Most teams trying to speed up code review with AI make the same mistake: they pick one tool and expect it to catch everything. It won't.

At Weave, every PR gets reviewed by 4 different AI tools before it reaches a human reviewer. This isn't overkill. It's the most efficient way we've found to maintain code quality while moving fast.

Here's what we learned about building an effective AI code review stack.

The Problem With Single-Tool Review

AI code review tools are powerful, but each one has blind spots. Some excel at catching syntax errors but miss logical flaws. Others identify edge cases but ignore style consistency. Some are great generalists but lack deep codebase context.

When you rely on a single tool, you're betting that its strengths perfectly align with your needs and that its weaknesses don't matter. That's rarely true.

The solution isn't finding the one perfect tool. It's combining multiple specialized tools that cover different aspects of code quality.

Our Four-Tool Stack

Here's how we structure our AI code review process:

1. Cursor Bugbot: Single-Purpose Bug Detection

Bugbot has one job: find bugs. It ignores style, patterns, and everything else that clutters typical code reviews. When Bugbot flags something, it's usually a legitimate issue.

We use this as the first scan. It's fast, focused, and catches the obvious logic errors before anything else runs.

2. Greptile: General Code Review

Greptile provides comprehensive feedback on logic, syntax, and potential improvements. It catches issues humans might miss when moving quickly and identifies edge cases that aren't immediately obvious.

The tool excels at spotting patterns across the codebase and flagging when new code doesn't match existing conventions.

3. Cubic: Redundant Coverage

At first glance, using both Greptile and Cubic seems redundant. They both provide general code review feedback. But we keep both because they occasionally catch different things.

As long as each tool catches at least one bug the other misses, the cost is worth it. If we see their coverage fully converge over time, we'll remove one. But for now, the overlap provides valuable redundancy.

4. Wispbit: Context-Aware Standards

Wispbit is different from the others. It learns from our past review comments and applies those patterns to new code. Over time, it gets better at understanding what matters specifically to our codebase.

This is crucial because every codebase has unique standards and conventions that generic tools can't know. Wispbit bridges that gap by studying our actual review history and enforcing our specific standards automatically.

The Results

Together, these four tools catch approximately 60% of bugs before human review. That's significant time savings for our engineering team.

But the real value isn't just in the bugs caught. It's in how these tools free up human reviewers to focus on what actually matters.

What Humans Still Need to Do

AI tools handle surface-level review effectively. They catch bugs, enforce patterns, and maintain consistency. But there's a critical gap they can't fill: architecture-level decisions.

No AI tool will look at a PR and say "this should be built differently from the ground up." They won't catch that you're creating long-term tech debt or that a feature doesn't align with the codebase's direction. They won't notice when a solution is technically correct but strategically wrong.

That requires human judgment.

This limitation is actually a feature, not a bug. The tools handle what they're good at (repetitive checks, pattern matching, bug detection), and humans handle what they're good at (architecture, design patterns, long-term codebase health).

When AI tools take care of the surface-level review, human reviewers can spend their time on the high-value work that actually impacts the product's future.

The Decision Framework

How do you know if you need multiple tools? Here's our thinking:

Add a new tool if it catches bugs the others miss. Each tool should bring unique value. If two tools provide identical coverage with no differentiation, remove one.

The cost of running multiple tools (both in actual cost and in processing time) should be less than the value of the bugs they catch. For us, catching one production bug typically costs more than running all four tools for months.

Don't add tools just because they exist. Add them when you have evidence they'll catch issues your current stack misses.

Making It Work

Running multiple AI review tools isn't just about installing them all. You need to think about how they fit into your workflow.

We run all four tools automatically on every PR. The results come back in separate comments, which makes it easy to see which tool caught which issue. This also helps us evaluate each tool's effectiveness over time.

The key is making the process automatic. If engineers have to manually trigger each tool, they won't do it consistently. Set it up once in your CI/CD pipeline and let it run every time.

The Bottom Line

The best code review setup isn't about finding the perfect AI tool. It's about using multiple imperfect ones that cover different gaps.

Single tools have blind spots. Multiple specialized tools catch more issues. And human reviewers focusing on architecture and long-term decisions catch what the tools miss.

That's how you build a code review process that actually works.

At Weave, we're constantly evaluating our tool stack. If you're building your own AI code review process, start with one tool to prove the value, then add more as you identify gaps. Don't overthink it, just start measuring what each tool catches and adjust from there.