Investor Relations

Building the intelligence layer for longitudinal care

Dr.MaxHealth builds clinical intelligence that helps outpatient teams understand how a patient is changing over time.

Investment snapshot

Stage: Pre-seed, founder-led
Legal entity: DMH Software UG (haftungsbeschränkt), Hamburg
Funding to date: Founder capital and a non-dilutive public innovation grant
External venture capital: None to date
Category: Medical intelligence for outpatient care
Initial market: Germany, then the Netherlands and Denmark
Product: MIA, with PULSE as its prediction engine
Programmes: IFB Hamburg InnoFounder · NVIDIA Inception · Microsoft for Startups

Investment thesis

Clinical systems record isolated events while chronic risk develops continuously. Healthcare organizations need systems capable of modeling the patient as a trajectory.

Product

Medical Intelligence Agent (MIA)

MIA reconstructs the available longitudinal clinical record, identifies changes for review, uses PULSE to project biomarker trajectories with stated uncertainty, and connects findings to supporting observations and medical evidence before physician review.

Patient timeline — Longitudinal record reconstruction
PULSE prediction engine — Temporal modeling and forecasting
Clinical orchestration — Trained clinical orchestration
Evidence graph — Evidence and provenance
Physician review loop — Physician review

Explore the Platform

Go to market

Initial commercial wedge

Germany: MVZs, outpatient clinics and practice groups
Provider owners and medical directors as buyers; physicians as users
Next European markets: the Netherlands and Denmark
US primary-care and longitudinal-care groups as a further market

Technology

Where the depth sits — and why it compounds

The defensibility is not one model. It is an ordered system in which each layer depends on the one before it, and in which physician review returns into the system.

Longitudinal record: Years of the organisation's own laboratory results, diagnoses, medications and notes.
Patient representation: One ordered longitudinal representation every other component reasons over.
Forecasting and orchestration: Recurring biomarkers projected forward; the orchestrator decides what a case needs.
Evidence-linked findings: Each finding carries its supporting observations and the medical evidence behind it.
Physician decisions: Confirmations, corrections and dismissals captured in structured form.

Physician decisions return into the representation and the evaluation.Illustrative patient data

Internally trained orchestration model: MIA's orchestration layer coordinates specialized medical reasoning, predictive models, and retrieval across the available patient context.
Longitudinal patient representation: One ordered representation of the available clinical record that every other component reasons over.
Predictive biomarker models: Temporal models that project recurring biomarkers forward with quantified uncertainty against the patient's own baseline.
Evidence Graph: Findings are linked to the record entries they were derived from and to the medical literature, guidelines, and structured knowledge that support them.
Physician Review Loop: Physician decisions and corrections are captured in structured form and feed back into evaluation.
Model-agnostic architecture: Reasoning components can be substituted as the underlying model landscape changes, without rebuilding the clinical layer.

Traction

Where we are

Physician evaluation: Retrospective cases adjudicated with clinicians on findings, omissions, and usefulness.
Clinical evaluation partners: Evaluated with physicians and outpatient-care partners in Europe.
Product development: MIA combines longitudinal patient context, PULSE prediction, evidence-linked reasoning, and physician review.
Research progress: Documented evaluation methodology with held-out data, defined comparators, and uncertainty evaluation.
Institutional support: IFB Hamburg — InnoFounder · NVIDIA Inception · Microsoft for Startups · EDIH Hamburg · JUPITER AI Factory
Research computing infrastructure: Access gained in 2026 through EDIH Hamburg and the JUPITER AI Factory.

Business model

How it is sold

Buyer: Outpatient provider organizations, including MVZs, practice groups, and care networks, initially in Germany.
User: Physicians and clinical teams conducting longitudinal patient review, plus the clinical leads who prioritize it.
Pilot: A retrospective run on the organization's own historical records, adjudicated by their clinicians, followed by a controlled pilot in one agreed review step.
Contract path: Evaluation agreement and data-processing agreement, then a controlled pilot with defined success criteria, then a commercial agreement scoped to population and workflow.

Pricing is agreed per engagement and is not published.

Team

Founder thesis

A molecular microbiologist and former infectious-disease researcher with peer-reviewed publications, building the clinical and research core, alongside an operator responsible for commercial execution.

Meet the Team

Investor materials

Request Investor Materials

Materials for investor review are shared directly on request. Write to us with who you are and what you are looking at.

Include your fund or organization, your role, and the areas you would like to review. We reply with the current materials and propose a call.

Email us for investor materials

Email inquiries reach office@drmaxhealth.de. Never send patient data.