Self-hosted deployment
On-premise or encrypted hybrid cloud install. Clinical data never leaves the organisation’s perimeter, and open-weight LLMs run locally, including medical models.
AgenticHealth
From self-hosted deployment to the doctor's decision in 5 steps. AgenticHealth prepares and documents; the diagnosis and the signature stay with the doctor, and the data stays in the organisation.
The 5 steps01 02 03 04 05
RAG on your data
Indexing of reports, lab results, discharge letters and imaging reports. HL7 FHIR, DICOM and clinical PDFs, with the knowledge base aligned to the EHR.
Query and retrieve with sources
The clinical chatbot answers in natural language and diagnosis support proposes differentials. Every answer links back to the documents and guidelines it is drawn from.
Monitor and simulate
Remote follow-up with AI-guided questionnaires and IoMT parameters, deterioration alerts and a digital twin to simulate therapy scenarios and estimate outcomes.
The doctor decides and signs
The clinical decision stays human: AgenticHealth proposes and documents, the doctor verifies, decides and takes responsibility. Every AI interaction stays in the audit trail.
Under the hood
An on-premise agentic workflow grounded in clinical data
Each agent has a job: run the LLMs locally, index reports and tests, retrieve the relevant documents (RAG), cite the sources, keep the follow-up going and log every interaction. Answers are anchored to the organisation’s own data and documents, with the sources in view, not to a model’s generic memory. And clinical data stays inside the organisation’s perimeter.
How we keep control
Guarantees built into the product
Answers with cited sources
The chatbot and diagnosis support anchor every answer to the organisation’s documents and to guidelines: on review you verify the source, you do not rebuild it.
Data stays local
LLMs run on-premise or in an encrypted hybrid cloud. Clinical data does not leave the organisation’s perimeter: no calls out to external services.
The doctor approves
AgenticHealth proposes and documents; the diagnosis, the clinical decision and the signature stay with the doctor. Human-in-the-loop as a constraint, not an option.
No training on your data
Patient data serves to answer on that patient’s case, not to train shared models. Professional secrecy and GDPR by design.
Where AgenticHealth sits
Compared to other approaches
| Approach | What it does | Where the data lives | Limit |
|---|---|---|---|
| Generic cloud AI chatbot | Answers generic questions | Leaves for the vendor’s cloud | No access to your clinical data, no cited sources, no audit trail |
| EHR / clinical record | Stores and displays the data | Stays in the organisation | Consultation, not natural-language querying or diagnosis support |
| Literature search engine | Searches guidelines and papers | On the public knowledge base | Does not know the patient, does not link their reports and tests |
| Telemedicine platform | Remote visits and follow-up | On the service’s cloud | No RAG over the clinical data, no digital twin |
| AgenticHealth | Clinical chatbot, RAG, diagnosis support, follow-up and digital twin | Stays in the organisation (on-premise / hybrid) | Requires an initial setup on clinical data; launch in RUO pre-CE |