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AWS Commercial and Professional Services Global

Thomson Reuters

Enterprise Knowledge Search

Thomson Reuters is a global provider of trusted content and technology to professionals across the legal, tax, and media industries. Thomson Reuters Labs, the company's dedicated innovation team, has been pioneering AI and natural language processing solutions for over 30 years.

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Reported impact

Business outcome

Thomson Reuters currently has more generative AI initiatives underway across all its major products as well as for a wide range of internal use cases, and the company is looking to develop even more LLM-based solutions.

  • Productivity Improvement
  • Speed to Market

Solution

In the fast-evolving world of artificial intelligence (AI), Thomson Reuters sought to stay at the forefront of responsible innovation by developing Open Arena , a self-service enterprise AI and machine learning (ML) platform, using Amazon Web Services (AWS). Thomson Reuters has evolved this initial concept into a purpose-built generative AI platform to democratize access to generative AI models and help its employees use AI safely and securely on innovative projects. The company expanded the solution's offering of models and use-case possibilities using Amazon Bedrock , a fully managed service from AWS that offers a choice of high-performing foundation models. As a result, Thomson Reuters reduced AI model deployment time from days to hours, streamlined testing and innovation, increased accessibility to generative AI tools, and simplified the user experience.

Solution architecture

1 components · 1 layer
  1. Compute
    • Amazon Bedrock Runs the foundation model layer for the knowledge search and question-answering workflows.

Architecture clues

  • "Instead, the product teams could independently explore in Open Arena using LLMs on Amazon Bedrock to see if this was a viable idea." Checkpoint Edge with CoCounsel is just one example of how Thomson...
  • "When initially developing Checkpoint Edge with CoCounsel, we didn't need to burden our engineering teams, which was a huge benefit," says Hron.
  • Using Amazon Bedrock, we can find the right model for the right job." Thomson Reuters's product teams used Amazon Bedrock to build a tax research generative AI application called Checkpoint Edge with...

Evidence from the source

"Using AWS, we can now efficiently use our world-class content and expertise with more breadth and efficiency than we could have before." Initially, its generative AI platform was used for existing generative AI applications, but Thomson...
As a result, Thomson Reuters reduced AI model deployment time from days to hours, streamlined testing and innovation, increased accessibility to generative AI tools, and simplified the user experience.
In the fast-evolving world of artificial intelligence (AI), Thomson Reuters sought to stay at the forefront of responsible innovation by developing Open Arena , a self-service enterprise AI and machine learning (ML) platform, using Amazon...
The company expanded the solution's offering of models and use-case possibilities using Amazon Bedrock , a fully managed service from AWS that offers a choice of high-performing foundation models.
The company now uses AI to improve information retrieval and automate content-sourcing processes.
Using AWS, Thomson Reuters developed Open Arena and, subsequently, its generative AI platform, a web-based playground where users can experiment with a growing set of ML tools powered by large language models (LLMs).
Transparency Evidence & methodology 56/100 reference value · 83% 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 value56/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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 scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence83%
  • 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
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity6/10
    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. AWS

    Thomson Reuters customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/thomson-reuters-bedrock-case-study/
    Source type
    Vendor-published customer story
    Published
    Apr 18, 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

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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