The TCA Research & Intelligence Framework is the foundational layer of the platform — structuring heterogeneous project inputs into a canonical, evidence-anchored analytical record before any scoring occurs.
Every meaningful risk assessment starts with a disciplined account of what is known, what is assumed, and what each claim rests on. The R&I layer produces exactly that — before the Risk Engine sees the project.
This separation means analytical judgment is never contaminated by underlying data-quality problems. If the R&I record is weak, the system says so openly. It does not compensate by guessing.
The framework applies identically to an E&P project, a utility-scale solar farm, a mixed-use development, or a growth-stage technology platform. The dimensions vary; the discipline does not.
Sector-specific insight enters at the content layer — which source types are authoritative, which assumptions warrant scrutiny, which benchmarks apply. The structural layer is constant.
A normalized record with identifying metadata, stage classification, operator data, asset characteristics — the common data structure every downstream process consumes.
Every material claim captured as a structured entry with stated confidence, evidence source, and citation. Versioned as understanding evolves.
The full document collection underlying the project record, cataloged by type, provenance, and SHA-256 content hash for tamper detection.
The framework paper walks through intake discipline, assumption capture, source pack curation, lock transitions, and the quality gates applied before a record moves to the Risk Engine.