Services for training, testing, and improving AI systems.
From expert-curated datasets to production-grade evaluation workflows, we help teams build AI systems that perform reliably in real-world domains.
Expert-Curated Training Data
We design and deliver datasets that help models learn domain-specific behavior, reasoning, tone, and accuracy.
- SFT datasets
- Prompt-response pairs
- Preference data
- Rejection data
- Edge-case prompts
- Domain-specific reasoning examples
Golden Evaluation Benchmarks
We create reusable benchmark sets and scoring rubrics so teams can measure model quality before and after every iteration.
- Factuality evaluation
- Citation accuracy
- Hallucination detection
- Reasoning quality
- RAG evaluation
- Task completion scoring
Human Feedback & Review Workflows
We manage structured human feedback loops that turn expert judgment into reliable model improvement signals.
- Expert review
- Rubric-based grading
- Reviewer calibration
- Multi-layer QA
- Audit trails
- Feedback taxonomies
Agent Reliability Testing
We test AI agents against real-world workflows, edge cases, and failure scenarios before they reach users.
- Workflow simulations
- Tool-call accuracy
- Policy compliance checks
- Failure analysis
- Regression test sets
- Production monitoring taxonomies
Domain Expert Network
Our workflows combine trained reviewers, domain experts, and QA systems to deliver high-confidence outputs.
- Finance analysts
- Legal reviewers
- Engineers
- Coders
- Language experts
- Healthcare and life sciences experts
Three ways teams start working with us.
AI Reliability Audit
Evaluate hallucinations, factuality, citation quality, and reasoning quality across a sample of model outputs.
Golden Dataset Build
Create a reusable domain-specific benchmark or training dataset for your product or model.
Ongoing Evaluation Program
Set up continuous human-in-the-loop evaluation and quality reporting for your AI system.