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MongoDB Software / SaaS Global

ElevenLabs

Application Refactoring · Enterprise Knowledge Search

Before shifting from AI research to enterprise products, ElevenLabs’ legacy database lacked native free-text search, draining engineering time.

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Reported impact

Business outcome

A stable data layer unlocked hyper-growth for ElevenLabs, driving it to $600M ARR and powering millions of global voice and chat agents.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Before shifting from AI research to enterprise products, ElevenLabs’ legacy database lacked native free-text search, draining engineering time. ElevenLabs moved to MongoDB on Google Cloud, eliminating separate index stores and sync code to bring simplicity to its data tier. The rapid advancement of artificial intelligence has transformed how global enterprises interact with audiences, shifting the focus from simple text interfaces to audio experiences. During the initial wave of the generative AI boom in 2022, London startup ElevenLabs set out to change the way humans interact with businesses through technology by building APIs for audio models. “Our voice Text to Speech model was revolutionary,” said Alex Holt, Field CTO at ElevenLabs. “It was the first AI model that sounded human.” However, ElevenLabs soon realized that deploying exceptional voice models was only part of the operational matrix.

Data flow

Application, customer, and workflow data is consolidated in MongoDB Atlas, indexed or enriched where needed, and then delivered to downstream applications, analytics, or AI features through low-latency queries and APIs. Upstream and downstream integrations include AWS, Google Cloud, APIs.

Solution architecture

1 components · 1 layer
  1. Storage
    • MongoDB Atlas Stores the operational, document, and context data behind ElevenLabs's AI-powered search and assistant experiences.

Architecture clues

  • AI agents or assistants are layered on top of the MongoDB data platform.
  • The deployment integrates with APIs.
  • The deployment integrates with AWS.
  • The deployment integrates with Google Cloud.

Evidence from the source

“Building an API that enables you to turn text into speech is fantastic,”
“But our customers all struggled with the same challenge: building something that not only sounds human but also interacts in a human way.”
“It was the first AI model that sounded human.”
“Our voice Text to Speech model was revolutionary,”
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

    ElevenLabs customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/elevenlabs
    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 Aug 21, 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 Jul 24, 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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