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 turn messy, multi-format inputs into a structured clinical knowledge graph—and we store what we learn as claims, traceable to their source, never as untraceable facts.
Structuring knowledge is not a one-time import. It is a continuous process that keeps the patient record current and lets our platform handle real-world complexity safely.
Every recommendation links back to the exact chart entry and supporting literature that produced it. When the evidence is thin, our agents expose the uncertainty 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. As new data arrives—labs, notes, claims, intake forms—the graph integrates and revises its understanding of the patient.
Humility
The most dangerous systems are those that do not know what they do not know. We store knowledge as claims with explicit confidence—surfacing uncertainty instead of being confidently wrong.
Medicine is knowledge work
Clinical decision-making depends on synthesizing vast amounts of specialized knowledge—patient history, clinical guidelines, pharmacology, insurance coverage, social determinants. Serelora extracts these from any format and any source into a single clinical knowledge graph, no manual entry required.
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. That is why every claim in the graph is traceable to its source.
The knowledge is always changing
A patient's record changes continuously. New labs return, notes are dictated, claims are filed, guidelines are updated. A system built on static facts becomes stale the moment care moves forward.
This is why knowledge stays current by design. As new data arrives, the graph incorporates it—keeping the structured, queryable record of the patient in step with the reality of their care.
How we build knowledge into the product
Extraction into structure
Messy, multi-format inputs—patient uploads, clinical notes, claims, intake forms, labs, SDOH—are extracted into structured clinical knowledge. Ambient scribing and live EHR integration produce records with no manual entry.
- Any format, any source—EHR-agnostic ingestion
- Medical NLP extraction into a clinical knowledge graph
- Structured records with no manual data entry
Agentic RAG demo
Claims, not facts
Knowledge is stored as claims, never as untraceable facts. A two-layer epistemology separates mentions from claims, each carrying its own confidence—so the system can represent disagreement, ambiguity, and what it does not yet know.
- Two-layer model: mentions resolve into claims
- Orthogonal confidence values on every claim
- Uncertainty is surfaced, not hidden
Domain agent demo
Graph-native and queryable
Relationships between diagnoses, medications, events, and social determinants are explicit and queryable. Specialized agents traverse the graph rather than scanning text chunks, and every output links back to the node that generated it.
- Explicit relationships across the patient record
- Every output linked to its source node and evidence
- Sub-second queries across complex patient context
Graph traversal demo
Knowledge is not static. It is a living process that our systems perform continuously.