Your AI passed the demo. Will real professionals trust it?
We put your product in front of experienced human practitioners who actually do the job — recruiters, accountants, clinicians, lawyers, engineers and other specialists — and find the gaps automated tests and internal teams miss.
The important question is not whether the output looks plausible. It is whether someone experienced enough to use it in the real workflow agrees — and where they do not.
Build the product. We’ll build the independent practitioner test around it.
Running a good human evaluation internally is more work than it appears — especially when the right testers are busy professionals, not generic research participants.
Recruiting the right people
We define the tester profile, source appropriate practitioners and match experience to your actual customer/workflow.
Designing a credible test
We turn your product claims into scenarios, edge cases, rubrics and questions that expose disagreement rather than invite polite feedback.
Separating signal from opinion
Practitioners test independently. We compare agreement, confidence, trust and failure severity instead of handing you three unstructured interviews.
Synthesising the evidence
AI handles the mechanical analysis; human judgement stays visible. You receive priorities, evidence and a retest plan rather than hours of raw notes.
Different industries fail in different ways.
We match the practitioner to the decision your AI is making — not just to a demographic profile.
Recruiting & HR
Test: screening, ranking, sourcing and AI interviews.
Find: false positives, missed unconventional candidates, weak explanations and workflow trust gaps.
Finance & accounting
Test: bookkeeping, analysis and financial-agent workflows.
Find: plausible-but-wrong reasoning, missing context and when a professional would refuse to rely on the output.
Legal workflows
Test: research, drafting, triage and document review.
Find: authority gaps, missed nuance, overconfidence and escalation points.
Healthcare
Test: patient-facing tools and clinician-support workflows.
Find: unsafe ambiguity, missing context, communication failures and when expert escalation is essential.
Sales & support
Test: autonomous support, sales agents and account workflows.
Find: wrong actions, poor handoffs, brand-risk responses and customer-friction edge cases.
Technical & engineering
Test: coding, diagnostics and specialist copilots.
Find: technically plausible errors, incomplete reasoning and where expert workflows diverge from benchmark scores.
Internal QA can prove the product works. It cannot fully prove professionals will trust it.
AI is probabilistic, context-sensitive and increasingly agentic. The same product can behave differently across users, markets and edge cases. External domain judgement is a different signal from technical QA.
Why teams buy the test
Wrong AI actions can create churn, reputational damage, retraining work and delayed enterprise deals.
Resolve “it looks good to us” debates with structured external practitioner judgement.
A strong test can become product evidence for customer conversations, without pretending to be formal certification.
Resolved edge cases can become reusable scenarios for future releases and continuous evaluation.
What a practitioner test can uncover.
These are illustrative scenarios based on the kind of recruiting-AI products we are currently approaching — not claims about existing ExpertTest customers.
Strong on obvious candidates. Weak on unconventional ones.
Three experienced recruiters independently review the same candidate set as the AI screening product.
The interview sounds natural — but does it ask the right follow-up?
Recruiters compare the AI interviewer’s probing questions and final assessment against what they would investigate themselves.
More candidates does not always mean a better shortlist.
Technical recruiters assess candidate relevance, seniority, transferability and hidden false positives in AI-generated sourcing results.
We will replace these illustrative examples with anonymised or customer-approved real case studies as soon as founding pilots are completed. We will not manufacture testimonials or imply customers we have not yet served.
From product URL to a decision-ready report.
The process is deliberately narrow: one important workflow, the right humans, enough structure to produce actionable evidence.
Scope
We identify the workflow, claims, risks and practitioner profile.
Match
Three relevant human experts are recruited and independently briefed.
Test
8–10 realistic scenarios expose edge cases, trust gaps and disagreement.
Report
We synthesise findings, severity, priorities and recommended retests.
See what we’d try to break before you pay us.
Send us your product and one target workflow. We’ll create an initial stress-test outline so you can judge the quality of our thinking before commissioning a human practitioner test.
Let three experienced humans pressure-test one important AI workflow.
3 matched practitioners · 8–10 scenarios · independent scoring · expert-vs-AI disagreement analysis · failure modes · prioritised recommendations · executive report.
The founding price is intentionally simple while we validate the service and build our practitioner network.
¹ Applause, State of Digital Quality in Testing AI 2026. ² UK Information Commissioner’s Office, Recruitment Rewired / automated recruitment update, March 2026. These references support the need for human and domain-expert evaluation; they do not endorse ExpertTest.