The visibility and improvement layer for agentic AI
We provide custom contextual evaluations, fine-tuning, and monitoring for agentic AI to work seamlessly in any workflow
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Challenges
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Unreliable Agent Behavior
Generic AI agents lack consistency, control, and predictable behavior in enterprise environments.
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Evaluation Gaps
Most agents are not tested against real-world edge cases or continuously evaluated after deployment.
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Trust & Compliance Risks
AI systems often operate as black boxes, creating trust and compliance concerns.
Solutions
Contextual evaluations built on real workflows
We design evaluations grounded in real tasks, decisions, and success criteria instead of generic benchmarks.
Expert level human judgement
Domain experts evaluate agent outputs to uncover reasoning gaps, edge-case failures, and risky behavior automated tests miss.
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Data-driven improvement loops
Evaluation results feed into targeted data generation, fine-tuning, and monitoring to continuously improve reliability.
How it works
1
Evaluation design
Define realistic scenarios and success criteria based on real workflows
2
Expert-calibrated human judgment
Domain experts evaluate agent outputs against desired outcomes
3
Failure mode analysis
See where and why agents break down, including edge cases and high-risk decisions
4
Improvement pathways
Evaluation results translate directly into targeted data, fine-tuning, and workflow changes
5
Continuous evaluation
Agents are re-evaluated over time to maintain reliability, alignment, and performance
