Not enough validated data
A dataset can look finished until a subject-matter expert reviews it properly. Then the team finds weak labels, inconsistent decisions, and too many edge cases to trust it in development.
Data that keeps up with AI
Stop waiting on data providers
SME-validated datasets and evaluations that keep up with the pace of healthcare AI. Teams do not just need more data - they need data they can trust, in the right structure, fast enough to keep development moving. That's why we built Temyrion.
- Get datasets built from scratch
- Fix weak labels with SME review
- Get structured outputs your team can use now
Delivered - 68 hrs
Cardiology discharge notes
340 records - ICD-10 labeling
Scope defined
Schema + review criteria agreed
Clinician review
3 cardiologists - adjudication complete
Structured output delivered
Cross-validation package included
dataset.json rubric.md
Inter-rater agreement
94.2%
Edge cases flagged
18 / 340
Ready in your eval pipeline Use now ->
Why teams get stuck with data providers
We built Temyrion after seeing too many teams hit the same wall: they were ready to build, but the data was not ready to use. It was not validated enough, not structured for the actual workflow, and too slow to arrive.
Wrong structure for your team
Even when the underlying data is useful, it often arrives in the wrong shape. The schema does not match the team's process, the outputs are not usable, and the team loses another cycle asking for revisions.
Slow delivery blocks development
When every iteration takes too long, engineering cannot move, evaluation gets delayed, and product progress depends on waiting instead of learning.
Healthcare AI teams get blocked when expert data ops break down
Teams building healthcare AI often hit the same bottleneck: they need SMEs to create gold datasets, define rubrics, review difficult outputs, and evaluate whether systems are actually improving. But off-the-shelf provider processes are often too shallow, too rigid, or too slow. We help teams move faster by combining SMEs with a reliable review and delivery process that makes expert work more structured and efficient.
Day 47 - still waiting
Cardiology discharge notes
~300 records - format TBD
notes_final_v3.pdf
meeting_notes(2).docx
labels_REAL.csv
Messy intake
No schema - 3 conflicting files
Unclear rubric
Revision #4 - criteria still shifting
Weak labels
No clinician sign-off - edge cases skipped
Label agreement
unknown
Delivery date
TBD
Engineering blocked - waiting Not usable
Three ways teams work with us
Dataset creation from scratch
Need 200 medical papers labeled by SMEs? We source, structure, label, quality-check, and deliver benchmark-ready data.
Materials in, structured data out
Send documents, literature, transcripts, spreadsheets, or model outputs. We return validated structured outputs, rubrics, and evaluation assets.
Continuous evaluation ops
Send flagged failures or sampled outputs. We run SME review, refresh datasets, and help you track whether your system is getting better.
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 into structured tasks so experts can review quickly and consistently.
3
Run expert review
Relevant experts review, label, correct, and adjudicate difficult cases.
4
Deliver usable outputs
You receive validated datasets, JSON outputs, rubrics, or refreshed evaluation slices ready to use.
Why we built Temyrion
Subject-matter expertise is the bottleneck
Healthcare AI breaks on nuance. Generic annotation workflows are not enough when the work depends on real SME review.
Delivered data is not the same as usable data
If the schema, rubric, or output format does not fit the team's process, the team still cannot build.
Slow provider cycles kill momentum
When every change takes another round trip, engineering and product end up waiting instead of learning.
Expert review needs a better process
We make expert review easier on our side, so SMEs spend their time reviewing difficult cases instead of wrestling with docx files, spreadsheets, and manual formatting.
Teams need a practical first step
Sometimes the right start is one dataset, one eval slice, or one blocked workflow - not a heavyweight engagement.
What we provide
We provide SME-validated datasets, structured outputs, and evaluation support for healthcare AI teams. We combine SME-led review with a structured validation process, so every rubric, gold set, and output can be checked before it reaches your team.
Tell us where your team is stuck
If you are blocked on unreliable labels, unusable output structure, or slow provider cycles, send us the problem. We will tell you quickly whether we can help and what a practical first step looks like.
Book a 20-minute call- Start with one dataset or evaluation slice
- No heavy integration up front
- We will be direct about fit