AI Consulting for Healthcare

Privacy is an architecture decision we make before any model is trained. As a German company we hold ourselves to European data-protection standards (GDPR) by default, for every client worldwide: data processing agreements, minimal data collection, pseudonymization or de-identification where the use case allows it, and deployments where the data stays in your environment.

For LLM-based assistants we deploy private, hosted models, so clinical knowledge and patient-related text never flow to third-party AI providers. For US clients, we design solutions to align with HIPAA requirements — we are not a certification body, but privacy-by-design is how we build regardless of jurisdiction.

It depends on the intended use, and we are upfront about that. Most of what we build — documentation assistants, intake automation, claims processing, capacity forecasting, compliance monitoring — supports administrative and operational workflows and typically falls outside medical device regulation.

Where a solution informs clinical decisions, such as image analysis, the regulatory pathway depends on exactly what the tool claims to do and how it is used. We design these systems as decision-support tools under expert review, and we help you assess the likely regulatory classification early — before development, not after. We do not provide legal or regulatory advice, and we recommend involving your regulatory counsel for classification decisions; what we bring is the technical documentation, validation evidence, and architecture choices that make that pathway navigable.

Only if it saves them time in the first week — so that is the design constraint. We involve clinical and administrative staff from the discovery phase, fit the AI into existing workflows rather than adding new screens, and keep humans in the loop: the AI drafts, counts, and flags; your people review and decide.

Adoption also survives handover better when the team understands the tool, which is why engagements can include practical AI training for the people who will live with the system.

Against expert-labeled data, measured before deployment and monitored after. Every proof of concept defines its accuracy metrics upfront — the ones that matter for the task, not a single flattering number — and is evaluated on held-out data your experts have annotated. Our pill counting system reached 99.9% accuracy through exactly this discipline.

Just as important: we design for the failure cases. Systems flag low-confidence outputs for human review instead of guessing silently, so accuracy in production is a supervised property, not an assumption.

Integration is usually where healthcare AI projects live or die, so we scope it in the discovery phase, not at the end. We build against your existing landscape — EHR/EMR, PACS, LIS, billing — using the interfaces your systems expose, and we design with healthcare interoperability standards such as HL7 and FHIR in mind where your systems support them.

Where a clean interface does not exist, we say so early and propose the pragmatic path: document-level automation, export-based pipelines, or a phased integration — rather than promising a seamless connection that your vendor landscape cannot deliver.

Often still enough to start. Modern approaches — transfer learning from pre-trained medical and general-purpose models, data augmentation, and large language models that need configuration rather than training — deliver strong results with far less data than classical machine learning required. Our ocular volume estimation work is research-grade deep learning built on a specialized medical dataset, not millions of images.

During discovery we assess what you actually have and tell you honestly whether it supports the use case. If the data is not there yet, the first deliverable becomes a realistic data collection and labeling plan — not a model that overpromises.

Every project is unique, so pricing depends on the complexity of the problem, the state of your data, and how deeply the solution must integrate with your clinical and administrative systems. AI Superior offers fixed AI development plans with a guaranteed outcome at a predefined price — PoC, MVP, and Product as separate, evidence-based decisions. Contact us for a quote based on your project.

No — and we would decline a project scoped that way. Everything we build in healthcare is designed as a support layer: it drafts documentation for a clinician to edit, quantifies what an expert then interprets, counts and verifies so staff review exceptions, and forecasts so managers plan better. The accountable decision stays with the qualified human, and the system architecture — confidence thresholds, review queues, audit trails — is built to keep it there.

Yes. We are headquartered in Darmstadt in the Frankfurt Rhine-Main region, with a second office in Berlin, and work with providers and healthtech companies internationally. Projects run remotely with structured communication at every stage, and our GDPR-grade approach to data protection travels with us to every engagement. Reach us at info@aisuperior.com or +49 6151 7076909.