What challenges did Availity face before AI implementation?

Engineers spent significant time in coordination meetings, manual release reviews, documentation, security reviews, and performance testing. Senior leaders manually assessed hundreds of release packages in long meetings, slowing release cycles and consuming engineering capacity.

What was the impact of the AI solution?

Amazon Q Developer generated 33% of the code written by participating engineers and had a 31% suggestion acceptance rate, producing 19,500 lines of AI-generated code. The Risk Assessment Bot autonomously conducted 12,600 security scans and summarized findings. Release reviews that previously took hours can now be completed in minutes.

Source

Evidence: Medium50/100

Availity: Accelerating Healthcare Technology Innovation with Amazon Q and Amazon Bedrock

Use case typeHealthcare workflow automationUpdated Jun 13, 2026

Availity, a U. S.-based healthcare technology company, used Amazon Q Developer, Amazon Q Business, and Amazon Bedrock to streamline software development workflows and reduce routine engineering overhead. The company built specialized generative AI bots integrated into development tools and chat systems to automate documentation, security assessment, release readiness, and risk analysis.

Organization: Availity
Industry: Healthcare
Location: United States
Published: April 2026

Reported outcomes

33%

quantified impactOther quantified impact

31%quantified impact

Strategic outcomes

Speed & agilityStreamlined release review cyclesNew product / capabilityBuilt AI-powered workflow botsRisk & complianceAutomated security and risk assessment

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Quantified impact: 33%

AWS Case StudiesApr 29, 2026Case studyInferred claimMedium evidence strength

Amazon Q Developer generated 33% of the code written by participating engineers and had a 31% suggestion acceptance rate, producing 19,500 lines of AI-generated code.

Normalized claim

Quantified impact: 31%

AWS Case StudiesApr 29, 2026Case studyInferred claimMedium evidence strength

Amazon Q Developer generated 33% of the code written by participating engineers and had a 31% suggestion acceptance rate, producing 19,500 lines of AI-generated code.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Availity
Provider: AWS
Maturity: Unknown
Linked source: AWS Case Studies

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 5

1Code generation
2Developer productivity
3Release management

Engineers spent significant time in coordination meetings, manual release reviews, documentation, security reviews, and performance testing.
Senior leaders manually assessed hundreds of release packages in long meetings, slowing release cycles and consuming engineering capacity.

Availity adopted Amazon Q Developer as a code completion and pairing assistant for engineers.
Using Amazon Bedrock and Amazon Q Business, Availity created specialized bots including a Production Readiness Bot, Risk Assessment Bot, and Security Bot.
The bots are grounded in Availity data stored on AWS and are embedded into internal development workflows to automate documentation requirements, flag issues early, summarize risks, and suggest remediation steps.

Technologies

Amazon Q Developer Amazon Q Business Amazon Bedrock

Amazon Q Developer generated 33% of the code written by participating engineers and had a 31% suggestion acceptance rate, producing 19,500 lines of AI-generated code.
The Risk Assessment Bot autonomously conducted 12,600 security scans and summarized findings.
Release reviews that previously took hours can now be completed in minutes.

Sources & evidence1

Evidence: Medium50/100Evidence strength