Verify real work, across teams.
For frontier model labs, vertical AI companies, and enterprise teams.
For frontier model teams
Practice the work models still struggle with.
Build task distributions around real workflows, with verifiable outcomes, process constraints, and adversarial cases.
Separate held-out evaluation instances from training demonstrations. Compare models under the same configuration to identify where reliability improves.
- Dedicated task environments
- Parameterized instance generation
- Trajectories and process supervision data
For vertical AI companies
Reliable grading for domain-specific reinforcement fine-tuning.
Reinforcement fine-tuning needs to recognize when work is truly complete. Translating domain constraints, evidence, and exceptions into grading rules is central to verifier design.
Start with explicit business rules, replay difficult cases, and retain strategies that exploit gaps in the checks.
- Integrable reward functions
- Domain invariant libraries
- Independent evaluation task sets
For enterprise teams
Rehearse before connecting to real operations.
Run agents in simulated enterprise software and inspect every call, state change, and business-rule violation.
Use private task sets to cover important exceptions and review complete action trajectories before granting production access.
- Private simulation environments
- Complete process records
- Pre-deployment evaluation reports