Training Data That Thinks Like a Professional
Domain-authentic agentic workflow datasets for AI labs fine-tuning on expert reasoning, not just expert answers.
See It Before You Talk To Us.
Methodology Document
Understand exactly how we model expert cognition and generate high-fidelity, auditable reasoning traces for LLM fine-tuning.
Equity Analyst Workflow
Problem
Financial AI models were summarizing earnings calls but failing to identify subtle margin risks hidden in supply chain footnotes.
Output Format
Multi-step reasoning trace with DCF model tool calls.
Result
34% improvement in risk-identification accuracy on withheld validation sets.
We Don't Prompt a Model. We Model an Expert.
Agent Type
We define who the expert is — their role, reasoning patterns, and decision logic.
Users
We map who they serve — context, constraints, and interaction patterns.
Output
We generate production-grade agentic workflow traces — tool calls, multi-step reasoning, professional decision chains.
Other Synthetic Data Starts With a Prompt. We Start With a Workflow.
| Feature | Others | Us (Three-Q Framework) |
|---|---|---|
| Starting Point | Prompt an LLM | Model expert cognition |
| Data Type | Domain-labelled | Domain-authentic |
| Output Format | Q&A pairs | Agentic workflow traces |
| Scalability | Prompt-dependent | Framework-driven |
| Auditability | Low | Structurally high |
Built for Regulated, High-Stakes Domains.
Our agentic workflow traces are generated by modeling the actual reasoning patterns of professionals in fields where precision is non-negotiable.
School of Chemistry
Chemical reaction analysis, material science reasoning
School of Finance
Equity analyst workflows, financial reasoning traces
School of Law
Case analysis, contract review decision chains
School of Medicine
Clinical reasoning, diagnostic workflow traces
School of Aerospace
Flight mechanics, aerospace engineering traces
Global Scale Infrastructure
Our agentic reasoning layers are deployed worldwide, powering high-stakes decisions across all timezones.