What Is the Clinia Context Engine?
A health-specific context layer between a patient's data and the agents built on it.
Health data is fragmented, comes in many different modalities, and is riddled with duplicates, conflicting values and gaps in time. Letting LLMs reason over that raw data is neither reliable nor cost-effective for health AI builders.
The Clinia Context Engine is a health-specific context layer that sits between a patient's data and the AI agents built on it. It turns that data into persistent memory and serves it to agents as context.
From records to context
The Context Engine forms memory from both the systems of record (Recorded Memory), such as EHRs, lab systems, and claims systems, and from agent interactions (Learned Memory), such as conversations between an agent and a patient, a caregiver or a clinician.
Both are unified into one patient graph per patient. Records from different sources are merged by clinical meaning using standard terminologies such as SNOMED CT, LOINC, ICD-10 and RxNorm, facts and concepts are linked to one another, and events are placed in time. Merges and decisions are recorded and trace back to the initial records they come from.
Agents read that memory through an MCP server that projects a patient's context as a virtual file system, with condition stories and search functionalities. Agents access information through progressive discovery, browsing the patient's folders and consulting only the files needed for a task. And because that context is connected, placed in time, and sourced, they perform their tasks more accurately, more consistently and at a lower cost. The health AI workflows those agents run get better outcomes as a result.
Next steps
- How the Engine Thinks About Data for the model behind the engine.
- Quickstart to explore a patient story in under 10 minutes.
- Ingest and Query a Patient for the full walkthrough with a FHIR bundle and a CDA document.
- Connect an AI Agent to give your own agent the MCP tools.