Get In Touch
a
M
Get In Touch

AI Code Review Case Study: Automating QA with Claude 3.5 & AWS

AWS GenAI Competency Partner

At a Glance

Client: Bayview Solutions
Industry: Financial Services / FinTech
Challenge: Manual code reviews creating bottlenecks and inconsistent quality in software delivery.
Solution: GitHub-integrated AI Code Review Agent.
Services Used: Amazon Bedrock (Claude 3.5 Haiku), AWS Lambda, API Gateway, Amazon S3
Outcome: Streamlined the software development lifecycle with instant, automated feedback on every Pull Request.
Key Impact: Achieved 45% faster pull request cycle times. 32% reduction in merge defects.

About Bayview Solutions

Bayview Solutions is a specialized financial services firm focused on accounts receivable solutions, distressed asset acquisition, and debt recovery. Since 2008, they have relied on robust, data-driven software to manage portfolios and deliver tailored financial services.

The Challenge: The Code Review Bottleneck

As Bayview’s software engineering needs grew, their manual code review process became a constraint. Managing multiple internal tools and client integrations meant that senior engineers were spending disproportionate time reviewing code rather than building features.

  • Slow Feedback Loops: Developers often waited days for feedback on Pull Requests (PRs).
  • Inconsistent Quality: Review rigor varied depending on which engineer was available.
  • Security Risks: Manual reviews occasionally missed subtle security vulnerabilities or best-practice violations.

The Solution

Bayview engaged AllCode to implement an automated “AI Pair Programmer” that could review code 24/7. AllCode architected a solution that integrates directly into the developer’s native workflow on GitHub.

Technical Architecture The solution leverages AWS Bedrock to power an intelligent agent that acts as a first-pass reviewer on every single Pull Request.

  1. Event Trigger: A GitHub Webhook triggers the workflow whenever a PR is opened or updated.
  2. Analysis Engine: AWS Lambda receives the payload and invokes Amazon Bedrock, utilizing the Claude 3.5 Haiku model for its balance of speed and reasoning capability.
  3. Contextual Memory: The agent utilizes Amazon S3 and DynamoDB to store context, allowing it to “remember” previous reviews and avoid repetitive feedback.
  4. Feedback Loop: The agent posts comments directly to GitHub, flagging security risks, code style issues, and performance improvements.
  5. Guardrails & Security: To protect intellectual property, AllCode implemented Guardrails for Amazon Bedrock to ensure no PII is logged and that the model strictly adheres to code analysis without hallucinating non-existent libraries or syntax.

Why AllCode for DevOps Automation?

Bayview required a solution that integrated with their existing CI/CD pipelines without disrupting developer habits. AllCode’s “Reference Implementation” for AI Agents allowed for a rapid deployment that was customized specifically to Bayview’s coding standards.

The Results

Bayview Solutions has redefined its development lifecycle, moving from a bottlenecked manual process to an AI-accelerated workflow. 

Key Metrics

  • Cycle Time: 45% reduction in time from PR creation to merge.
  • Bug Detection: 32% increase in issues caught prior to QA/Testing.
  • PR Review Time: Reduced Pull REquest Reviews from hours to minutes.

Impact Summary

  • Instant Feedback: Developers receive detailed code analysis in seconds, not days.
  • Standardized Quality: The AI enforces a consistent baseline of quality and security across all repositories.
  • Security Hardening: Early detection of potential vulnerabilities prevents bad code from ever reaching production.

Conclusion

Bayview’s implementation of an AWS-backed AI code review agent with AllCode’s guidance has redefined its development lifecycle. The solution serves as a robust foundation for continued innovation in software quality and demonstrates the power of combining cloud services with intelligent automation to drive business value.