Post-training data infrastructure

Expert reasoning,
structured for training.

Nthoric produces post-training datasets from institution-verified domain specialists worldwide — clinical, legal, and scientific reasoning chains built to your pipeline specification, with full provenance on every data point.

Request a sample dataset See how a data point is built →
data_point.process
"...the ST elevation in V1 alongside an inferior pattern raises suspicion for RV involvement — this changes management, since RV infarcts are preload-dependent. I would avoid nitrates and support pressure with fluids first, then confirm with a right-sided ECG before activating the cath lab..."
Expert reasoning
Structured data point
{
  "domain": "cardiology",
  "reasoning_depth": 4,
  "differential_considered": 3,
  "quality_score": 96.2
}
✓
DM Cardiology · Verified at source · Registration confirmed
Post-trainingSFT · RLHF · Reward modeling
VerificationCredential confirmed at source
ProductionAI-assisted, expert-verified
CoverageMedical · Legal · Scientific · Code
DeliveryBuilt to your pipeline spec
ProvenanceDocumented on every data point
Post-trainingSFT · RLHF · Reward modeling
VerificationCredential confirmed at source
ProductionAI-assisted, expert-verified
CoverageMedical · Legal · Scientific · Code
DeliveryBuilt to your pipeline spec
ProvenanceDocumented on every data point
The gap

Models are only as good as the reasoning they were trained on.

Pre-training scales on volume. Post-training depends entirely on the quality and verifiability of human expert judgment — and that supply does not exist at the depth or scale frontier labs now need.

01
Generic annotators can't reason
A cardiologist catches what a contractor cannot. Domain reasoning chains require domain practitioners — not instructions and a rubric.
02
Self-reported credentials don't hold
Most data vendors take a CV at face value. We verify every credential directly with the issuing body before a single task is assigned.
03
Raw data alone is commoditizing
The value isn't the data point — it's the verified reasoning chain, the provenance record, and the quality signal layered on top.
How a data point is built

From expert judgment to pipeline-ready data.

Production
①
Credential verified at source
Every specialist's qualification is confirmed directly with the issuing institution — not self-reported, not assumed.
②
AI drafts, expert verifies
Models generate first-pass scenarios and structure; specialists correct, deepen, and validate the reasoning — reducing cost without reducing rigor.
③
Independent quality review
A second verified specialist scores every submission against a domain-specific rubric before it enters the dataset.
Delivery
④
Formatted to your specification
We match your post-training pipeline exactly — schema, metadata, and structure defined with your research team before production begins.
⑤
Provenance on every record
Credential, institution, reviewer, and quality score travel with the data point — auditable at any point in your pipeline.
⑥
Pilot, then scale
Start with a sample set sized to your evaluation needs. Scale to production volume once quality is confirmed against your benchmarks.
Domains

Where reasoning can't be synthesized.

We focus on domains where a model's error has real consequence — and where expert judgment remains the only reliable training signal.

◐
Clinical reasoning
Diagnostic chains, differential reasoning, and management decisions from credentialed specialists.
⚖
Legal analysis
Contract review, statutory reasoning, and case analysis from bar-admitted practitioners.
∑
Scientific evaluation
Methodology review, claim verification, and research reasoning from domain PhDs.
⌥
Code & systems
Correctness review, security evaluation, and architectural reasoning from senior engineers.
Why Nthoric

Not a labor marketplace. A verified data layer.

We sell directly to research teams — never through intermediaries — and the value sits above the data, not just in it.

Verified, not self-reported
Every specialist's credential is confirmed directly with the issuing body before they touch a single task. Provenance is structural, not a claim.
Built to your specification
No off-the-shelf datasets. Every production run is scoped against your pipeline format, scale, and quality threshold before we start.
Independent, by structure
Not affiliated with any lab or hyperscaler. Your training data and your competitive position stay yours.
What you receive

A data card with every delivery.

Full provenance — credential, institution, and reviewer attached to every record.
Quality distribution — score breakdown across the full delivered set, not just averages.
Production methodology — exactly how each point was generated, verified, and reviewed.
Licensing terms — clear, documented usage rights for every dataset delivered.
DATA CARD NT-2026-0614
domainclinical_reasoning
records2,000
specialists24 verified
avg_quality94.6
review_pass2-reviewer
formatJSONL
licenseCommercial v1

Start with a sample, not a sales call.

Tell us your domain and pipeline format. We'll send a sample set built to your specification — full provenance included.

For research teams
Request a sample dataset
50–100 records, your domain, your format. No commitment, no cost.
Request a sample
For domain specialists
Apply to the verified network
Credentialed physicians, lawyers, scientists, and engineers — flexible, remote, paid per delivered record.
Apply as a specialist →