Description
Early-stage AI startup building an automated classification and risk assessment engine for a complex, multi-regime regulatory environment. The system ingests unstructured product and transaction data, extracts technical attributes, and classifies items against large government-maintained taxonomies with quantitative thresholds. The regulatory domain involves multiple overlapping classification systems, each maintained by different government agencies, with significant penalties for misclassification. Our system runs classifications across these taxonomies, correlates the outputs, and produces structured risk assessments designed for professional review. The core product. Specifically: • Information extraction pipeline that pulls structured, classifiable attributes from unstructured text (product descriptions, technical specifications, commercial documents) • Classification models that match extracted features against large hierarchical code systems (tens of thousands of entries across multiple overlapping taxonomies) • RAG architecture over a corpus of 500,000+ government rulings and regulatory documents • Vector similarity search for classification candidate retrieval and ranking • Technical parameter extraction and threshold matching against regulatory schedules (e.g., determining whether a quantitative product attribute crosses a controlled threshold) • Confidence scoring and calibration — the system must know when it’s uncertain and route to human review What We Need You