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MongoDB Software / SaaS United States

Adobe

Enterprise Knowledge Search

Adobe needed an AI-ready data foundation that could support embedded generative AI features across its creative products without forcing users or internal developers to leave core workflows.

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Adobe uses MongoDB in a lot of places now and in a lot of products.
Over 800 million monthly active users rely on Adobe products.

Reported impact

Business outcome

Adobe can keep AI inside core creative workflows, maintain user momentum, and let internal developers build new AI solutions more quickly on a single platform.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Adobe uses MongoDB Atlas as an all-in-one data layer for the AI capabilities it is embedding across products such as Adobe Creative Cloud and Acrobat. The Developer Foundations team needed a platform that could store application data, support retrieval for AI tasks such as chatting with PDFs, and let internal developers build new tools without stitching together separate storage and search systems. Atlas gives Adobe a single platform for storage, vector-enabled retrieval, and persistent search context so AI features can access the right data with minimal latency and high reliability. That simplifies how developers prototype and ship new capabilities because they can stay inside one operational platform rather than moving data between multiple tools. The result is a foundation that supports creative workflows with embedded AI, helping users preserve momentum inside Adobe applications while giving engineering teams a faster way to deliver new intelligent product experiences.

Data flow

Product and document data stay in MongoDB Atlas, where Adobe’s AI features retrieve the necessary context for experiences such as chatting with PDFs and then return responses directly inside Creative Cloud and Acrobat workflows.

Solution architecture

1 components · 1 layer
  1. Storage
    • MongoDB Atlas Provides the unified storage and retrieval platform behind Adobe’s embedded AI features and internal AI application development.

Architecture clues

  • AI features are embedded directly inside existing product workflows such as Acrobat.
  • Adobe uses MongoDB as an all-in-one data layer for embedded AI tools.
  • The Developer Foundations team uses MongoDB to help Adobe engineers build AI applications.
  • The same platform supports storage, vector search, and search persistence.

Evidence from the source

Adobe uses MongoDB for its all-in-one data layer, underpinning Adobe’s continued development of embedded AI tools.
“MongoDB works well for us as a platform, because you have the storage there, you have the vector search there, you can do the search persistence, everything there in the single platform.”
“So far, our partnership has been great. The team is pretty great. We use MongoDB in a lot of places now, in a lot of products.”
“We want users to be more creative by using AI not as a separate tool or layer, but embedded within the product itself.”
Transparency Evidence & methodology 71/100 reference value · 77% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value71/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. MongoDB

    Adobe customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/adobe
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Apr 11, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Apr 9, 2026 · MongoDB · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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