What challenges did Availity face before AI implementation?

Enterprise knowledge and code were scattered across repositories and data sources. Teams spent too much time searching for documentation, committed changes, and analysis inputs. These inefficiencies slowed release management, code reviews, and insight generation.

Source

ProductionEvidence: Medium65/100

Availity increases productivity and accelerates code development with Amazon Q

Use case typeCoding assistantsUpdated Jun 13, 2026

Availity, a large real-time health information network, used Amazon Q Business, Amazon Q Developer, and Amazon Q in QuickSight to improve access to enterprise knowledge, accelerate software development, and make data analysis faster across teams. The company faced scattered code and documentation across multiple repositories, which made it difficult for employees to find relevant information for release management, code reviews, and analytics.

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

Reported outcomes

−75%

timeTime & speed

2 hourstime30 minutestime33%quantified impact

Strategic outcomes

Speed & agilityAccelerated release-management reviewsNew product / capabilityEnabled natural-language data explorationBetter decisions & insightImproved data-driven decision-makingNew product / capabilityImproved code development and quality

Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →

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

Normalized claim

Time: 2 hours decrease

AWS Customer StoriesMay 13, 2026Customer storyInferred claimMedium evidence strength

Release-management review meetings dropped from 2 hours to 30 minutes, a 75% time savings.

Normalized claim

Time: 30 minutes decrease

AWS Customer StoriesMay 13, 2026Customer storyInferred claimMedium evidence strength

Release-management review meetings dropped from 2 hours to 30 minutes, a 75% time savings.

Normalized claim

Time: 75% decrease

AWS Customer StoriesMay 13, 2026Customer storyInferred claimMedium evidence strength

Release-management review meetings dropped from 2 hours to 30 minutes, a 75% time savings.

Normalized claim

Quantified impact: 33%

AWS Customer StoriesMay 13, 2026Customer storyInferred claimMedium evidence strength

Amazon Q Developer support contributed to 33% of Availity's total code-base and helped the team resolve a critical migration performance issue in days instead of multiple days of extra work.

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

Customer: Availity
Provider: AWS
Maturity: Production
Linked source: AWS Customer Stories

Amazon Q Business was deployed as a team-wide conversational assistant for release management and retrieval of committed changes from source control

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Generative AI knowledge assistant
2Code development acceleration
3Natural-language analytics

Enterprise knowledge and code were scattered across repositories and data sources.
Teams spent too much time searching for documentation, committed changes, and analysis inputs.
These inefficiencies slowed release management, code reviews, and insight generation.

Availity worked with AWS Professional Services to implement Amazon Q Business, Amazon Q Developer, and Amazon Q in QuickSight.
Amazon Q Business was deployed as a team-wide conversational assistant for release management and retrieval of committed changes from source control.
Amazon Q Developer was integrated to accelerate tasks across the software development lifecycle and improve code quality.
Amazon Q in QuickSight was added to enable natural-language querying of large enterprise datasets and generate data stories for business reporting.

Technologies

Amazon Q Business Amazon Q Developer Amazon Q in QuickSight

Within six months, Availity cut data research time in half and delivered insights twice as fast.
Release-management review meetings dropped from 2 hours to 30 minutes, a 75% time savings.
Amazon Q Developer support contributed to 33% of Availity's total code-base and helped the team resolve a critical migration performance issue in days instead of multiple days of extra work.
Users could explore a two-trillion-row dataset in seconds using natural language, improving productivity and decision-making.

Implementation partners1

AWS Professional Services

Sources & evidence1

Evidence: Medium65/100Evidence strength