Built to understand health context, not just consolidate data
Most context engineering tools were designed for enterprise AI and adapted for healthcare. Clinia Context Engine is built for health, from the ground up.
Differentiators at a glance
Decisions generic context tools can’t make
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Which record wins?
A lab-verified record here, a self-reported entry there. Simple memory systems trust whichever is newest. The Context Engine resolves conflict using a four layer strategy and gives your agents a coherent answer.
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Conversation memory or clinical facts?
Most memory layers capture what was said. The Context Engine captures both clinical context (Recorded Memory) and personal memory (Learned Memory), in two separate graphs served through a single access point.
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Why is this patient on this medication?
A list of medications isn’t clinical reasoning. Knowing ‘when and why metformin was prescribed for T2DM’ is. Context Engine infers clinical relationships so agents can act on accurate context.
Approach comparisons
None of the alternatives give agents the grounded context they need to be trusted in care.
| Context Engine | FHIR MCP engine | RAG on clinical records | General-purpose memory | Build your own | |
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| Entity deduplication |
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Custom dedup logic + clinical ontology mapping |
| Source conflict resolution |
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Custom dedup logic + clinical ontology mapping |
| Typed clinical relationship inference |
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Clinical KG + custom edge taxonomy + ontology curation |
| Pre-assembled agent context |
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Custom timestamp logic + clinical temporal reasoning model |
| Clinical standards (FHIR, SNOMED, LOINC, RxNorm) |
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License each standard + build + maintain mapping layer |
| Token-efficient agent retrieval |
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Custom compression + retrieval pipeline |
Side by side comparisons
Context Engine: the only context layer built for health complexity
| Context Engine | Zep | Mem0 | Hebbrix | |
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Purpose-built for health Built for healthcare, not adapted for afterwards. |
Designed for health from day one |
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General-purpose platform with a healthcare use-case | Generic memory layer with a healthcare use-case |
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EHR-native ingestion Connect to clinical data source without pre-transformation |
FHIR R4, C-CDA, and unstructured docs via native connectors | Need custom parser | Conversation text, images, PDFs, and generic documents. No FHIR/C-CDA connectors | No EHR connectors documented |
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Clinical data standards ICD-10 · SNOMED · RxNorm · LOINC |
Standard codes as primary resolution key |
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Clinical entity resolutions Conflicting records resolved to one node per entity |
4-layer cascade: Deterministic → NLP → embedding → LLM. ~40% resolve at zero LLM cost | LLM-based entity merging within a user graph. No clinical-code-based deduplication | Each distinct entity stored once and embedded; differently-phrased mentions matched to the same node | Merges duplicate entities within a collection via LLM extraction. No clinical-code-based deduplication |
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Source conflict resolution Adjudicated, auditable decisions, not silent overwrites |
4 strategies applied in order: corroboration → recency → source reliability → LLM judgment. Every decision written as ConflictRecord | Old fact is invalidated (not deleted), new fact created; both kept with 4 lifecycle timestamps | Old and new facts coexist with timestamps. No field-level adjudication record. | Newer memories overwrite older. Full audit logging is enterprise-only. |
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Clinical relationship inference Typed clinical edges that let agents reason, not just retrieve |
8 typed clinical edges backed by a curated clinical knowledge base | LLM-extracted relationships + custom edge types (up to 10). No pre-built clinical knowledge base | General-purpose, no clinical edge types | Auto-generated KG with generic edge types. No clinical taxonomy |
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Shared patient memory across agents All agents see the same resolved patient record at query time |
Every agent has access to the same VFS file, reads the same patient story | Co-accessible via Shared Graphs | Shared via API-level access controls | Shared via Collections. No entity-level resolution |
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Auditability & provenance Field-level audit trail written at pipeline time |
Field-level ConflictRecord per adjudication | Source-level trace. Temporal history preserved, no field-level conflict audit | Full fact history preserved via ADD-only architecture. No field-level conflict adjudication record. |
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Flexible deployment Choose the deployment model that fits your infrastructure |
SaaS, BYOC, or on premise | SaaS, BYOK, or BYOC (AWS / GCP / Azure) | SaaS or open-source self-hosted on any infrastructure | Cloud SaaS only |