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Microsoft Software / SaaS United States

Kinectify

Data Lake / Data Warehouse Modernization · Enterprise Knowledge Search · Real-time Analytics

A safer gaming environment To manage the complex task of tracking as many as 40 million transactions across networks daily, Kinectify created a robust AML platform built on Microsoft Azure .

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"We've been able to slash disposition time from nearly 13 minutes to just over 3 minutes per alert—and the industry average is 60 to 90 minutes per alert.
"We've detected 43 percent more suspicious activities
"We've detected 43 percent more suspicious activities and achieved 96 percent faster decisioning with our Azure-based platform," says Calvin.
A safer gaming environment To manage the complex task of tracking as many as 40 million transactions across networks daily, Kinectify created a robust AML...

Reported impact

Business outcome

This powerhouse of data management and analysis uses the cutting-edge capabilities of Azure to integrate data points from its wide range of sources to detect suspicious activities quickly and efficiently.

  • Compliance
  • Productivity Improvement
  • Revenue Growth
  • Risk Reduction

Solution

Kinectify modernized its analytics estate on Microsoft Fabric, Azure OpenAI Service, Azure AI Foundry, moving from fragmented legacy tooling to a governed cloud data platform. Fabric and related services unify streaming, lake, warehouse, and BI capabilities so analysts and frontline teams can query fresher data without stitching together separate products. This powerhouse of data management and analysis uses the cutting-edge capabilities of Azure to integrate data points from its wide range of sources to detect suspicious activities quickly and efficiently. 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

5 components · 3 layers
  1. Compute
    • Azure OpenAI Service Runs large language model inference for summarization, extraction, conversational AI, and grounded answers.
    • Azure AI Services Supplies prebuilt AI capabilities such as language, speech, or vision services within the application flow.
  2. Storage
    • Microsoft Fabric Unifies lake, warehouse, analytics, and BI workloads on a single managed data platform.
  3. 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

  • Consolidates analytics and BI workloads on Microsoft Fabric as a shared data foundation.
  • Uses Azure OpenAI for grounded generation or large-scale extraction workloads.

Evidence from the source

A safer gaming environment To manage the complex task of tracking as many as 40 million transactions across networks daily, Kinectify created a robust AML platform built on Microsoft Azure .
Kinectify has been able to build its scalable, robust, Microsoft Azure–powered AML platform with a seamless combination of Azure Cosmos DB, Azure AI Services, Azure Kubernetes Service, and the broader capabilities of Azure cloud services.
Kinectify uses Azure OpenAI Service for its large language model use cases of producing executive summaries of investigations and creating government filings, especially Suspicious Activity Reports.
This powerhouse of data management and analysis uses the cutting-edge capabilities of Azure to integrate data points from its wide range of sources to detect suspicious activities quickly and efficiently.
We then move the data into our analytical store, Microsoft Fabric , and analyze the data for business intelligence purposes.
Transparency Evidence & methodology 91/100 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 value91/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 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

    Kinectify customer story

    First-party
    https://www.microsoft.com/en/customers/story/1769930620896500264-kinectify-azure-openai-service-other-en-united-states
    Source type
    Vendor-published customer story
    Published
    May 17, 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 May 17, 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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