Prompt tweaks feel better
A few hand-picked examples look improved, but there is no stable scoreboard.
Clinical review for source-grounded AI
Evaluation assets for healthcare AI, built from clinician-reviewed data
Temyrion delivers fixed-scope review packages for clinical AI workflows: reviewed examples, groundedness checks, safety flags, failure taxonomies, and JSON/CSV exports.
- Source-grounded clinical summaries
- Record Q&A and healthcare RAG
- Intake, extraction, and documentation QA
Illustrative project card
Reviewed clinical outputs package
50 examples - groundedness and safety review
Scope defined
Source bundle, review criteria, and export schema agreed
Clinician review
Safety flags and source-linked rationales documented
Structured output delivered
JSONL/CSV exports included
reviewed.jsonl criteria.md
Groundedness
Scored
Safety flags
Included
Illustrative format, not client evidence Scope sprint ->
Why evaluation breaks
Clinical AI quality needs more than model upgrades
A stronger general model can improve the baseline, but it still will not know your clinical workflow, source material, risk tolerance, or failure patterns. Without reviewed evaluation data, teams end up comparing vibes instead of measuring quality.
Temyrion turns clinician review into reusable eval assets, so teams can measure groundedness, safety, and workflow fit before they scale changes.
Without proper evaluation
General models miss local rules
The model is broad, but your workflow, source material, and safety expectations are specific.
Agent failures blur together
Retrieval, ranking, prompting, and generation fail in different ways, but the team sees only a bad answer.
With Temyrion
Reviewed examples
Clinician-reviewed records create a stable eval set for future changes.
Groundedness and safety checks
Outputs are scored against source evidence, clinical accuracy, completeness, and risk.
Regression signal
Failure taxonomies and structured exports make quality easier to compare release after release.
Practical first sprint
50 clinician-reviewed examples + review criteria
A fixed-scope sprint for source-grounded healthcare AI. We usually start with clinical summaries, but the same review process fits document Q&A, medical RAG, intake, extraction, and documentation QA.
Deliverables
- 50 clinician-reviewed examples
- Review criteria and groundedness checks
- Two-reviewer adjudication notes
- Failure taxonomy and safety flags
- JSONL/CSV exports for evals and regression testing
- Findings memo
Sample deliverable
View a sample reviewed delivery
Inspect a synthetic, no-PHI sample with source material, model output, groundedness and safety scores, source-linked rationale, adjudication note, and export-ready JSON/CSV structure.
Where the first sprint fits
Clinical summary evaluation
Evaluate whether generated clinical summaries stay faithful to source material, preserve important context, and avoid unsafe omissions, source conflicts, or unsupported claims.
Record Q&A and healthcare RAG
Create reviewed examples for grounded answers, citation quality, retrieval failures, and clinical-document reasoning.
Intake, extraction, and documentation QA
Turn clinical documents, forms, transcripts, or model outputs into reviewed records your team can use for QA, evals, and regression testing.
How it works
1
Define the scope
We align on the clinical task, data schema, review criteria, and delivery format before work starts.
2
Prepare review-ready work
We turn source materials and model outputs into structured review tasks so clinician reviewers can work quickly and consistently.
3
Run clinician review
Clinician reviewers score groundedness, clinical accuracy, completeness, safety, and usefulness, then document source-linked rationales.
4
Deliver usable outputs
You receive reviewed examples, scores against the agreed criteria, safety flags, adjudication notes, a failure taxonomy, JSONL/CSV exports, and a short findings memo.
Why we built Temyrion
Clinical judgment is the bottleneck
Healthcare AI breaks on nuance. Generic annotation workflows are not enough when the work depends on clinical source-grounding and safety review.
Reviewed data still has to be usable
If the schema, review criteria, or export format does not fit the team's evaluation workflow, the team still cannot use the evidence effectively.
Slow review cycles kill momentum
When every revision takes another round trip, clinical, product, and engineering teams end up waiting instead of learning.
Clinician review needs a better process
We structure the review work so clinician reviewers spend their time making judgments, not wrestling with documents, spreadsheets, and manual formatting.
Teams need a practical first step
Sometimes the right start is one workflow, one small reviewed package, and clear review criteria - not a heavyweight study.
What we provide
We provide clinician-reviewed eval assets for source-grounded healthcare AI. The goal is a clear, auditable package your team can inspect, reuse in regression testing, and build against.
Start the first conversation without PHI
No PHI required to start
We can start with synthetic, public, or de-identified materials. PHI or sensitive patient data is not required for the first scoping conversation.
Clear scope limits
Temyrion provides evaluation assets and clinical review evidence. We do not provide regulatory approval, legal advice, or authorization for autonomous clinical use.
Book a 20-minute pilot call
If your team is building a source-grounded clinical workflow, we can scope the smallest useful package of reviewed examples, groundedness checks, and review criteria.
Book a 20-minute call- Start with 50 reviewed examples
- No PHI required for the first conversation
- We will be direct about fit