AI for healthcare
AI systems that answer from medical knowledge — in your AWS account, with no data leaving it.
For healthcare and pharmacy organisations — clinics, hospitals, pharmacies — that want to make medical knowledge available to patients or staff with reduced hallucination risk and without handing over data. We build RAG systems on Amazon Bedrock: the system is constrained to answer from your documents and cite the source. Your data stays with you.
The problem
A generic AI chatbot in healthcare is not an inconvenience — it is a risk.
A public language model answers from what it "remembers" from training. In most contexts that is enough. In healthcare an "almost right" medical answer is dangerous — the model can sound confident and be wrong at the same time. There is no source to hold on to, no trail to examine.
No citations means no auditability. No auditability is a risk — clinical and legal. On top of that comes the data question: medical records, patient data, internal protocols cannot travel to public APIs. An AI system in healthcare has to run on controlled sources, cite specific passages and keep data within your infrastructure.
Use cases
What you can build — on controlled sources, with an audit trail.
Every use case rests on the same mechanism: the model answers only from the documents you supply.
Knowledge base of procedures and guidelines
The system searches your internal protocols, standards of care and clinical guidelines — and cites the specific passage rather than paraphrasing from memory. Staff get the answer and a pointer to the document it came from.
Patient information support
The chatbot answers questions based on educational materials you have approved — leaflets, treatment schemes, FAQs. It does not diagnose; it provides information from controlled sources and points to where to look further.
Search across medical documentation
Instead of manually searching treatment histories or administrative documents — a natural-language query, an answer with its location in the document. The data stays in your environment.
Multilingual patient communication
A system based on approved content can answer in many languages — without ad hoc translation by staff and without the risk of distorting a medical message.
Risks and compliance
The AI Act classes some AI systems in healthcare as high-risk — worth understanding before you deploy.
Classification under the AI Act
AI systems supporting clinical decisions may be classed as high-risk. That means obligations — technical documentation, human oversight, risk management. We build the architecture with that in mind, not as a later add-on.
Security of personal and medical data
Patient records and medical data fall under GDPR — with a stricter regime for sensitive data. In the architecture we use, data is processed solely in your AWS account, is not used to train models and is not shared with the model providers.
Human oversight and auditability
Citations are not just convenience — they are an audit requirement. Every system answer is tied to a source. Logs of queries and answers let you examine what the system said and on what basis.
The limits of the system — what it does not do
These are information-support tools, not medical devices and not diagnostic systems. The system is not intended to diagnose or take clinical decisions — it is constrained to answer from approved sources and cite them, and the decision always rests with a human.
Unsure how the AI Act classifies your systems? Start with an EU AI Act compliance audit.
Proof
We built our own healthcare product in production — the same stack we propose to you.
RAG
versioned knowledge, source citations and controlled refusal
$0.0006
rounded inference-layer cost per AI scan
302,516
drug-interaction records in the production knowledge base
The numbers come from the production environment of mojApteczka — our own digital product in the healthcare domain, built entirely on Generative AI and RAG with citations, with automated evaluations of answer quality. In healthcare, citations and evaluations are a requirement, not a decoration.
See the full case studyQuestions
Before you ask
No. We work solely on Amazon Bedrock — models run in an isolated environment in your own AWS account. Your documents are not used to train any third-party models and do not leave your infrastructure.
No. The systems we build are information-support tools based on controlled sources — not medical devices, not diagnostic systems. They answer from the documents you supply and cite the source. The clinical decision always belongs to a human.
Some healthcare AI use cases may be classed as high-risk systems. That means concrete organisational and technical obligations. We analyse the classification at the audit stage — so you know what you are getting into before you start.
Always priced by scope — there are no off-the-shelf price packages. We start with an audit that defines the real scope and an estimate. No commitment to a full project up front.
The audit usually takes 2–4 weeks. After that the timeline depends on the scale and complexity of the data. We can start with a PoC — you verify it works on real documents before deciding on a full rollout.
Have medical documentation, procedures or clinical knowledge your AI should answer from?
Let's start with an audit — we'll check what you have, how to index it and which architecture to choose. No experiments on your production budget.