CalEval | AI evaluation and reliability accelerator
Define, measure, monitor, and improve AI quality across development and production
Measured AI quality
AI systems can produce fluent responses while still being incorrect, unsupported, unsafe, or unsuitable for the task. CalEval turns AI quality into a defined and repeatable engineering process.
- Define use-case-specific quality, safety, and operational criteria
- Build golden test sets covering typical flows, edge cases, and known failures
- Evaluate prompts, models, retrieval, tools, and workflows against established baselines
- Monitor production behavior for drift, recurring failures, latency, cost, and safety
CalEval combines automated checks, LLM-based scoring, human review, and business metrics to support release decisions, production oversight, and continuous improvement.
Business value
- Release confidence:
Use shared criteria and regression baselines to support clearer AI release decisions. - Earlier issue detection:
Detect regressions, drifts, hallucinations, and threshold breaches earlier. - Quality accountability:
Give product, engineering, QA, and governance teams a common evidence base. - Quality-cost balance:
Consider quality, latency, token consumption, and model cost together when improving AI systems.
To Know More
About how we can align our expertise to your requirements, reach out to us.