AI data built by experts who actually know the domain
Zstate delivers RLHF training data, SFT datasets, and evaluations built by credentialed specialists. Our engineering team takes your models into production.
Training data for models that interpret clinical notes, discharge summaries, and physician reasoning. Evaluated by practicing clinicians.
Diagnostic Q&A & multimodal
Expert-graded preference data for diagnostic reasoning tasks, imaging interpretation, and clinical decision support evaluation.
Medical coding & EHR abstraction
ICD-10 and CPT coding validation, and EHR data abstraction tasks handled by certified coders and health informaticists.
HIPAA-compliant data workflows built in from day one
Earnings & analyst evaluation
Preference data and SFT datasets for models reasoning over earnings reports, 10-K filings, and sell-side research. Evaluated by credentialed analysts.
Risk & compliance data
Training and evaluation data for risk model assessment, regulatory compliance tasks, and stress testing scenarios. Reviewed by risk professionals.
Fraud detection & trade rationale
Expert-annotated datasets for fraud detection, trade rationale evaluation, and financial reasoning benchmarks.
SOC 2 Type II & SEC-aware data handling
Agentic system design
Architecture and build of multi-agent systems from scratch, including tool use, memory, orchestration, and handoff logic designed for complex, long-horizon workflows.
RL environment engineering
Custom reinforcement learning environments that simulate real expert decision workflows. Built to generate high-signal training data and meaningful evaluation benchmarks.
Production deployment & ops
From working prototype to production system, with guardrails, observability, human-in-the-loop checkpoints, and the infrastructure to run agents reliably at scale.
258k real engineering tasks
Complete agent trajectories across 258k real-world software engineering problems with reasoning traces, tool calls, code edits, and explicit user acceptance signals. Nothing synthetic.
Three derived datasets
Task dataset (258k cleaned prompts), Trajectory dataset (3.7M full agent traces with tool use and code generation), and Reward dataset (130k explicit user acceptance signals supporting multi-accept workflows).
Beyond SWE-Bench
Where SWE-bench captures prompt → code, ours captures the full lifecycle: reasoning → tool calls (6–7 per task across 22 tools) → code edits → human acceptance. Real production tasks, not curated benchmarks.
Our annotators hold software engineering credentials, clinical certifications, and finance licenses. They understand the task, not just the label schema. This is the difference between a data vendor and a domain partner.
Compliance-first by design
Compliance-first workflows aren't an add-on. They are the architecture. Built for the domains where data handling mistakes have legal and human consequences.
Engineers who ship production AI
We've built agentic systems for regulated industries. That means our training data is built with deployment outcomes in mind, not just F1 scores. We know what good data produces downstream.
Vertical depth, not horizontal breadth
We go deep in software engineering, healthcare, and finance instead of shallow across twenty industries. That depth is why our data is defensibly better, and why our clients don't look elsewhere.
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Whether you need expert training data or a production AI system, let's start with a conversation.
Data services
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RLHF, SFT datasets, evaluations & red-teaming by domain experts
Engineering
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Production-grade agentic AI systems for regulated industries