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Microsoft Commercial and Professional Services United States

Lionbridge

Enterprise Knowledge Search · Fraud Detection · Risk Management

Realizing the need for faster and more agile localization services Founded in 1996, Lionbridge combines the best of human and machine intelligence to support large companies around the world with content creation, content testing, and translation and localization services.

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"We already have more than 500 customers using AI to help with content optimization, and we're getting that content to market with high efficiency and...
Lionbridge disrupts localization industry using Azure OpenAI in Foundry Models and reduces turnaround times by up to 30% Lionbridge Technologies, LLC...
Reducing turnaround times by up to 30% with Azure OpenAI In 2023, Lionbridge began developing its solution with Azure OpenAI.
Since launching its AI-enabled solutions, which use GPT-4 on Azure OpenAI, Lionbridge has reduced project turnaround times by up to 30%.

Reported impact

Business outcome

Since launching its AI-enabled solutions, which use GPT-4 on Azure OpenAI, Lionbridge has reduced project turnaround times by up to 30%.

  • Productivity Improvement
  • Revenue Growth
  • Risk Reduction
  • Scalability

Solution

Lionbridge built its solution on Azure OpenAI Service, Azure AI Foundry, Azure AI Translator to automate high-friction work and turn enterprise data into faster decisions. The implementation combines cloud application services, data services, and AI capabilities so business users can search, summarize, analyze, or act on information through a single digital workflow instead of manual handoffs. Since launching its AI-enabled solutions, which use GPT-4 on Azure OpenAI, Lionbridge has reduced project turnaround times by up to 30%. The architecture emphasizes managed services, reusable integrations, and governed data handling so the organization can scale adoption without rebuilding the solution for each new team or use case.

Data flow

Content and operational context are prepared in the application data layer, passed to Azure OpenAI for generation or extraction, stored in Azure storage services, and returned through the user-facing application to employees or customers with grounded responses.

Solution architecture

4 components · 2 layers
  1. Compute
    • Azure OpenAI Service Runs large language model inference for summarization, extraction, conversational AI, and grounded answers.
    • Azure AI Translator Translates multilingual content and inputs inside the workflow.
  2. Orchestration
    • Azure AI Foundry Provides the managed environment for model orchestration, experimentation, and responsible AI operations.
    • Power Platform Provides the low-code automation and application platform for workflow modernization.

Architecture clues

  • Uses Azure OpenAI for grounded generation or large-scale extraction workloads.

Evidence from the source

"We're helping our employees learn and grow with the power of AI, and at the end of the day, we're helping our customers, too." "We've been able to respond quickly to changes in technology because our relationship with Microsoft has helped...
Contact Sales Microsoft 365 Dynamics 365 Microsoft Power Platform Microsoft Teams Microsoft Security Windows 365 Nonprofit Transform work with Microsoft AI Bring intelligence into the flow of work and help your organization achieve its...
Lionbridge disrupts localization industry using Azure OpenAI in Foundry Models and reduces turnaround times by up to 30% Lionbridge Technologies, LLC (Lionbridge), a global translation and localization services provider, is using Microsoft...
Realizing the need for faster and more agile localization services Founded in 1996, Lionbridge combines the best of human and machine intelligence to support large companies around the world with content creation, content testing, and...
Since launching its AI-enabled solutions, which use GPT-4 on Azure OpenAI, Lionbridge has reduced project turnaround times by up to 30%.
Transparency Evidence & methodology 95/100 reference value · 89% 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 value95/100
  • 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 impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Microsoft

    Lionbridge customer story

    First-party
    https://www.microsoft.com/en/customers/story/1792260322207475324-lionbridge-technologies-azure-openai-service-other-en-united-states
    Source type
    Vendor-published customer story
    Published
    Jul 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

  • Published on source page Jul 18, 2024 · Microsoft · First-party
    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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