AI code review that reasons out loud.
iReview analyzes every pull request on GitHub and Azure DevOps, flagging bugs, security issues, and style problems with full codebase context. Every review streams its reasoning in real time, and a second AI pass validates each finding before anything is posted.
Review tiers, quotas, and role limits reflect iReview's default configuration.
How a pull request moves through iReview
From the moment a PR is opened to the moment comments land, a review passes through three stages, each one logged and auditable on its own.
Ingest & context
Paste a PR URL from GitHub, GitHub Enterprise Server, or Azure DevOps, or let a webhook trigger the review automatically.
Test files, lock files, generated code, and binaries are excluded automatically across 35+ pattern-based filters.
Changed files are chunked at AST boundaries and embedded into a per-project vector index for semantic search.
Automatic detection across TypeScript, Python, Java, Go, C#, and more, each with its own review guidelines.
Analyze & validate
Relevant code from the existing codebase and callee function signatures are injected into the review, reducing hallucinated fixes.
The AI drafts findings by severity, critical, high, medium, or low, each with a location and a suggested fix.
A second AI pass validates line numbers, checks that fixes are executable, and filters out low-confidence findings.
Findings are checked against existing PR threads and prior reviews, so settled discussions are never re-raised.
Post & track
Approved comments are posted directly to the PR thread on GitHub or Azure DevOps.
The AI's reasoning is streamed live and stored for replay, so nothing about a review is a black box.
Every review records tokens used, model, and tier, rolled up by organization, project, and user.
Results are scoped by a five-level role hierarchy, from viewer to organization owner.
A reviewer you can talk back to.
Reply to any AI comment directly in the PR thread. iReview answers with the same codebase context it used for the original review, and every reply stays scoped to that project, never leaking context across repositories.
Ask why something was flagged and get the reasoning, with code examples from the same repository.
Ask how to resolve an issue and get a suggested refactor that matches existing codebase conventions.
Ask if a pattern appears elsewhere in the codebase and get a list of similar usages.
Each reply carries the thread history, so the conversation builds instead of resetting.
Governance teams read this part first.
iReview is built to satisfy the people who own access, spend, and compliance, not just the engineers using it day to day.
- A five-level role hierarchy, from viewer to organization owner, resolved per organization and per project.
- Every organization is fully isolated: no data, code, or search results ever cross tenant boundaries.
- Webhook events are HMAC-signed and validated before a review is ever queued.
- Every review records token usage, model, and cost, rolled up by organization, project, and user for chargeback.
- Admin impersonation, deletions, and role changes are all written to an audit trail.
What changes when every PR gets reviewed automatically?
Manual review depends on who has time. Here is what changes when the first pass is automatic.
| Area | Manual review | iReview |
|---|---|---|
| Coverage | Depends on reviewer bandwidth | Every file in every pull request, automatically |
| Consistency | Standards vary by reviewer | The same language-specific guidelines every time |
| Turnaround | Hours to days waiting on a reviewer | 30 seconds to 10 minutes, depending on tier |
| Transparency | No visibility into a reviewer's reasoning | Every review streams its reasoning live |
Ready to see it on your own pull requests?
Tell us which VCS platform you use and what your review process looks like today, and we will set up a walkthrough on a real repository.
contact@srivasistatech.com