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MongoDB Software / SaaS Global

Emergent Labs

Application Refactoring · IT Operations Automation

Emergent Labs needed a flexible, scalable database to support AI agents building and iterating on vibe-coded applications.

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Almost 2 million apps have been built on the platform, and multi thousands of them deployed.
In four months, nearly 2 million applications were built on Emergent Labs’ platform across 180 countries.
When users prompt the AI agents to build an application, the initial build typically takes just 5 to 20 minutes to produce a first working version.

Reported impact

Business outcome

In four months, nearly 2 million applications were built on Emergent Labs’ platform across 180 countries.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Emergent Labs needed a flexible, scalable database to support AI agents building and iterating on vibe-coded applications. The flexible document structure of MongoDB enables rapid iteration and development, allowing AI agents to dynamically adapt schemas. Through “vibe coding,” users describe what to build while autonomous AI agents write the code, test it, and refine it based on their feedback. “The software industry has been like a movie production house, where you need a big budget and large teams to produce software,” said Mukund Jha, Co-founder and CEO of Emergent Labs. The Emergent Labs team initially tried PostgreSQL, but it found that the agents frequently got stuck in schema migration loops when trying to add or delete columns as users added new functionality to their applications. “We found very early on that MongoDB Atlas was the best choice for our platform, especially for nondevelopers.

Data flow

Operational data from legacy or distributed applications is consolidated in MongoDB Atlas, where flexible document models support microservices, APIs, and downstream user experiences without batch-heavy synchronization. Upstream and downstream integrations include AWS, APIs.

Solution architecture

1 components · 1 layer
  1. Storage
    • MongoDB Atlas Provides the managed document database backing Emergent Labs's modernized application platform.

Architecture clues

  • The deployment integrates with APIs.
  • The deployment integrates with AWS.

Evidence from the source

“MongoDB Atlas is the easiest database for an AI agent to work with. The scaling properties are great, especially now. Almost 2 million apps have been built on the platform, and multi thousands of them deployed.”
“The software industry has been like a movie production house, where you need a big budget and large teams to produce software,”
“We found very early on that MongoDB Atlas was the best choice for our platform, especially for nondevelopers. These users care the most about their end-use case, so we can recommend the best defaults for an agentic app-building experience.”
“With a platform like ours, we’re truly trying to democratize software development, where anybody with an idea and a computer can build what they want.”
Transparency Evidence & methodology 96/100 reference value · 79% 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 value96/100
  • 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 impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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 specificity10/10
    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

    Emergent Labs customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/emergent-labs-inc
    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 Jan 3, 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 Dec 22, 2025 · 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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