Your context
Repository structure, requirements and engineering knowledge can inform the review.
Customer-controlled AI code review
AI code review, under your control.
Run Ironwood in your environment. Use the AI providers you choose. Review code with the context of your codebase, requirements and engineering standards.
30 days2 developersFull Professional featuresNo credit card
1Code changeStart with the pull request or code being reviewed.
+2Engineering contextCodebase · requirements · engineering knowledge
→3Ironwood reviewFindings and evidence for an engineer to verify.
See Ironwood in action
One review. Two repositories. Evidence you can inspect.
Step through a completed Ironwood review. See how context, multiple AI providers and engineering impact stay connected to the review.
Ironwood AIInteractive product walkthrough
Run a code review● Worker running
Review scope
Select the code you want reviewed.
Ironwood can review repositories together without losing their individual context.
✓
book-lending-api-ghGitHub · main · d520026d
Selected✓
smart-ui-gap-analyzerGitHub · master · 4bd60bea
SelectedAuto replay · completed Ironwood review
Example: two-repository batch completed 10 September 2026. ROI values are estimates based on captured usage and configured engineering-cost assumptions, not provider invoice data.
Works with the tools your team already usesGitHubAzure DevOpsBitbucketOpenAIClaudeGeminiOllama
Why Ironwood
More than another AI opinion on a pull request.
A useful review needs more than the changed lines. Ironwood can review a change with the context of the wider codebase, your requirements and the engineering knowledge your team gives it. You choose the AI providers doing the analysis, and you can compare their findings instead of relying on a single model.
Your AI providers
Use the providers your organisation chooses instead of being locked into ours.
Your environment
Run Ironwood under your control and manage repository and AI provider credentials yourself.
Self-hosted by design
Your code review stays under your control.
Run Ironwood in infrastructure you control. Manage your own source control and AI provider credentials, choose the providers your reviews use, and keep the review workflow close to your engineering environment.
Explore self-hosted Ironwood →Multi-repository review
Review related repositories together.
A change does not always stop at one repository. Select several repositories, review each one in the same batch and keep the findings tied to the repository they came from.
See the review workflow →Reviewed togetherIronwood batch
Requirements & integrations
Review code against what your team is actually building.
Give Ironwood the context your team already has: repository structure, requirements, architecture guidance and engineering knowledge. Connect the source control and AI providers you already use.
Review contextCodebaseRequirementsEngineering knowledge
→Review engineIronwood
→Connected toolsGitHub · Azure DevOps · BitbucketOpenAI · Claude · Gemini · Ollama
Comparing AI code review tools?
Choose the operating model that fits your team.
If you are comparing Ironwood with a managed reviewer, start with the questions that change the decision: where the platform runs, who manages the AI credentials and how much control your team needs over review context.
Considering a CodeRabbit alternative? →Evaluate it with your own code
See what Ironwood finds before you decide to buy.
30 days · 2 developers · Full Professional capabilities · No credit card.