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
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. Multi-Model Grounded Reasoning
The final output is generated by our dynamic multi-model architecture. We intelligently route high-volume analysis to DeepSeek and high-stakes drafting to Claude 3.5 Sonnet, all constrained by strict "grounding" rules. Strategic recommendations are designed to be supported by citations from the retrieved context.
Grounding and Escalation Checks: AI outputs are checked against retrieved FDA Knowledge Graph context. If the system detects elevated grounding risk, our dynamic router can escalate the task to Claude for higher-stakes review. Regulatory logic should remain source-backed and professionally reviewed.
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.
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