AI coding tools like Curor and Codex are transforming software development. By running locally or on a remote VM, they are able to build and test code going from idea to feature in seconds. As foundational models improve and context windows extend, we expect these agents to take on tasks that are increasingly complex. One major gap that remains, even with the base models, is transferring the institutional engineering knowledge into every line of code. While agents can produce workable code, they operate more like contractors and less like team members. Agents aren’t formally onboarded, they are not bound to security and compliance meetings, and they don’t participate in architecture reviews. The result is that new AI-generated code is inherently inconsistent with the standards and conventions of your organization. Human code review was supposed to be the answer, but with more and more code produced it is obvious human reviewers alone can’t scale facing the growth in new PRs.
At Baz, we’re building the infrastructure for Code Intelligence: context, memory & agents that doesn’t just understand code, but enables purposeful utilization of coding agents. That starts with reviews. We’re flipping the model by learning from the best signal you already have: your team's actual code review conversations. This approach not only avoids the pain of brittle, hand-crafted rules but also creates reviewers that evolve naturally as your engineering practices grow.
Introducing Custom Reviewers, powered by your team’s own pull request history. Paired with the Prompt Playground, this lets you encode, test, and evolve your team’s engineering brain into a system of principles that AI can actually use.
Here’s what’s now available in Baz AI Code Review:
- Custom Reviewers trained on your team's real code review history
- The Prompt Playground to edit, test, and tune AI reviewer behavior
Watch it in action:
AI Code Review Built on Your Team’s Knowledge
Custom Reviewers: Agents that enforce your standards
Unlike static markdown configuration files Baz auto-generates Custom Reviewers by analyzing past pull request conversations. These AI agents reflect the real conventions your team has already enforced-like preferring composition over inheritance or caching instead of recalculating. Each reviewer comes with linked examples and source PR comments so you know exactly why it exists.
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Admins can activate a reviewer immediately or customize it in the Playground before rollout. It’s like scaling your best reviewer across every repo without the burnout.
Baz converts your past pull request discussions into a Discussion Memory which pulls review history to find repeated themes, best practices, and hard-won decisions. These memories become the foundation for Custom Reviewers, giving your team a scalable and consistent method to embed its engineering principles on every PR. Like your team’s engineering brain, codified.
Prompt Playground: Refine and Test Reviewers Before They Go Live
Want to tweak the tone of a reviewer? Add a new example? Or make it less picky? Baz’s Prompt Playground lets you fully test any Reviewer changes in a sandbox:
- Edit prompts directly or through natural language suggestions
- Test changes on real CRs before deploying
- Simulate how updated reviewers will behave, then activate when ready
It’s like a console for reviewer behavior-ideal for hands-on tech leads or org admins managing larger teams.
This is the Future of AI Code Intelligence
Other AI code review tools rely on generic best practices or rigid rules but Baz’s Custom Reviewers learn from your codebase, adapt to your team’s decisions, and evolve alongside your standards. We are building infrastructure for intelligent, domains-specific code quality.
→ Try Baz Reviewer today and start turning your team’s knowledge into action.
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