NextStack orchestrates AI builders, gates their output with multi-model review, and ships code that meets the standard of expert human review. The model-agnostic quality layer for AI-generated code.
"Because the AI that wrote the code shouldn't be the only one reviewing it."
We benchmarked 4 frontier models across 6 review methods. The result: different models catch different things. ns review routes each method to the best model for the job.
No competitor publishes head-to-head model comparison data. We do. Our routing decisions are backed by 600 controlled evaluations, not assumptions.
Each review method targets a specific class of defects. Model routing sends each method to the model that's empirically best at finding those defects.
Default on your first review — see the full pipeline in action. Configurable after. Code goes through three gates. Only clean code ships.
Same PR surface, different depth. CodeRabbit comments once with one model's opinion. ns review uses different models for different review types, then actually fixes the code and re-reviews until clean.
| Typical PR Bots | ns review | |
|---|---|---|
| Models | Single model | Multi-model with empirical routing |
| Output | Suggestions / comments | Actual fixes via fix-then-grade |
| Review types | Generic "review this PR" | 6 specialized methods, each optimized |
| Adaptability | Static prompts | Continuously adapts as models evolve |
| Benchmark data | None published | 600 evaluations, 4 models, 6 methods |
| Fix loop | No | Fix → re-review → repeat until clean |
Gate 1 is always free. Multi-model review costs what the API calls cost — no margin stacking.
CLI + Gate 1 are open source. Multi-model review + fix-then-grade require an API key.
One command. Multi-model review. Automatic fixes. Zero config.
Get Started →