AI for healthcare
AI systems that answer from medical knowledge — in your AWS account, with no data leaving it.
For healthcare organisations that want to make medical knowledge available to patients or staff — without the risk of hallucination and without handing over data. We build RAG systems on Amazon Bedrock: answers always come 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.
Proof
We built our own healthcare product in production — the same stack we propose to you.
100%
AI extraction accuracy on packaging data (validation set, n=200)
$0.0006
cost per AI scan in production
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.
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.