Halodoc
Read post

Automating Code Reviews with AI: How Halodoc Scaled Quality Across the Full Stack

Halodoc built an AI-powered code review system embedded in their Jenkins Global Library CI/CD pipeline, reducing Merge Request feedback time from 30–45 minutes to 1–2 minutes. The system uses two LLMs — Gemini CLI for fast diff-based reviews and Claude Sonnet via AWS Bedrock for deeper contextual analysis — with stack-specific prompts covering Java, Angular, Swift, Kotlin, SRE, and Data Engineering repositories. A Comment Lifecycle Manager prevents duplicate discussions and auto-resolves fixed findings. Over six months, the platform processed 58,811 review invocations (~9,800/month) at roughly $1,600–$1,700/month in inference costs, with 85% of findings accepted by developers. Key lessons: prompt engineering and guardrails matter more than model selection, and lifecycle management is essential for developer trust.

    #cicd#ai-coding#prompt-engineering#gitlab#jenkins
Jul 10•14m read time•From blogs.halodoc.io
Post cover image
Table of contents
Prerequisites for Adopting AI Code Review in ProductionExecution ControlReview QualityComment LifecycleCross-Stack ConsistencySecurity and CredentialsSolution ArchitectureModel Selection and Trade-offsCost-Benefit AnalysisLessons LearnedConclusionJoin usAbout Halodoc
990 Impressions
Halodoc's image
Halodoc

HaloDoc is a healthcare technology platform that offers telemedicine services, online pharmacy, and ...

61 Followers

•

678 Upvotes

Would you recommend this post?

Copy link
WhatsApp
Facebook
X
New Squad
  • © 2026 Daily Dev Ltd.
  • Guidelines
  • Explore
  • Tags
  • Sources
  • Squads
  • Leaderboard