3ffc2167-13e0-4c4d-8610-92d30b963008",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.4,
        "x": 0.3,
        "y": 0.12
      },
      "kind": "title",
      "text": "Types of Observability Across IT",
      "attrs": null,
      "subkind": "subtitle",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "fd2a1e8d-f532-4a47-9bd1-5d9a0f9aff2f",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Comparison frame",
      "slug": "comparison-frame",
      "agent": null,
      "layer": "slide",
      "matchId": "baccdefa-10a2-42cf-a982-451835e1ca58",
      "evidence": "Types of Observability Across IT",
      "confidence": 0.8
    }
  ],
  "frameworks": [
    {
      "name": "comparison-frame",
      "slug": null,
      "matchId": "308f3468-2ca6-44e0-bfab-6083a913dc2e",
      "evidence": "Three-column comparison of observability domains",
      "confidence": 0.9
    }
  ],
  "arcBeats": [],
  "loops": [],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}