Improve AI systems with real-world experts
We connect doctors, lawyers, regulatory specialists, and local experts to evaluate, correct, and improve AI systems before failures become expensive in production.
Doctors, lawyers, regulatory specialists, and local experts.
Review production tasks, identify failures, and create high-signal data.
Errors caught late are expensive. Expert review reduces risk before issues scale.
Production AI needs real-world expert review
The hardest AI failures happen in production, where local context, regulation, and domain expertise matter. We turn expert judgment into structured data that improves model performance.
Tasks are collected from real-world workflows where model quality, local context, and reliability matter.
Focused on production failures, not synthetic demos
Doctors, lawyers, regulatory specialists, and local experts evaluate outputs, identify risks, and apply corrections.
High-signal feedback from professionals with real-world context
Expert feedback is converted into evaluation sets, preference data, and curated training examples.
Built for fine-tuning, evals, and production monitoring
Models improve before mistakes become more expensive across users, workflows, and geographies.
Better performance, lower risk, stronger production reliability
Structured tasks for model assessment and regression tracking.
Curated examples for domain adaptation and instruction tuning.
Expert comparisons and ranked outputs for model improvement.
Specialist validation for healthcare, legal, regulatory, and country-specific workflows.
Structured failure signals for reliability, risk reduction, and release readiness.
The biggest challenge in AI is not just capability. It is performance and safety in production.
Models break when local context, regulation, and domain expertise are missing.
Expert review closes that gap before failures scale.
We help AI labs improve systems with the people who actually understand the problem in the real world.
The gap is not just model quality. It is production readiness.
Benchmarks show how far models still are from reliable real-world performance. Closing that gap requires expert review and better data.
General AI Assistant Benchmark
Web Agent Tasks
Computer Environment Tasks
Use cases
Expert review and curated datasets for teams building, evaluating, and improving AI systems.
Healthcare
Healthcare models fail when they lack clinical judgment, patient communication quality, and local medical context. Specialist review improves safety, clarity, and production performance.
What teams need
- Clinical review
- Triage validation
- Patient-safe outputs
- Local medical context
What improves
- Safer responses
- Better clinical reasoning
- Lower production risk
- More reliable patient guidance
Better data and better review systems for teams improving AI performance.
Talk with Xase about expert review and production QA.
Get in touch