Expert data and evaluation systems for domain-specific AI.
We help AI teams train, evaluate, and improve models and agents with expert-curated datasets, golden benchmarks, human feedback loops, and reliability testing.
Built for AI labs, vertical AI startups, and enterprises deploying high-stakes AI systems.
The infrastructure layer for model quality.
Four core capabilities that turn expert judgment into measurable model improvement.
Training Data Creation
High-quality SFT, preference, rejection, and domain-specific prompt-response datasets created and reviewed by trained experts.
Evaluation Benchmarks
Golden datasets and structured evaluation rubrics to measure factuality, reasoning, citation accuracy, hallucination, and task success.
Human Feedback Operations
Managed expert review workflows for RLHF-style feedback, rubric-based grading, reviewer calibration, and quality assurance.
Agent Reliability Testing
Workflow simulations, tool-use evaluation, edge-case testing, and failure taxonomies for enterprise AI agents.
Built for high-stakes verticals.
Trained reviewers and domain experts across the fields where AI quality matters most.
Models are only as reliable as the data and feedback systems behind them.
As AI products move from demos to production, teams need more than prompts. They need expert-reviewed data, reusable evaluation sets, human judgment loops, and clear quality metrics. We provide the infrastructure and operations layer to make domain-specific AI systems more accurate, reliable, and trusted.
A partner for serious AI teams.
AI Labs
Post-training data, expert feedback, preference datasets, and domain-specific evaluations.
AI Startups
Golden benchmarks, quality audits, and model improvement datasets for vertical AI products.
Enterprises
Reliability testing, hallucination audits, and human-in-the-loop evaluation for copilots and agents.
Building a domain-specific AI system?
Let's create the data, evaluation, and feedback systems that make it reliable.