Platform
TestML is purpose-built around a single guarantee: that the LLM agents you ship into production behave exactly as specified, under adversarial conditions, across every dimension that matters. Since 2022, the platform has evaluated 340+ enterprise LLM pipelines across legal, financial, medical, and insurance verticals.
This is not generic benchmarking. The evaluation framework runs as a continuous pipeline, not a one-time pass-fail gate.
The 20+ Dimension Evaluation Pipeline
Most teams measure three things at launch: accuracy, latency, and cost. Those three tell you whether the agent works. They don't tell you whether it stays within regulatory boundaries, degrades under prompt manipulation, or drifts after a model update.
TestML runs 20+ evaluation dimensions in a single pipeline. Accuracy and latency sit alongside safety scoring, compliance boundary checking, hallucination rate, cost per inference, and adversarial resilience. Every deployment generates full-spectrum evidence across all dimensions simultaneously. No cherry-picking.
The pipeline connects directly to your existing infrastructure. Bring your own model endpoints, agent frameworks, and evaluation datasets. TestML instruments the pipeline and returns structured results you can tie directly to deployment decisions.
Red-Teaming: Adversarial Tests Built for Your Threat Model
Generic red-team exercises test for known public jailbreaks. Enterprise deployments face a different threat surface: indirect prompt injection via retrieval-augmented context, hallucination exploits in multi-step agent chains, and regulatory boundary violations that look like normal outputs until an auditor finds them.
David Park, TestML's Head of Evaluation Science, built adversarial test suites for three Fortune 500 LLM rollouts before joining. The red-team methodology targets your specific deployment context. Median time from environment access to a written report is 72 hours.
Sarah Moran, Head of LLM Platforms at a European investment bank, ran TestML's adversarial suite before go-live. Three regulatory boundary violations surfaced. None reached production.
Domain-Specific Evaluation Suites
Legal contract review agents face different failure modes than insurance claims processors. A medical triage assistant requires safety thresholds that a financial advisory chatbot does not.
Pre-built evaluation suites cover legal, medical, financial, and insurance workflows. Each suite embeds criteria drawn from real operational risk registers and regulatory frameworks: GDPR, HIPAA, SOC 2 Type 2, and sector-specific requirements. These are not generic benchmarks relabelled for verticals.
Aditi Verma, Director of AI at a multinational law firm, deployed TestML's legal evaluation suite. Post-deployment review cycles fell by half.
Continuous Monitoring and Drift Detection
A model that passes evaluation at deployment can fail three months later. Providers update base models. Retrieval corpora shift. User queries drift outside the distribution the agent was tested on.
Ewa Kowalska, TestML's Lead ML Engineer, built the production monitoring system around automated regression testing and statistical drift detection. The system runs evaluation probes continuously, compares results against your deployment baseline, and alerts when scores cross defined thresholds. Catch silent degradation before it becomes a compliance incident or a customer failure.
You define the thresholds. TestML watches the pipeline.
How Teams Use the Platform
A typical engagement starts with a technical review scoped to your existing LLM infrastructure. The evaluation pipeline is instrumented within days. Red-team findings arrive within 72 hours. Domain-specific suites run in parallel.
From that baseline, continuous monitoring takes over. Monthly reports give engineering and compliance teams a single source of truth on production model behaviour. That record matters when regulators ask questions.
Book a technical review to scope your evaluation requirements with the TestML team directly.
Stop guessing. Start measuring what matters in production.