AI for healthcare teams, from clinical document structuring to assistive decision support.

Yes, when the system is designed correctly. Health data is special-category and prohibited from processing unless an Article 9(2) condition applies, most commonly explicit consent, public-interest health, or the provision of healthcare under a contract with a regulated professional. We deploy on EU regions by default, sign DPAs that include zero-retention and no-training clauses for inference traffic, pseudonymise at the boundary, and produce a DPIA-ready data-flow diagram before any system goes live. Article 9 is met when the technical and contractual stack is built around it, not bolted on. Where your information-governance committee or DPO needs additional artefacts, we ship them as part of the build, not as an after-thought.

No. Impetora ships assistive systems that surface evidence, structure documents, and accelerate operations around the clinical decision. Anything that performs a function intended for diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of disease falls under the Medical Device Regulation in the EU and the FDA SaMD framework in the US, and requires a conformity-assessment pathway with a Notified Body or pre-market submission. Where a client engagement requires that pathway, we say so explicitly during discovery, scope the regulated work into the proposal, and partner with the appropriate quality-management partner. We never ship clinical-decision-making AI on a non-regulated route.

The EU AI Act classifies a number of healthcare-adjacent uses as high-risk under Annex III §3, which triggers obligations on risk management, data governance, technical documentation, record-keeping, transparency, human oversight, accuracy, robustness, and cybersecurity. We build conformity-assessment scaffolding into the system from week one: an ISO 42001-aligned governance memo, the technical documentation pack the regulation requires, an append-only audit log, and a documented human-in-the-loop step for any output that affects a patient or care decision. If your specific use case is limited-risk rather than high-risk, we ship the proportionate controls, but we never default to less than the regulation requires.

A first engagement targets one workflow with a measurable baseline, runs 4 to 12 weeks to production, and lands as a single signed-off system inside one EHR, RIS, LIS, or operations surface. Common scopes are: clinical document extraction across one or two document types; patient triage automation across one or two digital channels; medical coding automation across one or two specialties; consent and audit-readiness automation. Submit a project with the workflow you have in mind and the rough volume, and we scope and price the discovery phase before any code is written.

Yes. The delivery layer is built around your data surface, not the other way around. We ship FHIR-native integrations where the source system supports them, HL7 v2 bridges where it does not, and queue-based bridges with idempotent writes for legacy systems. We integrate with the major hospital information systems and with the digital health platforms our clients build on. The audit log writes regardless of where the data lands, so you can prove lineage even when the downstream system cannot.

Production-grade deployments see code-level error rates of 0.4 to 0.7% on routine specialties after the first three weeks of evaluation tuning, against typical human-only baselines reported in industry studies. Accuracy depends on specialty, documentation quality, and the breadth of the evaluation set. We do not claim a single accuracy number across all specialties. We baseline first, target a specific delta against your current process, and report against it weekly through the pilot. A coder always signs off; the AI structures and proposes, the human decides.

By default, all processing and storage runs in EU regions on infrastructure under EU jurisdiction. We support regional pinning when a regulator or contract requires it (Germany-only, France-only, Lithuania-only, US-only). Original documents land in immutable EU object storage with hashes recorded in the audit log. We do not train any model on your records, full stop. If your contract requires US-resident processing for a US-only deployment, we expose that as an explicit configuration toggle, never a default.

Pricing is set after the discovery sprint, against your specific workflow, integration surface, and regulatory tier. We do not publish a flat rate because the scope variation across healthcare AI is wide: a non-clinical document extraction system on a uniform corpus is a different build from a system that touches the regulated SaMD boundary. Submit a project with the workflow and rough volume, and we come back with a discovery proposal within one business day. Production deployments that sit on the regulated SaMD boundary include a conformity-assessment work package which is scoped explicitly in the proposal.