Emerging Risk Model

The central constraint: language models are allowed to classify passages against a closed nine-mechanism vocabulary and verify quotes verbatim, but every numerical score comes from deterministic additive rules over curated modifier cells. This keeps the output reproducible and lets each number be traced to its source passage.

1. 01Scored risks along velocity, impact and confidence, with provenance recorded at the level of the individual modifier cell.
2. 02Built a deterministic additive engine over a set of curated modifier cells so scores are reproducible run to run.
3. 03Enforced an architecture where LLMs classify against a closed vocabulary and verify verbatim quotes, and never emit a score.
4. 04Automated ingestion from multiple sources with source-level permissions and a four-gate admission test.
5. 05Added an audited LLM-as-a-judge second opinion that annotates but cannot override the deterministic result.

• Deterministic scoring model design
• LLM boundary enforcement
• Data provenance and auditability
• Web scraping and document parsing
• Scheduled ingestion workers
• Automated testing
• Technical methodology documentation