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MongoDB Software / SaaS global

Huntr

Enterprise Knowledge Search

Huntr needed to replace Elasticsearch so its small engineering team could deliver reliable search across a rapidly growing job database, support global location-based discovery, and create headroom for new AI-driven job-search features.

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Huntr automatically generates job boards for over 150 major cities worldwide.
Huntr serves 400,000 customers across 190 countries.
The architecture no longer needs a DevOps person dedicated to running the database.
The platform stores more than 14 million jobs.

Reported impact

Business outcome

Huntr improved reliability, removed dedicated search-operations overhead for its three-person engineering team, and now serves global, AI-assisted job discovery experiences across millions of listings and hundreds of city-specific boards.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Huntr consolidated its search and operational datastore on MongoDB Atlas so its lean team could focus on product development instead of babysitting Elasticsearch. Atlas stores more than 14 million jobs and supports 400,000 users across 190 countries, while MongoDB Atlas Search powers the core experience: fuzzy keyword matching across job titles and descriptions, geospatial lookup for nearby roles, and automatically generated job boards for more than 150 cities. By moving search into the same managed platform that already held application data, Huntr eliminated separate search-cluster overhead and gained a more flexible base for experimentation. The company has already launched AI Resume Review on top of this stack and plans to extend the same data foundation with Atlas Vector Search so it can extract richer signals from its corpus and produce more actionable guidance for job seekers.

Data flow

Job records are stored in MongoDB Atlas, Atlas Search indexes descriptions, titles, and locations, users query the index through keyword and geospatial searches, and the same data is reused to create dynamic job boards and newer AI-assisted job-search features.

Solution architecture

2 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores Huntr's job data and provides the managed operational database for the platform.
  2. Serving
    • MongoDB Atlas Search Delivers fuzzy keyword and geospatial search across Huntr's global job listings and generated city pages.

Architecture clues

  • Geospatial search supports both city searches and proximity-based nearby results.
  • Huntr migrated search from Elasticsearch into MongoDB Atlas Search.
  • Huntr plans to use Atlas Vector Search for future AI features.
  • The platform uses fuzzy keyword search across job descriptions and titles.

Evidence from the source

“Huntr also uses this technology to power dynamic job boards for over 150 major cities worldwide.”
“Huntr migrated its search functionalities from Elasticsearch to MongoDB Atlas, simplifying the company’s tech stack and gaining the flexibility needed to quickly iterate and develop new features.”
“I consider MongoDB to be a fourth member of our engineering team. We don’t need a DevOps person dedicated to running our database when we have MongoDB Atlas in front of us.”
“Search is the number one function of Huntr’s website.”
Transparency Evidence & methodology 65/100 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 value65/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    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

    Huntr customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/huntr
    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 Jun 18, 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 Nov 26, 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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