LogiqReview AI
An AI code review assistant that gives structured, ranked feedback on pull requests.
Last updated
The Problem
Pull request reviews queue up behind the most senior engineers. Generic AI review bots help a little, but they often post long, unranked comments that teams learn to ignore.
I want review feedback that is short, ranked by severity, and tied to exact lines, so engineers act on it.
The Build
LogiqReview AI reads a pull request’s diff and the code around it, asks the model for findings in a fixed schema, and posts them back as a structured review. It is in active development, and I share progress in a build log.
[PLACEHOLDER: build log URL]
Stack
- [PLACEHOLDER: confirm stack]
- LLM structured output
- Pull request integration
Architecture
Trigger
- Pull request events
Application
- Diff and context collector
- Review with structured output schema
- Findings ranking and de-duplication
Output
- Structured review on the pull request
Infrastructure
- Model provider
Key decisions
The choices that shaped LogiqReview AI, and why I made them.
Structured findings, not free text
Why · Each finding has a severity, file, line and suggestion, so findings can be filtered, ranked, de-duplicated and tested.
Review the diff plus targeted context
Why · Sending only what changed, plus the code it touches, keeps comments relevant and cost predictable.
Suggest, never commit
Why · The assistant comments on the pull request. Engineers decide what changes, so ownership of the codebase stays with the team.
Screens
Pull request review summary Structured findings
What it proves
How this build maps to the AI Engineering modules I offer clients.
- AI workflow automation
- AI feedback runs inside an existing engineering workflow, on every pull request.
- LLM integration architecture
- A typed output schema between the model and the application.
- Evaluation and guardrails
- Structured findings make it possible to measure and test review quality.