Expert reasoning data for high-stakes, regulated domains.

In finance and legal AI, a confident wrong answer is a liability. These teams need data authored and reviewed by qualified domain experts, rigorous factuality checks, and evaluation that tracks compliance and reasoning quality, not surface fluency.

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AI use cases

Where it applies.

Document and contract analysis
Domain reasoning fine-tuning
Factuality and citation checks
Compliance evaluation
Risk and policy red teaming
Expert preference judgments

Data requirements

What it takes.

Finance / legal domain experts
Citation and factuality verification
Policy-grounded rubrics
Confidential data handling
Reasoning trace review
Versioned benchmarks

Relevant data products

Products that map to this work.

Data Product

Frontier Alignment

CoT reasoning, SME RLHF, SFT demonstrations, DPO data, and red teaming for frontier model post-training.

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Data Product

Model Integrity & Evaluation

Private benchmarks, hallucination evaluation, safety red teaming, bias and compliance audits.

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Data Product

Agentic AI Data

Golden trajectories, tool-use logs, RL environments, and workflow simulations for agents that ship.

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Workflow

How the program runs.

01Scope
02Rubric Design
03Expert Production
04Factuality QA
05Evaluation
06Iteration

Continuous loop — outputs feed back into the data engine.

Quality & compliance

Built for regulated, high-stakes work.

Every engagement runs on our quality system and enterprise-grade security workflows — the controls an auditor would expect.

Confidentiality and NDA workflows
Data isolation
Audit logs
Regulatory-aligned governance

Quality & Security model

Case study

Proof in production.

Foundation Models

Scaling Expert Reasoning Data for Frontier Model Alignment

Expert reasoning and preference data for domain-specific model alignment — 40+ qualified SMEs producing calibrated CoT and preference labels across STEM and finance.

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