research

Auditable research requires deterministic logging of searches and strict evidence management to track verifiable sources. This workflow uses domain mapping and Python-based scripts to generate structured artifacts like evidence.json and validation.json for full traceability.

Deterministic logging records every search query and source accessed during multi-domain research to ensure auditability. It produces structured artifacts, allowing you to verify that final synthesized reports are backed by evidence from market, technical, or user research.

To generate a structured research report, you input your research objective and questions into the workflow. It processes requirements through domain mapping and evidence validation, outputting a final report.md alongside structured JSON files like analysis.json and evidence.json.

Yes, Python-based scripts validate research requirements across multiple domains by generating a domain-plan.json. This structured approach ensures your market, technical, and user research questions receive verifiable sources and pass recency checks before synthesis.

The best way to organize multi-domain research is using a structured workflow that maps domains and validates evidence. This approach generates deterministic JSON artifacts like requirements.json, validation.json, and analysis.json, culminating in a synthesized report.md with citations.

Automated domain mapping for market research relies on strict evidence management and recency checks, meaning it requires clearly defined research questions and objectives. Without proper requirement validation, the generated structured artifacts may not fully address the target problem space.