Train Your Codex Models with Real Engineers
Expert code review. Debugging. Unit testing. Efficiency ranking—delivered by engineers who write production code.
Talk to Our Engineering TeamCode model quality depends on RLHF from annotators who can evaluate implementations with the depth of experienced developers.
From edge case detection to architectural feedback, every annotation shapes how your model reasons through problems
Code evaluation across Python, JavaScript, Java, C++, and Go demands engineers who understand language-specific performance characteristics, idiomatic patterns, and ecosystem constraints.
Code Review
Models generate functions that look correct but fail silently on edge cases. Production-quality review requires engineers who spot the difference between code that runs and code that works.
Debugging
Models produce errors. Meaningful annotation requires root cause diagnosis, corrected implementations, and precise explanations of failure—the reasoning data that drives model improvement.
Unit Testing
Code models demand validation against real-world conditions. Comprehensive test suites—those that verify functionality, catch edge cases, and expose failure modes.
Solution Ranking
Comparative evaluation of multiple solutions—ranked by efficiency, security, and readability with documented rationale.
How We Deliver
Expert Code Review
Line-by-line evaluation of model outputs for correctness, edge case handling, and adherence to best practices. Our engineers catch subtle bugs—off-by-one errors, race conditions, security vulnerabilities hiding in plain sight.
Systematic Debugging
When your model produces broken code, our team diagnoses the root cause, not just the symptom. We provide corrected implementations with step-by-step explanations of what went wrong and why the fix works.
Debugging annotations
Rigorous Unit Test Writing
Comprehensive test suites that verify code functionality across expected inputs, edge cases, and failure modes. Tests written by engineers who understand what actually breaks in production
Production-quality test cases
Precision Efficiency Ranking
Side-by-side comparison of alternative implementations, ranked on efficiency, security, readability, and maintainability. Our engineers evaluate time complexity, memory usage, and scalability—with detailed rationales for every ranking decision.
Expert Human Feedback for AI Code Generation
Every code annotator on our team holds a degree in Computer Science or Software Engineering. They've written production code, debugged real systems, and understand why one implementation outperforms another.
100% Degreed Engineers
No exceptions. Every annotator holds a CS or SE degree.
Production Experience
Built real software. Ship-quality standards.
Reasoning-First
Every annotation includes the why—not just labels.
Language Coverage
Contact us for specialized language requirements.
Code RLHF Services
Code Completion Models
Train models that suggest accurate, contextually appropriate completions.
Code Review Assistants
Build AI that catches bugs and suggests improvements like a senior engineer.
Automated Testing Tools
Develop models that generate meaningful test coverage.
Security Analyzers
Train models to identify vulnerabilities and suggest secure alternatives.
Ready to Upgrade Your Training Data?
Your code model is only as good as the feedback it learns from. Let's discuss how our engineering team can deliver the expert code review, debugging, unit testing, and efficiency ranking your RLHF pipeline needs.