Environments
Reinforcement learning environments built from real workflows, with verifiable rewards, so agents train on tasks that look like the actual job.
Largwit AI Labs
Data, environments and evaluations for frontier AI
Largwit Labs
We build the expert human data, training environments and evaluations that teach AI systems to do real work, and show whether they can.
Evaluations
Evaluations scored against expert rubrics that measure how agents perform in production, not just on public benchmarks.
Expert Data
Supervised, preference and reasoning data written and reviewed by vetted experts in security, law, finance, medicine and software.
Human expertise, turned into training signal for AI that works in the real world
Research
We publish the benchmarks and methods behind our environments and evaluations.
Agentic security benchmark
How well AI agents investigate alerts, triage incidents and write detections inside a live, instrumented environment.
- Containment success
- Time to resolution
- Unsafe actions
Long-horizon tool use
Multi-step tasks across browsers, terminals and APIs, scored on whether the work actually got done.
- Task completion
- Steps to success
- Recovery from errors
Rubric reliability
How closely LLM judges agree with expert graders across domains, and where they diverge.
- Expert agreement
- Judge bias by domain
- Rubric coverage
Expertise is the scarce input in AI
Get in touch
Tell us what you're training or evaluating, or what you're an expert in. We reply within two business days.