Databricks Software / SaaS United Kingdom

Thomas

Enterprise Knowledge Search

Not only did these traditional methods struggle to scale, but they weren’t connected to the typical applications necessary within a modern work environment. One main objective was to transition away from the labor-intensive model of manually training HR directors and hiring managers to understand and implement their psychometric assessments.

Because a user profile is completed with Thomas every 90 seconds, it was imperative for them to be able to ingest massive amounts of data more effectively and maintain their leadership in the field of people science
We could give our clients the ability to find the answers they need, rather than throwing a 40- to 50-page report at them

Reported impact

Business outcome

Reported outcomes include Because a user profile is completed with Thomas every 90 seconds, it was imperative for them to be able to ingest massive amounts of data more effectively and maintain their leadership in the field of people science; We could give our clients the ability to find the answers they need, rather than throwing a 40- to 50-page report at them.

  • Revenue Growth

Solution

Thomas’ central ethos has been that companies can elevate job satisfaction and productivity by better understanding how people interact with each other in the workplace. As they sought to expand and deepen their impact in the field of people science, Thomas encountered major roadblocks with traditional data management and utilization approaches. They turned to Databricks for help with improving their data workflows — from ingestion and transformation to analysis and solutions for customers and employees. Plus, the integration of GenAI through AI Search and retrieval augmented generation (RAG) further improved content searchability and automated content generation. The Databricks Data + AI Platform and Databricks tools enabled Thomas to improve their data workflows — from ingestion to transformation to analysis — all within a secure environment.

Data flow

Operational, customer, and transactional data lands in Databricks, is transformed in the lakehouse, and is served to downstream users and applications through Microsoft Teams.

Solution architecture

3 components · 2 layers
  1. Compute
    • Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workloads.
  2. Serving
    • AI Search Provides semantic retrieval over enterprise content for downstream generative AI experiences.
    • Microsoft Teams Acts as the conversational delivery channel for the data assistant experience.

Architecture clues

  • Runs on Azure.

Evidence from the source

As they sought to expand and deepen their impact in the field of people science, Thomas encountered major roadblocks with traditional data management and utilization approaches.
Because a user profile is completed with Thomas every 90 seconds, it was imperative for them to be able to ingest massive amounts of data more effectively and maintain their leadership in the field of people science
They turned to Databricks for help with improving their data workflows — from ingestion and transformation to analysis and solutions for customers and employees.
Thomas’ central ethos has been that companies can elevate job satisfaction and productivity by better understanding how people interact with each other in the workplace.
We could give our clients the ability to find the answers they need, rather than throwing a 40- to 50-page report at them
Transparency Evidence & methodology 100/100 reference value · 97% confidence · 2 sources

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 confidence97%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references 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. Databricks

    Thomas customer story

    First-party
    https://www.databricks.com/customers/thomas
    Source type
    Vendor-published customer story
    Published
    May 21, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. Companies House

    THOMAS INTERNATIONAL LIMITED overview

    Government
    https://find-and-update.company-information.service.gov.uk/company/01568983

    The registered office address is listed as 'First Floor, 18 Oxford Road, Marlow, Buckinghamshire, United Kingdom, SL7 2NL.'

    Source type
    Government registry
    Supports
    • Region

Date evidence

  • Published on source page May 21, 2024 · Databricks · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.