{
  "docId": "019dd923-5eff-723e-9be4-79de8bd5e439",
  "docSlug": "9c498b9235fb4d0f",
  "documentTitle": "2024 MongoDB local NYC Product Update",
  "authorId": "MongoDB",
  "authorName": "MongoDB",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.777,
  "pageNumber": 38,
  "pageCount": 87,
  "prevPage": 37,
  "nextPage": 39,
  "slideType": "testimonial",
  "function": "illustrate_case",
  "density": "dense",
  "nDataPoints": 0,
  "notes": null,
  "elementsJson": [
    "quote_block"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5eff-723e-9be4-79de8bd5e439/38",
  "deckHref": "/decks/019dd923-5eff-723e-9be4-79de8bd5e439",
  "deckJsonHref": "/decks/019dd923-5eff-723e-9be4-79de8bd5e439.json",
  "deckAnchorHref": "/decks/019dd923-5eff-723e-9be4-79de8bd5e439#slide-38",
  "components": [
    {
      "bbox": {
        "h": 0.05,
        "w": 0.2,
        "x": 0.08,
        "y": 0.8
      },
      "kind": "paragraph",
      "text": "Russell Sherman, Co-Founder & CTO, VISO Trust",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "4e3f5628-0dee-4f24-96f0-76a69ebdad77",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.2,
        "x": 0.55,
        "y": 0.8
      },
      "kind": "paragraph",
      "text": "Sabato Severino, Senior AI Solution Architect, Eni",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a23ec16f-f61d-4979-a65c-e40b29f329cc",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.35,
        "x": 0.08,
        "y": 0.215
      },
      "kind": "quote",
      "text": "With Atlas Vector Search, we now possess a battle-tested vector/metadata database, refined over a decade, effectively addressing our dense retrieval requirements. There's no need to deploy a new database, as our vectors and artifact metadata can be seamlessly stored alongside each other.",
      "attrs": null,
      "subkind": "testimonial",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a61a7358-1fea-4dea-baac-09782f52fa9a",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.35,
        "x": 0.55,
        "y": 0.215
      },
      "kind": "quote",
      "text": "The generative AI we've introduced currently creates vector embeddings from documents, so when a user asks a question, it retrieves the most relevant document and uses LLMs to build the answer. We're looking at migrating vector embeddings into MongoDB Atlas to create a fully integrated, functional system. We'll then be able to use Atlas Vector Search to build AI-powered experiences without leaving the Atlas platform - a much better experience for developers.",
      "attrs": null,
      "subkind": "testimonial",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f9fe85b8-d130-422a-bd1c-f366fb95e6bc",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "quote",
      "text": "With Atlas Vector Search, we now possess a battle-tested vector/metadata database, refined over a decade, effectively addressing our dense retrieval requirements. There's no need to deploy a new database, as our vectors and artifact metadata can be seamlessly stored alongside each other. — Russell Sherman, Co-Founder & CTO, VISO Trust; The generative AI we’ve introduced currently creates vector embeddings from documents, so when a user asks a question, it retrieves the most relevant document and uses LLMs to build the answer. We’re looking at migrating vector embeddings into MongoDB Atlas to create a fully integrated, functional system. We’ll then be able to use Atlas Vector Search to build AI-powered experiences without leaving the Atlas platform - a much better experience for developers. — Sabato Severino, Senior AI Solution Architect, Eni",
      "attrs": null,
      "subkind": null,
      "toolName": "Authority citation",
      "toolSlug": "authority-citation",
      "confidence": null,
      "componentId": "019dd952-78b5-733e-80fd-131a029797c2",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [],
  "arcBeats": [],
  "loops": [],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}