Enterprise Services
Clinical AI evaluation,
measured by people who practise medicine.
Calibrated UK clinicians score your medical AI against expert consensus — every metric backed by confidence intervals and full safety coverage, ending in evidence you can take to procurement and regulators.Calibrated UK clinicians score your medical AI against expert consensus — every metric backed by confidence intervals, inter-rater agreement, and full safety-taxonomy coverage. From structured evaluation and red-teaming to RL environments and high-quality datasets, every engagement ends in evidence you can take to procurement, regulators, and the lab — a Reliability Report, or training data with the rigour to back it.
The data behind breakthroughs in Medical AI
Every leap in medical AI rests on judgement only practising clinicians can give.
Clinical AI Evaluation
Know exactly how much to trust your model.
Calibrated evaluators
UK-registered clinicians, statistically calibrated against expert consensus before they score a single output.
Stats on every metric
Confidence intervals and inter-rater agreement — Cohen's κ and Fleiss' κ — reported on every result.
Full safety coverage
Severity-weighted accuracy across all ten clinical failure-mode categories.
From triage to prescribing, we measure clinical accuracy, safety, and appropriateness — and tell you how reliable the verdict is.
Talk to our teamClinical AI Red-Teaming
Find the failures before patients do.
Clinician adversaries
Trained clinicians probe your system the way a real GP, pharmacist, or ED doctor would — not generic prompt attacks.
12-category taxonomy
Structured adversarial testing across our clinician-built taxonomy of medical AI failure modes.
Severity-weighted report
Clinical-impact analysis, mitigation steps per failure mode, and a re-test protocol to validate fixes.
Dangerous dosing, false reassurance, contraindication misses, hallucinated diagnoses — we surface the safety-critical failures and show you how to close them.
Talk to our teamClinical RL Environments
Reward signals grounded in clinical reality.
Real clinical workflows
Triage queues, prescribing flows, ED handovers, patient conversations — simulators built around how care actually happens.
Clinician-signed rewards
Reward functions with severity weights, signed off by the specialist who runs the workflow being simulated.
Auditable end-to-end
Docker images, documented APIs, deterministic seeds, and Phase 2-calibrated trajectory scoring on every run.
For RL, PPO, GRPO, and DPO training — interactive clinical environments where every reward your agent learns from is justifiable and reproducible.
Talk to our teamMedical AI Annotation
Expert labels you can build on.
Domain-expert labellers
Active GMC, NMC, GPhC, and HCPC registrants label your data — clinicians who live the context, not general annotators.
Quality-controlled
Gold-standard injection, drift detection, and attention checks hold consistency across the whole run.
Consensus + agreement
Multi-annotator consensus labels with full agreement statistics and per-annotator reliability scores.
Clinical NLP, medical chatbots, diagnostic models — get expert-labelled datasets with the audit trails to trust them for safety-critical work.
Talk to our teamHigh-Quality Datasets
A sovereign clinical dataset, built in Britain.
Sovereign methodology
Built and curated in the UK by registered clinicians — data and IP that stay onshore, under a documented, auditable process.
Calibrated provenance
Every datapoint carries the reliability of the calibrated clinician behind it, with agreement statistics and full version history.
Built to your spec
Bespoke scenario curation across specialties — preference pairs, gold sets, and evaluation corpora to your requirements.
The clinical judgement frontier models can't scrape — a high-quality, rights-clean dataset asset you can train on and defend to regulators.
Talk to our teamHealthcare AI Advisory
Clinical judgement on tap.
Practising specialists
Senior NHS clinicians across every major specialty — current, real-world knowledge, not textbook theory.
Design to deployment
Product design, workflow integration, safety frameworks, and regulatory strategy at any stage.
Flexible engagement
From a one-off consultation to a standing clinical advisory board, structured around your team.
Direct access to the clinical expertise you need to build medical AI that works in real clinical settings — and stays safe once it gets there.
Talk to our teamEvery engagement ends in evidence you can defend.
Two procurement-grade deliverables — the same statistical rigour behind both.
Reliability Report
A report on the system or its evaluators — the artifact you take to safety reviews, regulators, and procurement.
- PDF + JSON
- Per-metric confidence intervals
- Coverage across all 12 safety categories
- Full evaluator audit trail
Documented Dataset
The data itself — rights-clean and ready to train on, shipped with the provenance to defend it.
- Calibrated clinician provenance
- Inter-annotator agreement statistics
- Full version history
- Rights-clean, UK-onshore
What makes us different
Six reasons our clinical evaluation stands up to a regulator — not just another annotation vendor.
Clinical Experts
Every evaluator is a UK-registered healthcare professional — not a general annotator. They understand the clinical context because they work in it daily.
Statistical Rigour
Confidence intervals on every metric. Inter-annotator agreement. Proper scoring rules. You know exactly how much to trust the results.
Safety-First
Built on our 12-category clinical AI failure mode taxonomy. We test for the specific ways medical AI fails in practice.
Calibrated Evaluators
Every evaluator passes two-phase calibration before assessing your system. Their reliability is measured, not assumed.
Quality Control
Gold-standard injection, drift detection, attention checks, and justification analysis maintain quality throughout production.
Full Audit Trail
Complete records of every evaluation decision, justification, and quality metric — ready for regulatory review.
Clinical Experts
Every evaluator is a UK-registered healthcare professional — not a general annotator. They understand the clinical context because they work in it daily.
Statistical Rigour
Confidence intervals on every metric. Inter-annotator agreement. Proper scoring rules. You know exactly how much to trust the results.
Safety-First
Built on our 12-category clinical AI failure mode taxonomy. We test for the specific ways medical AI fails in practice.
Calibrated Evaluators
Every evaluator passes two-phase calibration before assessing your system. Their reliability is measured, not assumed.
Quality Control
Gold-standard injection, drift detection, attention checks, and justification analysis maintain quality throughout production.
Full Audit Trail
Complete records of every evaluation decision, justification, and quality metric — ready for regulatory review.
30-minute call. Get a quote.
Tell us what you’re building and what you need evaluated, red-teamed, annotated, or generated. We’ll come back with a fixed-price brief — no long enterprise procurement.
- Fixed-price quote
- No drawn-out procurement process
NDA available on request.
entertheloopClinicians powering AI alignment, training & safety.Verified against
entertheloopClinicians powering AI alignment, training & safety.