Regulatory Intelligence
Methodology
Transparency is the foundation of regulatory trust. Here is how our Intelligence Engine processes FDA data to guide your submission.
1. Data Ingestion & Indexing
We maintain a high-fidelity vector database of the last 10 years of FDA 510(k) clearances, product classification databases, and consensus standards. Every document is chunked and indexed using OpenAI's text-embedding-3-large models to ensure semantic accuracy beyond simple keyword matching.
2. Retrieval-Augmented Generation (RAG)
When you describe a device, our engine doesn't just "guess." It performs a multi-stage retrieval across disparate FDA data silos:
Knowledge Graph Search
We query our Neo4j Knowledge Graph to identify related product codes, regulations, and previous FDA decisions.
Vector Semantic Search
We find the most technically similar predicate devices by comparing device descriptions against millions of FDA data points.
3. Grounded Reasoning
The final output is generated by Claude 3.5 Sonnet, constrained by strict "grounding" rules. Every strategic recommendation must be supported by a citation from the retrieved context.
Zero Hallucination Policy: If the engine cannot find a verified source for a claim within the indexed FDA data, it is automatically flagged for human review. We never "invent" regulatory logic.
4. Continuous Verification
Regulatory AI cannot be static. We run automated evaluation audits against a ground-truth dataset of verified FDA cases to measure classification accuracy, product code matching, and groundedness on every build.