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MongoDB Media and Entertainment UK

Financial Times

Enterprise Knowledge Search

FT needed to deliver scalable, hybrid search that blends full-text precision and AI driven semantic discovery across more than a million daily search queries, maintaining performance and relevance at scale.

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Using MongoDB Atlas with Atlas Search and Atlas Vector Search, FT built an AI-powered hybrid search platform in just 18 weeks, combining keyword and semantic search for better content discovery.

Reported impact

Business outcome

Hybrid search launched in 18 weeks Supports over a million searches per day Improved relevance and reader engagement through AI-powered discovery

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

FT needed to deliver scalable, hybrid search that blends full-text precision and AI driven semantic discovery across more than a million daily search queries, maintaining performance and relevance at scale. Using MongoDB Atlas with Atlas Search and Atlas Vector Search, FT built an AI-powered hybrid search platform in just 18 weeks, combining keyword and semantic search for better content discovery. The deployment has positioned the as a leader in hybrid search within the media sector, offering readers highly relevant article suggestions—without the need for manual searches: a boon for time-poor subscribers. Next, the hybrid search rollout will extend to the ’s mobile apps, followed by its specialist titles, ensuring all users will benefit from its enhanced capabilities. Ultimately, the goal is to make content across all platforms accessible through a unified hybrid search system. “We are at the beginning, and we expect to improve it further,” said Terziev.

Data flow

Business documents, tickets, records, and other operational content stay in MongoDB Atlas Vector Search and MongoDB Atlas Search, 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, APIs.

Solution architecture

3 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores the operational, document, and context data behind Financial Times's AI-powered search and assistant experiences.
  2. Serving
    • MongoDB Atlas Vector Search Retrieves semantically relevant context from Financial Times's data for AI, search, and recommendation workflows.
    • MongoDB Atlas Search Provides relevance-based text retrieval across Financial Times's documents and application data.

Architecture clues

  • AI agents or assistants are layered on top of the MongoDB data platform.
  • The deployment integrates with APIs.
  • The deployment integrates with CRM systems.
  • Vector search is part of the retrieval layer for semantic or generative-AI use cases.

Evidence from the source

“Being able to slice, dice, and surface the content in different ways is critical.”
“From an architectural standpoint, it made a lot of sense to keep content searchable at [the] source rather than transferring it to an external search engine,”
“The problem is, hybrid search is new territory,”
“We have various products, and at the core of all of them is gold star journalism,”
Transparency Evidence & methodology 100/100 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 value100/100
  • 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 specificity20/20
    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

    Financial Times customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/ft
    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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