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Recruit the right humans
We define the tester profile and match professional experience to your actual workflow.
Human expert testing for AI products
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.
Matched human practitionersIndependent evaluationDecision-ready findings
Example practitioner panelMatched to the product & workflow
Human reviewedTR
Technical recruiterAgency + in-house SaaS hiring
10 yrs
TA
Talent acquisition leadEnterprise recruiting operations
12 yrs
RO
Recruitment ops specialistATS, assessment & workflow design
8 yrs
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The key question is not whether an AI answer looks plausible. It is whether someone experienced enough to use it in the real workflow agrees — and where they do not.
AI quality is now a product problem, not just a model problem.
61%of organisations surveyed by Applause use human input to evaluate AI performance¹
30+UK employers engaged by the ICO on automated recruitment safeguards²
What we save you from
Build the product. We’ll build the practitioner test around it.
Running a useful external evaluation means finding scarce professionals, designing cases that expose failures, keeping judgement independent and turning it into product evidence.
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Design a credible test
We turn product claims into scenarios, edge cases and questions that expose disagreement.
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Separate signal from opinion
Practitioners test independently, so we can compare agreement, trust and failure severity.
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Synthesise the evidence
AI handles mechanical analysis while human judgement stays visible. You receive priorities and a retest plan.
Where it matters
Different industries fail in different professional realities.
We match the practitioners to the judgement your AI makes, not simply to a generic user demographic.
🧑💼
Recruiting & HR
Test: screening, ranking, sourcing and AI interviews.
Find: missed unconventional candidates, false positives and weak explanations.
📊
Finance & accounting
Test: bookkeeping, analysis and financial-agent workflows.
Find: plausible-but-wrong reasoning and missing professional context.
⚖️
Legal workflows
Test: research, drafting, triage and document review.
Find: authority gaps, overconfidence and escalation points.
🩺
Healthcare
Test: patient tools and clinician-support workflows.
Find: unsafe ambiguity, communication failures and escalation needs.
💬
Sales & support
Test: autonomous support and sales agents.
Find: wrong actions, weak handoffs and brand-risk responses.
🛠️
Technical & engineering
Test: coding, diagnostics and specialist copilots.
Find: technically plausible errors and workflow divergence.
Why independent practitioners?
Internal QA can prove the product works. It cannot fully prove professionals will trust it.
AI is probabilistic and context-sensitive. External domain judgement gives you a different signal from technical QA, automated evals or generic user research.
Your internal teamKnows the intended behaviour and happy paths extremely well — which can make some blind spots harder to see.
Generic user researchExcellent for usability, but may lack the domain depth to challenge professional judgement.
Automated evalsFast and repeatable, but mostly evaluate what you can already specify and measure.
ExpertTestRelevant professionals challenge the workflow itself, then AI helps turn their judgement into structured evidence.
Relevant to our first recruiting pilots
What a practitioner test can uncover.
Illustrative scenarios for recruiting AI — not claims about existing ExpertTest customers.
Strong on obvious candidates. Weak on unconventional ones.
Three recruiters independently review the same candidate set as the AI.
Potential finding: systematic under-ranking of strong candidates with non-standard career histories.
Natural conversation, weak follow-up.
Recruiters compare what the AI probes against what they would investigate themselves.
Potential finding: polished UX masks weak probing when answers are ambiguous or rehearsed.
More candidates, not always a better shortlist.
Technical recruiters assess relevance, seniority and transferability.
Potential finding: excellent recall but too many superficial matches, moving rather than removing recruiter work.
We will replace illustrative examples with anonymised or customer-approved case studies after founding pilots. We will not manufacture testimonials.
See what the finished deliverable looks like.
Open an illustrative recruiting-AI report with a practitioner panel, scores, findings, recommendations and retest plan.
The pilot
From product access to a decision-ready report.
One important workflow, the right humans and enough structure to produce actionable evidence.
1
Scope
Workflow, product claims, risks and practitioner profile.
2
Match
Three relevant humans are recruited and independently briefed.
3
Test
8–10 scenarios expose edge cases, trust gaps and disagreement.
4
Report
Findings, severity, priorities and recommended retests.
Free first step
See what we’d try to break before you pay us.
Give us a public product URL and one workflow. Where possible, we read the public product page to tailor an initial stress-test outline.
1Likely failure modes
2Edge cases worth testing
3Recommended human practitioner profile
4Draft evaluation protocol
Your initial stress-test outline
Recommended practitioner profile
Draft protocol
Common questions
What teams usually want to know first.
Why not recruit participants ourselves?
You can. ExpertTest saves the work of finding sufficiently relevant professionals, designing scenarios that expose judgement failures, keeping evaluations independent and synthesising disagreement into product evidence.
How is this different from automated evals?
Automated evals are essential. We focus on the harder layer: whether an experienced practitioner accepts the AI’s judgement in a realistic workflow, including edge cases you may not have encoded yet.
Are the practitioners actually human experts?
Yes. The paid pilot uses matched human practitioners with relevant professional experience. AI helps us design, structure and analyse the work; it does not replace the human judgement we sell.
Is this a compliance or bias audit?
No. The founding pilot is practitioner evaluation, not statutory certification, legal advice or a formal bias audit.
What do you need from us?
A product or test environment, one important workflow and enough context to understand the intended user and success criteria. We handle the rest.
Why only three practitioners?
The founding pilot is designed for depth and disagreement discovery, not population statistics. Larger panels can follow when the decision warrants them.
Founding pilot
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.
ExpertTest provides independent practitioner evaluation, not legal advice, compliance certification or a statutory bias audit.
£950fixed founding-pilot price
Discuss a pilot →¹ Applause, State of Digital Quality in Testing AI 2026. ² UK Information Commissioner’s Office, automated recruitment update, March 2026. These references support the need for human/domain-expert evaluation; they do not endorse ExpertTest.