Patient Memory turns fragmented health data into a trusted, reasoning-ready view of a patient. So healthcare's most abundant resource finally becomes its most useable.
Health data is digitized, standardized, and largely accessible. The meaning, the relationships, and the structure that make it clinically usable by humans or the AI agents working alongside them are not.
One medication, three spellings, five different IDs across five systems. Agents can't reason over a record they can't even resolve.
Metformin 500 mg in the EHR, 1000 mg in the PDF, both timestamped today. Which one does your agent answer with?
An A1c spike, a diabetes diagnosis, and a medication change belong together. The record keeps them apart and the agent never connects them.
A decade of encounters arrives as a pile, not a chronology. Agents can't reason over what changed, when, or why.
Agents reason over fragments and produce inconsistent, hallucinated, or shallow answers. The care suffers. So does trust in the AI meant to support it.
Token costs balloon when AI re-reasons instead of remembers. Every restart re-solves context you already paid for.
Accuracy suffers when relationships are inferred from contradictory fragments.
Patient Memory connects and restructures health data so every agent reasons over a complete, contextual, and persistent clinical picture.
# Type 2 Diabetes First documented 2019-04-12. Current Rx: metformin 1000 mg BID. Latest HbA1c: 6.9% · ↓ improving Comorbid: hypertension, dyslipidemia. Complications: retinopathy (mild).
Build with the primitive that fits your needs. A REST API for your engineers; an MCP server for your agents.
Ingest data to update the graph. Audit and inspect every decision the system made on every record.
Agents use a set of primitives to reason over a patient's story, without bringing the whole record into context.
Infrastructure that holds up where it matters: clinical standards, provenance, and access control built in.
Every value in the record links back to its source document, the rule that resolved it, and the decision the system made.
FHIR R4/R5, C-CDA, HL7 v2, PDF, DICOM, SQL exports, and whatever else lives in your stack.
Concepts grounded in SNOMED · LOINC · ICD-10 · RxNorm. Every entity speaks the language clinicians read.
OAuth 2.1 and the Agent Auth Protocol.
Enterprise-grade performance you can rely on. The architecture holds whether you serve a hundred patients or a hundred million.
Structured retrieval over a resolved graph means conversations don't pay to re-derive the same record on every turn.
Horizontally scaled by default. The architecture holds no matter the number of patients or concurrent agents.
Supports every clinical and adjacent data types, so Patient Memory scales with the patient.
Every new data point and every agent interaction extends a living memory. Durable across encounters and decades.
Choose the deployment model that fits your compliance posture and your data-residency requirements.
Compliant by default. Go live without infrastructure overhead, we operate the platform end to end.
Deploy into your own VPC. Your data, your keys, your environment, always. Operated, monitored, and upgraded by us.
For organizations with strict sovereignty and compliance mandates. Air-gapped if you need it.
Built to meet the highest standards in healthcare security and compliance, wherever you operate, whoever the regulator is.
Every clinical record is protected at every layer. Privacy and security requirements met by design.
All data is processed, stored, and accessed under controls that meet GDPR requirements. Patient privacy, always.
Independently audited. Continuous monitoring of security, availability, and processing integrity.
Health data collected, used, and disclosed under controls that meet PIPEDA requirements. Patient consent and privacy are protected by default.
