Make enterprise AI measurably reliable
CalEval is Calsoft’s AI evaluation and reliability accelerator for measuring, monitoring, and improving LLM, RAG, copilot, and agentic systems.
Establishing quality and reliability for AI-systems
CalEval is Calsoft’s AI evaluation and reliability accelerator for measuring, monitoring, and improving LLM, RAG, copilot, and agentic systems across development and production. It provides the quality, reliability, and observability layer through evaluation criteria, test datasets, automated pipelines, regression checks, production monitoring, and continuous feedback. It helps customers build safer, more reliable, measurable AI systems.
Why CalEval? Can you trust your AI in production?
Unmeasured quality
01Response quality can’t be measured consistently, and manual review doesn’t scale.
Silent regressions
02Model or prompt updates ship without validation and introduce undetected issues.
Delayed detection
03Problems surface through user feedback or escalation only after they cause impact.
No proof of reliability
04No consistent measurement framework to demonstrate reliability or explain behavior
Want to know exactly how your AI behaves in production?
The real gap
AI drives real decisions, but errors often surface only after the impact, hence users stop trusting it.
What CalEval delivers
CalEval addresses these gaps by introducing evaluation as a continuous and integrated process within the AI lifecycle. The service establishes a coordinated evaluation framework.
CalEval enables:
Evaluation strategy & metrics
turn business expectations into measurable criteria: accuracy, groundedness, policy adherence, latency, and cost.
Golden datasets
curated from your production queries, validated responses, and edge cases as the evaluation baseline.
Automated validation (LLM-as-a-Judge)
repeatable, at-scale scoring that reduces reliance on manual review.
Offline evaluation pipelines
validate every model, prompt, or retrieval change against baselines before release.
Production monitoring
sample and score live responses to catch drift, hallucinations, and inconsistencies early.
Observability & reporting
dashboards and metrics that make reliability visible and auditable.
Retrieval evaluation
verify outputs are grounded in relevant, accurate source data.
Inside CalEval: AI evaluation & reliability layer
Evaluation is what converts AI from a demo into a reliable enterprise system. CalEval brings four layers together into one continuous framework.
Layer 01
Evaluation layer
Golden datasets · Prompt/model comparison · RAG evaluation
Layer 02
Reliability layer
Hallucination detection · Tool-call correctness · Regression testing
Layer 03
Observation layer
Drift monitoring · Cost & latency tracking · Human feedback loops
Layer 04
Continuous improvement
Production replay testing · Policy compliance scoring · Feedback-driven optimization
Business Impact
Typical improvements organizations observe with structured evaluation:
Impact area
What improves
Typical impact
Release stability
Validation before deployment reduces regression risk during model and prompt updates.
Up to 60% lower change-failure rates
Release stability
Validation before deployment reduces regression risk during model and prompt updates.
Typical impact
Up to 60% lower change-failure rates
Regression control
Regression testing catches performance decline before release.
30–60% less release-related instability
Regression control
Regression testing catches performance decline before release.
Typical impact
30–60% less release-related instability
Issue detection
Continuous monitoring detects drift, hallucinations, and inconsistencies earlier.
75–90% faster detection (weeks → days)
Issue detection
Continuous monitoring detects drift, hallucinations, and inconsistencies earlier.
Typical impact
75–90% faster detection (weeks → days)
Cost efficiency
Visibility into token usage, latency, and model behavior supports optimization.
15–25% cost-efficiency improvement
Cost efficiency
Visibility into token usage, latency, and model behavior supports optimization.
Typical impact
15–25% cost-efficiency improvement
Operational risk
Early identification of quality issues reduces incorrect outputs in production.
Lower customer impact and escalation
Operational risk
Early identification of quality issues reduces incorrect outputs in production.
Typical impact
Lower customer impact and escalation
Decision confidence
Measurable performance enables informed model and release decisions.
Confident deploy and scale decisions
Decision confidence
Measurable performance enables informed model and release decisions.
Typical impact
Confident deploy and scale decisions
See these results in a real engagement:
How we engage
A service-led, phased, low-risk path from use case to production:
Discovery
Identify AI use cases, risk exposure, quality expectations, and what “good” looks like.
Design
Define metrics and thresholds and build the golden dataset from your data and policies.
Validate
Stand up offline evaluation and LLM-as-a-Judge scoring to validate changes before release.
Productionize
Instrument production monitoring, retrieval checks, dashboards, and alerts.
Scale & optimize
Expand coverage across systems, tune for cost and latency, and feed results into continuous improvement.
Discovery
Identify AI use cases, risk exposure, quality expectations, and what “good” looks like.
Design
Define metrics and thresholds and build the golden dataset from your data and policies.
Validate
Stand up offline evaluation and LLM-as-a-Judge scoring to validate changes before release.
Productionize
Instrument production monitoring, retrieval checks, dashboards, and alerts.
Scale & optimize
Expand coverage across systems, tune for cost and latency, and feed results into continuous improvement.
