Evaluation frameworks
Golden datasets curated with your domain experts, task-appropriate metrics, LLM-as-judge scoring calibrated against human raters, and thresholds agreed with the business owner before launch.
Engineering & Delivery
Prove it works. Then keep proving it.
Traditional QA asks whether the output is correct. AI QA has to ask whether it is correct enough, often enough, fairly enough, and still correct after last night's model update. We build the harness that answers all four.
Threshold, not pass/failCaught before users are
A deterministic system passes or fails a test. A probabilistic system has a distribution — and it moves. Every prompt edit, retrieval change, model upgrade and data drift shifts it, usually silently, usually in production.
Why AI testing is a different discipline
Why AI testing is a different discipline
Traditional software QAAI system assurance
Expected resultOne correct answerA quality distribution against a threshold
Test dataFixed casesGolden datasets, adversarial sets, live sampling
Regression riskCode changesPrompt, model, retrieval, data and provider changes
Failure modeCrash or wrong outputPlausible wrong output, bias, leakage, unsafe action
Coverage meansCode paths exercisedScenario, demographic and adversarial coverage
When testing endsAt releaseNever — monitoring is the test
What we build and run
What we build and run
Regression suites
Automated evaluation gates in CI so no prompt, model or retrieval change reaches users without a measured comparison against the current baseline.
Red-teaming & adversarial testing
Prompt injection, jailbreak, data exfiltration, tool misuse and unsafe-action testing — run against agentic systems where the blast radius is real actions, not just text.
Bias & fairness testing
Demographic performance analysis, disparate impact measurement and documentation that satisfies both internal ethics review and external regulatory expectations.
Robustness & drift monitoring
Input distribution monitoring, output quality sampling, groundedness and hallucination rate tracking, with alert thresholds and defined revalidation triggers.
Assurance evidence packs
Test results, coverage, decisions and sign-offs assembled into the artefacts an internal auditor, a regulator or an enterprise customer will ask to see.
The assurance lifecycle
The assurance lifecycle
| Stage | What we test | Gate |
|---|---|---|
| Design | Risk classification, failure mode analysis, oversight design | Testable acceptance criteria agreed |
| Build | Component evals, retrieval quality, prompt regression | Baseline established and versioned |
| Pre-release | End-to-end scenarios, red-team, bias, performance and cost | Threshold met and signed off |
| Release | Canary and shadow comparison against the human or incumbent baseline | Rollback criteria armed |
| Operate | Drift, quality sampling, incident review, periodic revalidation | Evidence pack current |
Testing agentic systems
Testing agentic systems
When a system takes actions, testing has to cover trajectories rather than outputs: did the agent choose a reasonable plan, use the right tool, stay inside its authority, recover from a failed step, and escalate when it should have.
We build trajectory-level evaluation and replay tooling so an agent's behaviour can be examined step by step — before release and after any incident. Where clients build through our Agentic AI & Orchestration practice, this tooling ships with the platform.
Why Adroitent
Why Adroitent
A testing heritage, not a testing pivot
Two decades of enterprise QA and validation across regulated industries, extended into AI rather than invented for it.
Independent or embedded
We run assurance as an independent function against systems built by others, or embed it inside our own delivery pods. The evaluation standard is the same either way.
Governance alignment
Test evidence maps directly onto ISO/IEC 42001 controls and EU AI Act documentation duties, so assurance work feeds the management system instead of duplicating it.
CMMI Level 3 rigour
Appraised process discipline in traceability, defect management and review — applied to probabilistic systems with the metrics adjusted accordingly.
5
assurance lifecycle stages
100%
of releases gated by regression evals
CMMI L3
appraised process maturity
ISO 42001
aligned evidence output
Frequently asked
Frequently asked
Can you test AI systems we did not build?
Yes, and it is a common engagement. We start with an assurance assessment covering evaluation coverage, observability and risk exposure, then build the missing harness before recommending any change to the system itself.
How large does a golden dataset need to be?
Smaller than most teams expect, if it is curated well. A few hundred well-chosen cases with domain-expert labels usually outperforms thousands of scraped examples. Coverage of edge and adversarial cases matters more than volume.
How does this relate to your AI governance service?
Governance defines the controls and the accountability; testing produces the evidence those controls demand. Clients often buy them together, but each stands alone.
ISO 42001:2023 Certified
ISO 9001:2015 Certified
ISO 27001:2013 Certified
SEI CMMI Level 3 Appraised
Agility. Delivered.
Ready to move on AI Testing & Governance?
Talk to an Adroitent AI lead. We will come with a point of view on your estate, not a generic capability deck.