Model Evaluation

Most teams run evaluation against a benchmark suite and call it done. The benchmark says 87% accuracy. The model ships. Three weeks later, a claims agent misclassifies a policy exception, or a contract summary omits a material clause. Benchmarks measure what the test set measures. Production measures everything else.

TestML was built on one observation: the gap between benchmark performance and real-world reliability is where enterprise deployments fail.

What 20+ Dimensions Actually Covers

Accuracy is one signal. A legal research agent that answers correctly 94% of the time but hallucinates citations 6% of the time is unusable in litigation support. A customer-facing insurance agent that scores well on accuracy but responds in 8 seconds per query creates a different class of failure.

TestML's evaluation pipeline measures across five families: accuracy and groundedness, safety and alignment, latency and reliability, cost and efficiency, and compliance conformance under GDPR, HIPAA, and SOC 2 Type 2. Every deployment gets evaluated across all five. No cherry-picking. Full-spectrum evidence on every deployment.

Red-Teaming and Adversarial Testing

David Park, Head of Evaluation Science, built adversarial test suites for three Fortune 500 LLM rollouts before joining TestML. His team's red-teaming targets the failure modes that standard evaluation never triggers: purpose-built prompt injection sequences, jailbreak vectors calibrated to your specific system prompt, and regulatory boundary probes tuned to your domain's threat model.

A financial services agent faces different adversarial risk than a medical summarisation agent. Generic red-teaming treats them identically. Ours does not.

Median time from environment access to written findings: 72 hours.

Sarah Moran, Head of LLM Platforms at a European investment bank, ran her team's agent stack through red-teaming before go-live. Three regulatory boundary violations were caught and corrected before a single external user touched the system.

Domain-Specific Evaluation Suites

Generic benchmarks do not know what a material adverse change clause is, or when an agent has misread one. TestML maintains pre-built evaluation suites for legal, medical, financial, and insurance workflows, developed with domain advisors and calibrated against real regulatory and operational risk.

Legal evaluation covers citation accuracy, contractual term extraction, and privilege boundary handling. Medical covers clinical summary fidelity, drug interaction flagging, and HIPAA output controls. Financial tests for FCA and MiFID II boundary conformance alongside standard accuracy and cost metrics.

Aditi Verma, Director of AI at a multinational law firm, found that domain-specific evaluation cut her post-deployment review cycles by half.

Continuous Monitoring in Production

Deployment is not the finish line. Ewa Kowalska, Lead ML Engineer, specialises in production drift detection and regression pipelines. When a model update ships or a prompt configuration shifts, automated regression testing surfaces the performance delta before it compounds into something costly.

Drift alerting fires when production distributions diverge from the evaluation baseline. You do not wait for a compliance incident to learn that something changed.

Across 340+ enterprise LLM pipelines evaluated since 2022, continuous monitoring has caught silent degradation that point-in-time benchmarks missed entirely.

Stop guessing. Book a technical review and start measuring what matters in production.