For specialists
Doctors, lawyers, and regulatory experts earn above-market rates for their expertise. Work is flexible, remote, and impactful—specialists shape AI systems that will affect their fields for decades.
The problem
AI models are deployed in high-stakes environments—healthcare, legal, finance—where errors are costly. The gap between model capability and real-world reliability remains significant.
Current benchmarks show AI systems performing at 15-45% on complex tasks where humans achieve 70-90%. More concerning, these gaps widen when models encounter local regulations, regional medical practices, or jurisdiction-specific legal frameworks.
The cost of catching errors post-deployment is 10-100x higher than preventing them during training. Yet most AI labs lack systematic access to the specialists who understand these nuances.
Our approach
Xase builds infrastructure to connect AI labs with verified local specialists. We focus on domains where expertise is non-negotiable: medical diagnosis, legal compliance, regulatory validation.
Our platform handles three core functions:
- Matching: Connect the right specialist with the right task based on credentials, location, and domain.
- Review: Structured workflows for expert annotation, validation, and feedback.
- Delivery: Curated datasets and evaluation reports ready for model improvement.
Two sides, one goal
For AI labs
Access verified experts who understand local context. Get high-signal feedback before deployment. Build training datasets that reflect real-world complexity.
Current focus
We are currently prioritizing three specialist categories where local knowledge is most critical:
Medical
Clinical validation
Legal
Compliance review
Regulatory
Local frameworks
Principles
Expertise first. We verify credentials and domain knowledge before specialists join our network. Quality of feedback matters more than quantity.
Fair compensation. Specialists should earn well for their expertise. We price work to reflect the value of specialized knowledge.
Production focus. We optimize for catching issues before they reach users, not just academic benchmarks.