One engine between the record and the decision

A patient's health data lives across disconnected systems and formats. The Health Context Engine resolves it into a longitudinal record, reasons over it using clinical evidence, and returns structured answers with citations.

88%Endocrinology board exam accuracy

0.8%Hallucination rate on clinical cases

5Clinical coding standards resolved

Three layers, one contract

Each layer has defined inputs and outputs, with citations that make the result auditable

Data layer

Four streams that are almost never in the same place at once.

Clinical data

EHR, labs, imaging, diagnoses, medications, encounters

Patient-generated data

Wearables, food logs, surveys, patient-reported outcomes

Omics & biomarker data

Genomics, microbiome, advanced biomarkers

Claims & utilization data

Claims, cost, coverage, utilization

Health Context Engine

Where fragmented records become a single, queryable longitudinal record.

Personal Health Knowledge Graph

A longitudinal timeline per patient, resolved against a standards ontology backbone.

SNOMED-CTLOINCRxNormICD-10CPT

Reasoning Engine

Inference combined with deterministic clinical rules, so output is explainable and repeatable.

InferenceClinical rules

Clinical Evidence

Guidelines, landmark trials, and curated literature, retrieved with citations attached.

GuidelinesLandmark trialsCurated literature

Application layer

Use the same intelligence across patient, clinician, and automated workflows.

AI Chat

Natural-language access to a patient’s full context.

Insights & recommendations

Behavior change guidance grounded in the record.

Predictions & clinical decisions

Risk stratification and next best action.

AI agents

Orders, referrals, and alerts completed automatically.

Grounded, traceable, deterministic

Clinical workflows require consistent, traceable outputs. January combines retrieval with deterministic rules so answers can be reproduced and traced back to their sources.

88%

Highest endocrinology benchmark score

Our Health Context Engine outperforms GPT, Gemini, and human clinicians on the endocrine medical board exam. Retrieval runs against a curated internal corpus of clinical guidelines and peer-reviewed literature rather than the open web.

Endocrine Medical Board Exam results; January AI RAG performance vs. GPT, Gemini, and human users

0.8%

Hallucination rate

The engine produced hallucinations in 0.8% of cases and contradictions in 1.8%, significantly lower than general-purpose frontier models tested on the same clinical cases (−log₁₀(p) = 8.0 and 8.4).

Internal evaluation vs. GPT and Gemini across clinical case set

Meet your stack where it is

Easy-to-integrate APIs

REST endpoints for glucose prediction, health context, and nutrition, backed by 54M foods in our database. Ship in days, not quarters.

Read the API docs

EHR-connected workflows

Epic-integrated and qualified on Mayo Clinic Platform, insights land where clinicians already work.

White-label applications

Deploy the full consumer experience under your own brand, on your own terms.

See the engine run on your data

Book a demo