Microsoft Manufacturing United States

Coats Digital

Application Refactoring · Document Processing · Supply Chain Optimization

Looking to improve customer onboarding and navigation of its database of product information and training materials, the company used Microsoft Azure to create an AI assistant that understands questions and provides precise answers.

Reported impact

Business outcome

Customers worldwide can now make the most of Coats Digital’s programs, such as its production planning tools, with natural language questions about features, documentation, order status, and more.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Coats Digital, a Manufacturing organization in United States, modernized this application refactoring workload with Azure OpenAI Service, Azure, Azure AI Services, Azure AI and Machine Learning, and Azure Machine Learning. Looking to improve customer onboarding and navigation of its database of product information and training materials, the company used Microsoft Azure to create an AI assistant that understands questions and provides precise answers. Azure OpenAI Service runs the large language model workloads that power summarization, generation, or conversational reasoning in the solution. Azure provides the scalable cloud foundation hosting the customer’s modernized applications, data, and AI workloads. Azure AI Services supplies packaged AI capabilities such as language, speech, or document intelligence for the application flow.

Data flow

Business transactions and user requests move through Azure OpenAI Service and related Microsoft services for orchestration and processing, with Azure AI Services delivering the resulting workflow and insight to end users.

Solution architecture

5 components · 2 layers
  1. Compute
    • Azure OpenAI Service runs the large language model workloads that power summarization, generation, or conversational reasoning in the solution.
    • Azure provides the scalable cloud foundation hosting the customer’s modernized applications, data, and AI workloads.
    • Azure AI Services supplies packaged AI capabilities such as language, speech, or document intelligence for the application flow.
    • Azure Machine Learning trains, manages, and deploys machine learning models used in the production workflow.
  2. Uncategorized
    • Azure AI and Machine Learning supports part of the customer’s Microsoft-based application and data workflow.

Evidence from the source

Azure OpenAI Service
Coats Digital is a longtime SaaS provider for the apparel industry that has taken its solutions to the next level with AI. Looking to improve customer onboarding and navigation of its database of product information and training materials, the company used Microsoft Azure to create an AI assistant that understands questions and provides precise answers. Customers worldwide can now make the most of Coats Digital’s programs, such as its production planning tools, with natural language questions about features, documentation, order status, and more. Since launching its AI tool, Coats Digital has helped its customers achieve more in less time while freeing up its support staff.
Coats Digital maintains a vast knowledge base of product documentation and training materials, known as Docs, and the company saw an opportunity to harness this information to enhance customer self-service and productivity, especially for new customers. However, with content dispersed across multiple sources, customers faced challenges finding the exact information they needed efficiently.
Customers worldwide can now make the most of Coats Digital’s programs, such as its production planning tools, with natural language questions about features, documentation, order status, and more.
Looking to improve customer onboarding and navigation of its database of product information and training materials, the company used Microsoft Azure to create an AI assistant that understands questions and provides precise answers.
Transparency Evidence & methodology 61/100 reference value · 87% 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 value61/100
  • 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 scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 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. Microsoft

    Coats Digital customer story

    First-party
    https://www.microsoft.com/en/customers/story/1710544856389676066-coatsdigital-azure-manufacturing-en-united-states
    Source type
    Vendor-published customer story
    Published
    Dec 7, 2023
    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 Dec 7, 2023 · 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.