Clinical record enters review
Source trigger
Domain intelligence for better models.
Expert-verified training data and reward signals for healthcare, STEM, software, finance, RL environments, and knowledge work.
Healthcare dataset1M+
Connected clinical records across X-ray, CT, MRI, pathology, dermatology, oncology, prescriptions, and drug grounding.
- X-ray
- CT · MRI
- Pathology
- Dermatology
- Oncology
- Rx
Coding dataset50k+
Real software engineering tasks with full agent trajectories, tool calls, and human acceptance signals. Nothing synthetic.
- Trajectory steps
- 833k
- Reward signals
- 29k
- Tool interactions
- 382k
Domain experts2M+
Specialists across healthcare, software engineering & finance.
Latest roles
Generic annotation fails where domain judgment matters.
Generic annotation
- Labels from people who have never done the job.
- Shallow fragments with none of the workflow around them.
- Benchmarks that pay for looking right, not being right.
- Judgment errors that surface only in production.
The Zstate approach
- Tasks, rubrics, and reward signals designed with working domain experts.
- Complete trajectories captured from real production workflows.
- Signals grounded in downstream behaviour, so models learn judgment.
Five expert verticals, one standard.
Built with the people who do the work.
Showing Health domain
Healthcare specialists
X-ray, CT, MRI, pathology, dermatology, oncology, prescriptions. 1M+ records grounded by working specialists.
1M+Health records
Typical work
- X-ray, CT & MRI
- Pathology & oncology
- Dermatology
- Prescriptions & drug grounding
Ground every clinical case
Specialists ground each record across imaging, pathology, skin and oncology, check drugs and findings, and ship outcomes with full clinical context.
Specialist grounds the case
Clinician review
Drug and imaging checked
Domain grounding
Outcome ships with context
Signal ready
Software engineers
Review AI code, write production-grade solutions, and shape the next generation of coding models.
50k+Engineering tasks
Typical work
- Real engineering problems
- Stack-matched projects
- Failure analysis
- Reference patches
Score real engineering work
Stack-matched engineers review the trace, flag failure modes, and turn the fix into reward signal.
Repo task enters the queue
Source trigger
Engineer reviews the trace
Stack-matched review
Failure modes scored
Precise analysis
Reference patch becomes signal
Reward ready
Scientists, researchers & PhDs
Verify AI reasoning, check proofs, and train models that handle units, methods, and causation with real rigor.
2.5k+PHD level tasks
Typical work
- Derivations & proofs
- Math, physics, bio, chem
- Units & causation checks
- Gold references
Verify with domain rigor
Specialists derive the solution, check the proof, and attach a gold reference the model can learn from.
Hard STEM problem arrives
Source trigger
Expert derives the solution
Specialist review
Proof checked for rigor
Verified by experts
Gold reference attached
Signal ready
Investment, risk & finance professionals
Evaluate earnings, filings, risk models, and trade rationale. Credentialed judgment, not pattern matching.
5000+Financial tasks
Typical work
- Earnings & 10-K evaluation
- Risk & compliance
- Fraud rationale
- Analyst scoring
Audit financial judgment
Credentialed analysts score the rationale, attach risk flags, and leave an audit trail with the signal.
Filing lands in the workspace
Source trigger
Analyst scores the rationale
Credentialed review
Risk flags attached
Compliance check
Trade signal leaves with audit
Signal ready
Sharp readers & writers
Teach AI to follow instructions, write clearly, and stop inventing facts. Judgment most people already have.
120k+Ranked responses
Typical work
- Instruction following
- Factuality ranking
- Hallucination flags
- Gold responses
Rank for clarity and truth
Readers rank tone and clarity, flag hallucinations, and publish preference signal models can train on.
Model responses enter ranking
Source trigger
Readers rank clarity and tone
Expert ranking
Hallucinations flagged
Factuality check
Preference signal published
Signal ready
From real workflows to reward signals.Every dataset walks the same path.
Three stages from source material to production-ready signals, with the same rigor across health, STEM, coding, finance, and generalist work.
Collect complete context
Capture reasoning traces, tool calls, code edits, records, and source material from real production work.
Review with specialists
Domain experts apply explicit rubrics and score the reasoning, method, tool use, and outcome.
Deploy durable signals
Turn accepted work into evaluations, reward signals, and environments that retain source judgment.
1 / 3
Intelligence for the work that moves the world forward.
Expert-verified samples across healthcare, STEM, software, finance, RL, and knowledge work.
Healthcare
{
"case": "discharge-plan / 7A",
"guidelines": 4,
"findings": 12,
"safety": "complete",
"reviewer": "attending MD"
}Stem task
{
"environment": "STEM",
"task": "selectivity-028",
"system": "Pd catalyst screen",
"conditions": "65°C · 12 h",
"variables": 18,
"result": "validated yield"
}Software
{
"task": "build-regression / TS-184",
"suite": "34 tests · 3 services",
"stack": "TypeScript · Node",
"ci": "reproduced",
"reward": 0.91
}Finance
{
"task": "portfolio-stress / Q3",
"holdings": 42,
"factors": 6,
"shortfall": "2.7%",
"audit": "locked"
}RL env
{
"env": "warehouse-routing / E-12",
"actions": 64,
"constraints": 8,
"success_rate": 0.912,
"episodes": 1200
}Knowledge
{
"task": "policy-brief / research",
"sources": 28,
"claims": 16,
"citations": "100%",
"hallucination": "none"
}Built with the people who do the work.
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