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AWS Healthcare United States

Availity

Enterprise Knowledge Search · IT Operations Automation

Availity needed faster access to scattered enterprise knowledge, source-code context, and large analytics datasets because employees were spending too much time retrieving information for data analysis, software development, and release management.

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33 percent of Availity’s total code-base
In only six months, Availity implemented three Amazon Q services
cut their data research time in half
dropping from 2 hours to 30 minutes —a 75 percent time savings

Reported impact

Business outcome

Within six months, Availity implemented three Amazon Q services, cut data research time in half, dropped release-review meetings from 2 hours to 30 minutes, used Amazon Q Developer on code that now accounts for 33 percent of the codebase, and enabled users to query a two-trillion-row dataset in seconds with natural language.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Availity deployed multiple Amazon Q services to make enterprise knowledge, code, and analytics easier to use across its healthcare network operations. Amazon Q Business provides conversational assistants that retrieve information from scattered documentation and participate in release-management workflows, including automatically analyzing committed changes from source control. Amazon Q Developer accelerates software-development tasks such as code generation, formatting, issue analysis, and migration troubleshooting, while Amazon Q in QuickSight lets users query Availity’s large enterprise datasets in natural language and generate business explanations and visuals for the company’s digital newspaper report. Together, these components connect internal knowledge bases, source-code repositories, and analytics data into a unified AI-assisted workflow that reduces search effort, shortens review cycles, and helps teams make decisions faster.

Data flow

Enterprise documents, source-control changes, and analytics datasets feed Amazon Q Business, Amazon Q Developer, and Amazon Q in QuickSight so Availity employees can retrieve answers, review releases, troubleshoot code, and generate data insights through conversational AI.

Solution architecture

3 components · 1 layer
  1. Serving
    • Amazon Q Business Powers conversational assistants for enterprise knowledge retrieval and release-management analysis.
    • Amazon Q Developer Assists Availity engineers with coding, troubleshooting, code review, and migration work.
    • Amazon Q in QuickSight Lets users explore enterprise datasets with natural language and generate AI-assisted business narratives and visuals.

Architecture clues

  • Q Business automatically retrieves and analyzes committed changes from source control.
  • Q Developer helped resolve a major migration performance issue in minutes instead of days.
  • Q in QuickSight powers data stories for Availity's digital newspaper report.

Evidence from the source

Amazon Q Developer supports coding activities that result in 33 percent of Availity’s total code-base.
Availity created a team-wide Q&A conversational assistant with Amazon Q Business to make its release-management process more efficient.
Availity turned to Amazon Q Business Amazon Q Developer, and Amazon Q in QuickSight.
Review meetings are now quick and focused, dropping from 2 hours to 30 minutes —a 75 percent time savings.
Users can now explore Availity’s two trillion-row dataset in seconds using natural language
Transparency Evidence & methodology 90/100 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 value90/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric 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 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 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. AWS

    Availity customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/availity-case-study/
    Source type
    Vendor-published customer story
    Published
    Feb 27, 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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