The Science of
Deterministic Intelligence
How we eliminate hallucinations by combining Large Language Models with structured Knowledge Graphs and formal medical ontologies.
1. GraphRAG Architecture
Standard RAG relies on vector similarity, which often retrieves irrelevant chunks. We use Graph-based Retrieval Augmented Generation to traverse semantic relationships (e.g., "Drug A" ➔ *treats* ➔ "Condition B"), ensuring contextually accurate retrieval.
2. Ontology Enforcement
Our model output is strictly constrained by standardized medical ontologies including SNOMED-CT, RxNorm, and LOINC. If a concept doesn't exist in the verified taxonomy, it is flagged for review.
3. The "Judge" Layer
Every response passes through a secondary "Judge Model" trained on medical guidelines. It verifies that every claim is explicitly supported by the retrieved citations before the answer is shown to the user.
Accuracy Benchmarks
Performance on the MultiMedQA suite compared to general purpose models. VitalSearch demonstrates significantly lower hallucination rates in drug interaction scenarios.
* Internal benchmarks based on USMLE Step 2 & 3 sample questions.