Knowledge
Clinical decisions require real knowledge, continuously updated as care evolves.
It is acceptable to say 'I don't know.' It is unacceptable to stop there.
Spencer Wozniak
Co-Founder
What knowledge means to us
Real decisions require real knowledge. We believe that systems should not pretend to know things they do not know. When information is missing, the honest response is to acknowledge the gap—and then close it.
Knowledge acquisition is not a fallback or an error state. It is an intentional part of the reasoning process that allows our platform to handle real-world complexity safely.
Our AI agents are built to retrieve clinical evidence, expose uncertainty, and make their reasoning visible. When they encounter gaps, they seek answers rather than guessing. This is how systems earn trust.
Why knowledge matters
Intelligence without knowledge is pattern-matching. Knowledge without reasoning is trivia. The combination—knowledge integrated into reasoning—is what transforms information into action.
Precision
Decisions are only as good as the information behind them. Incomplete knowledge leads to approximate answers that miss the mark when precision matters most.
Adaptability
Knowledge that cannot be updated becomes obsolete. Systems must continuously acquire, integrate, and revise their understanding as new information emerges.
Humility
The most dangerous systems are those that do not know what they do not know. Intellectual humility—acknowledging gaps—is the foundation of responsible intelligence.
Medicine is knowledge work
Clinical decision-making depends on synthesizing vast amounts of specialized knowledge—patient history, clinical guidelines, pharmacology, insurance coverage, social determinants. No single human can hold it all.
Healthcare AI fails when it assumes more than it knows. Systems that hallucinate drug interactions, fabricate guidelines, or invent patient history are not just unhelpful—they are dangerous. The bar is higher here.
The knowledge is always changing
Medical knowledge evolves continuously. Guidelines are updated, drugs are approved, coverage policies change. A system that cannot acquire new knowledge becomes stale and unreliable.
This is why knowledge acquisition must be built into the reasoning process itself—not as an occasional update, but as a continuous capability that keeps pace with the reality of care.
How we build knowledge into the product
Agentic RAG architecture
Our agents do not just retrieve documents—they reason over them. When answering a question, the system evaluates whether available information is sufficient, identifies gaps, and initiates targeted searches to fill them.
- Dynamic retrieval based on reasoning needs
- Multi-step knowledge acquisition chains
- Continuous evaluation of evidence sufficiency
Agentic RAG demo
Domain-specific agents
Different domains require different expertise. Our clinical agents understand medical terminology, drug interactions, and diagnostic criteria. Our financial agents understand insurance policies, coverage rules, and authorization requirements.
- Clinical-grade reasoning with medical guardrails
- Financial/benefits reasoning for insurance interpretation
- Specialized knowledge bases for each domain
Domain agent demo
Graph-native knowledge
Our agents traverse the Claims Graph, following relationships and edges rather than just retrieving text chunks. This enables reasoning that understands connections—how a diagnosis relates to a medication, how a procedure connects to a coverage policy.
- Traverse relationships, not just retrieve documents
- Understand causal, temporal, and logical connections
- Sub-second queries across complex patient context
Graph traversal demo
Knowledge is not static. It is a living process that our systems perform continuously.