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Your evals measure the wrong things.
Aggregate quality scores can obscure the small number of workflows and failure modes that actually determine product success.
Calibrate AI helps teams evaluate the reliability, failure modes, and real-world performance of AI products and agents before they scale.
AI systems often perform well on internal benchmarks and still fail in ways that matter to customers. The difficult part is not generating more test cases. It is determining whether your evaluation system predicts real-world behavior.
Aggregate quality scores can obscure the small number of workflows and failure modes that actually determine product success.
LLM-as-judge systems can create false confidence when rubrics, thresholds, disagreement, and human validation are poorly designed.
Tool calls, ambiguous requests, multi-step workflows, state, permissions, and unexpected context create failure modes that standard benchmarks miss.
A focused, independent assessment of your AI system's most important workflows, evaluation framework, and production risks.
We work with your team to identify critical user journeys, define meaningful success criteria, pressure-test your current evaluation approach, and surface the failures most likely to affect customers or the business.
Identify the user journeys and agent behaviors with the highest product and business risk.
Categorize where the system fails across reasoning, tool use, grounding, state, policy, and edge cases.
Assess test coverage, scoring methodology, metrics, thresholds, and the relationship to actual user outcomes.
Review LLM-as-judge prompts, rubrics, disagreement patterns, and human-validation strategy.
Build or refine test cases around representative and high-consequence scenarios.
Prioritized findings, remediation recommendations, and clear criteria for launch or expanded usage.
Engagements can begin with a focused assessment or extend into evaluation design, implementation, and ongoing reliability work.
Evaluate whether agents can consistently complete critical multi-step workflows, use tools correctly, preserve state, and recover from unexpected conditions.
Core capabilityDesign and validate grader prompts, scoring rubrics, thresholds, disagreement analysis, and human review protocols.
Evaluation infrastructureDevelop evaluation architecture that connects offline testing, production telemetry, human feedback, and business outcomes.
Systems designSurface hidden failure modes, adversarial conditions, risky edge cases, and high-severity scenarios before customers do.
Risk discoveryCreate hybrid evaluation workflows that combine expert judgment, human graders, and automated scoring where each is most reliable.
Hybrid evaluationWhen evaluation exposes gaps, we can help teams improve prompts, workflows, retrieval systems, agent architecture, and production reliability.
Selective implementationEvaluation is most useful when it helps a team make a decision: whether to launch, what to fix, where to invest, and what level of reliability is acceptable.
Clarify what the evaluation needs to tell the business or product team.
Identify the scenarios where system performance has the highest customer or operational consequence.
Use targeted tests, calibrated graders, and human review to understand where reliability breaks down.
Prioritize remediation, establish thresholds, and define the evaluation system needed for continued improvement.
Calibrate AI works with teams building real AI products—not hypothetical demos.
We focus on the difficult layer between model capability and product reliability: defining what good looks like, designing meaningful evaluations, diagnosing failure modes, and turning those findings into better systems.
Our work spans product evaluation, LLM-as-judge systems, agent workflows, human evaluation, AI quality measurement, and production-readiness strategy.
No. We typically work alongside product, engineering, research, or AI quality teams. The goal is to add independent perspective, identify blind spots, and accelerate development of a stronger evaluation system.
No. We can evaluate an existing system or help define the first meaningful evaluation framework for a product moving toward production.
Yes. Engagement scope can be designed around sandbox environments, representative traces, test datasets, demonstrations, or other controlled access appropriate to your security requirements.
Yes. Evaluation is the primary entry point, but we can selectively help teams implement recommendations across prompts, workflows, retrieval, agent architecture, graders, and evaluation infrastructure.
Scope depends on the number of workflows, system complexity, evaluation maturity, access requirements, and the business decision the team needs to make. We begin with a short discussion to determine the right level of evaluation.
Tell us what you're building and what your evaluation system needs to prove. We'll use the first conversation to understand the product, critical workflows, and whether an independent evaluation would be useful.