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MongoDB Software / SaaS Global

DevRev

Customer Service Automation · Enterprise Knowledge Search

Using MongoDB Atlas to consolidate data and boost efficiency for DevRev The mechanics of running a modern business are complex, involving many separate teams and systems — from customer relationship management (CRM) and support to IT service management (ITSM).

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Reported impact

Business outcome

Building a scalable foundation for AI-powered efficiency As DevRev has grown, it has evolved its use of MongoDB from simply using the service to communicating with the MongoDB team to develop new features and capabilities.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Using MongoDB Atlas to consolidate data and boost efficiency for DevRev The mechanics of running a modern business are complex, involving many separate teams and systems — from customer relationship management (CRM) and support to IT service management (ITSM). Improving development velocity by 3 to 4 times using MongoDB Relying on MongoDB Atlas has empowered DevRev to innovate and make its AgentOS product the go-to solution that companies of all sizes can use to consolidate their teams and data sources into one platform. However, these commonly operate in silos, leading to inefficiency and communication gaps. was born out of this need to bring data and people together, embracing the capabilities of AI and creating a new vision of a unified data platform. “One of the main challenges was improving how we can coordinate across geographically dispersed teams and disparate systems,” said Steven Poitras, Founding Architect at DevRev.

Data flow

Business documents, tickets, records, and other operational content stay in MongoDB Atlas Vector Search and MongoDB Atlas, where search indexes or vector embeddings are built and then queried in real time to supply grounded results to applications, assistants, or discovery features. Upstream and downstream integrations include CRM systems, ITSM systems, support chat.

Solution architecture

2 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores the operational, document, and context data behind DevRev's AI-powered search and assistant experiences.
  2. Serving
    • MongoDB Atlas Vector Search Retrieves semantically relevant context from DevRev's data for AI, search, and recommendation workflows.

Architecture clues

  • AI agents or assistants are layered on top of the MongoDB data platform.
  • The deployment integrates with CRM systems.
  • The deployment is designed for multi-cloud flexibility.
  • The solution uses a microservices-based architecture.
  • Vector search is part of the retrieval layer for semantic or generative-AI use cases.

Evidence from the source

“By bringing data together and working on it in real time, we keep everyone in sync. Combine a platform built for AI with a full picture of the business and you have a gold mine for efficiency.”
“From the beginning, we always knew that MongoDB was going to be the core platform from a data perspective,”
“One of the main challenges was improving how we can coordinate across geographically dispersed teams and disparate systems,”
“When we looked at MongoDB, we saw that not only did it provide scalability and flexibility as a service, but there’s also a lot of innovation taking place.”
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

    DevRev customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/devrev
    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 Mar 20, 2025 · 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 Jun 3, 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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