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Microsoft Software / SaaS Global

KMS Lighthouse

Customer Service Automation · Document Processing · Enterprise Knowledge Search

Always seeking innovation, it aimed to enhance its knowledge management platform to provide more efficient content creation and advanced AI capabilities for its global clientele across various industries.

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"Our net new business exceeded $1 million in the first few months." We've seen an increase in demand for our solution on the Azure Marketplace.
Its customers report a 70% reduction in onboarding times, a 40% reduction in call center wait times, a 150% growth in productivity authoring content
Its customers report a 70% reduction in onboarding times, a 40% reduction in call center wait times, a 150% growth in productivity authoring content, and a...
KMS Lighthouse enhanced access to its solution through Azure Marketplace, resulting in $1 million in new business within months.

Reported impact

Business outcome

KMS Lighthouse helps companies retrieve and share enterprise knowledge.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

KMS Lighthouse used Azure OpenAI Service, Dynamics 365 Field Service, Power Platform to replace disconnected manual workflows with a more consistent digital operating model. Customer and service data that had been scattered across CRM, service, and support tools were consolidated into Dynamics 365 so sales, case handling, dispatch, and reporting could run on a single omnichannel platform. Low-code automation and copilot capabilities orchestrate tasks, route work, and surface context to agents or business users without requiring separate point solutions. Where generative AI is involved, Azure OpenAI handles the language understanding layer while connected data and workflow services keep responses grounded in enterprise context. It also connects with surrounding systems such as Call-center telephony, which lets the new Azure-based workflow fit into existing operations rather than standing apart from them. KMS Lighthouse helps companies retrieve and share enterprise.

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 · 3 layers
  1. Compute
    • Azure OpenAI Service Runs large language model inference for summarization, extraction, conversational AI, and grounded answers.
  2. Serving
    • Azure Marketplace Distributes the solution through Microsoft’s commercial marketplace channel.
  3. Orchestration
    • Dynamics 365 Field Service Coordinates work orders, field dispatch, and downstream service execution.
    • 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

Always seeking innovation, it aimed to enhance its knowledge management platform to provide more efficient content creation and advanced AI capabilities for its global clientele across various industries.
And with Azure OpenAI Service, companies can create relevant content more quickly within the KMS Lighthouse application.
KMS Lighthouse enhanced access to its solution through Azure Marketplace, resulting in $1 million in new business within months.
KMS Lighthouse helps companies retrieve and share enterprise knowledge.
Reaching more customers As the company started growing globally, it introduced KMS Lighthouse as a transactable application on Azure Marketplace in 2021, making it more readily available to users.
Transparency Evidence & methodology 92/100 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 value92/100
  • 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 specificity12/20
    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 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

    KMS Lighthouse customer story

    First-party
    https://www.microsoft.com/en/customers/story/19875-kms-lighthouse-azure
    Source type
    Vendor-published customer story
    Published
    Dec 3, 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 Dec 3, 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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