Methodology

The gap between a working prototype and a trustworthy production agent is wider than most teams expect. It is not a matter of prompt iteration or one more benchmark run. Closing it requires a structured, repeatable process for measuring everything that can fail in a real enterprise environment.

TestML's evaluation methodology was built by practitioners who shipped LLM systems at scale before they built the tooling. James Callahan, co-founder and CEO, spent fifteen years deploying ML systems at tier-1 financial institutions. Niamh O'Sullivan, co-founder and CTO, came from AI safety research before moving into enterprise evaluation architecture. The framework reflects what goes wrong in production, not what looks good in a demo.

Full-Spectrum Evidence, Not Cherry-Picked Metrics

Most teams evaluate on three or four metrics: accuracy on a held-out test set, maybe latency, possibly a quick hallucination check. That is not an evaluation. That is confirmation bias with extra steps.

TestML covers 20+ evaluation dimensions in a single pipeline: accuracy, safety, latency, cost, compliance, factual grounding, output consistency, instruction adherence, and more. Every dimension is measured on every deployment. There is no selecting convenient metrics after the fact.

David Park, our Head of Evaluation Science, has built adversarial test suites for three Fortune 500 LLM rollouts. His team designs evaluation criteria around what actually matters in each vertical: what a compliance failure looks like in a claims workflow is not the same as what it looks like in a legal document summarisation pipeline.

Red-Teaming: Proprietary Adversarial Testing

Standard evaluation catches average-case failure. Red-teaming finds the edge cases that become incidents. Our red-team protocol targets your specific enterprise threat model, not a generic checklist.

Within 72 hours of environment access, clients receive a written red-team report. The protocol covers prompt injection, jailbreak attempts targeted at your agent's system prompt, hallucination exploit paths, and regulatory boundary violations. Sarah Moran, Head of LLM Platforms at a European investment bank, found three regulatory boundary violations in pre-production that would have been live risks on day one.

Domain-Specific Evaluation Suites

Generic benchmarks do not reflect the risk profile of legal contract review, medical triage assistance, or insurance claims processing. Each domain has different failure modes, different regulatory obligations, and different tolerances for false positives.

TestML maintains pre-built evaluation suites for legal, medical, financial, and insurance workflows. The criteria are grounded in real regulatory and operational risk: GDPR Article 22 automated decision obligations, HIPAA data minimisation requirements, FCA suitability standards. Aditi Verma, Director of AI at a multinational law firm, cut post-deployment review cycles by half after switching from generic benchmarks to the domain-specific legal evaluation suite.

Continuous Monitoring After Go-Live

Evaluation does not end at deployment. LLMs drift. Upstream model updates, prompt changes, and shifting data distributions can all degrade agent performance without a single code change on your side.

Ewa Kowalska, our Lead ML Engineer, built the drift detection and regression pipeline that monitors production agents continuously. Automated alerts fire on statistical deviations from baseline performance, catching silent degradation before it becomes a compliance incident. Across 340+ enterprise LLM pipelines evaluated, the pattern is consistent: the teams that catch drift early are the ones that deployed with proper baseline documentation and continuous monitoring in place from day one.

That is the standard we hold every engagement to. If you want full-spectrum evidence on your next deployment, start with a technical review.