Medical & Research AI
We turn complex medical and research workflows into practical AI solutions — starting with a feasibility assessment, and going to production only when a prototype has proved it should.
How an engagement runs
The situation
Most research and healthcare teams are not short of data. They are short of the hours needed to work through it.
More published work in a field than any team can read, screen and keep current with.
Protocols, reports, submissions and records whose useful content is locked inside prose and PDFs.
Tabular results next to free text, scans, notes and exports that never share a schema.
The same extraction, screening, formatting and cross-checking performed by hand, week after week.
Knowledge split between drives, mailboxes, databases and tools that do not talk to each other.
Re-typing, reconciling and reformatting — expensive in time and a reliable source of error.
Answers that exist somewhere inside the organisation but cannot be found when they are needed.
Time spent preparing information rather than interpreting it.
What AI can realistically do here
AI does not solve medicine. It can help teams process information, automate repetitive work, build internal tools, and make complex workflows measurably more efficient. That is the part we build — and the part worth scoping honestly before anyone writes code.
What we build
Each of these begins as a scoped prototype around a single workflow. What it becomes depends on what the prototype shows.
Custom AI systems designed around a specific research workflow, rather than a generic assistant dropped on top of it.
Systems for working with complex datasets and document collections — turning material that has to be read into material that can be queried.
These systems organise and surface information for qualified people to review. They are not built to make clinical decisions, and the specification says so in writing.
The repetitive half of a research workflow, handled by software — with a person kept in the loop wherever judgement is required.
Internal tools for healthcare organisations — aimed at operations, documents and knowledge, not at patient care decisions.
The scope is deliberate: operational and informational support for your team. Diagnosis and treatment stay with qualified professionals.
For organisations that have an idea but no answer yet to whether it is technically feasible.
Have a medical or research problem that might be solvable with AI? A prototype is how you find out.
Not sure which of these your problem is? That is what the first step decides.
Discuss Your ProjectCore process
Five steps, in this order, with a decision point after each one.
We start from the medical, research or operational problem in your own words — not from a technology shortlist. What takes too long, what gets missed, what has to be redone by hand.
We determine where AI can realistically help, what data and system access it would require, and which parts of the workflow are better solved by ordinary software — or left alone.
A working proof of concept built around one specific use case, running on representative data, so the question stops being theoretical.
We test the prototype against the real workflow and the criteria set in step 02 — output quality, coverage, throughput, cost per document or per query.
If the prototype proves useful, it becomes a production system: integrations, access control, monitoring, error handling, and a handover your team can build on.
Not every idea survives step 02, and some should not. Finding that out in a few weeks costs a fraction of finding it out after a year of development — which is why the assessment comes before the proposal.
You do not need to know which technology you need. Start with the problem — the workflow that takes too long, the information nobody can find, the analysis that never gets done. We evaluate the possible AI applications and tell you what would be technically realistic, including when the honest answer is that this is not an AI problem.
Tell Us About Your ProblemExample applications
Illustrative applications of the capabilities above. These are examples of what such systems look like — not descriptions of delivered client work, and not claims about results.
An AI system that lets a research team search and interact with its own scientific knowledge base — asking questions in plain language and getting answers that cite the underlying documents.
A pipeline that extracts and structures information from large collections of documents, turning a folder of PDFs and reports into records that can be filtered, compared and exported.
An AI-assisted workflow that removes repetitive information-processing steps from a recurring research task, leaving the interpretation to the researchers.
An organisation-specific assistant connected to approved internal information sources, so staff get consistent answers from material the organisation has already authorised.
We publish delivered work only where the client has agreed to it being published. Nothing on this page is presented as a medical case study, and no results are claimed.
Why custom
Every research and healthcare workflow is different. A generic AI tool has no way of knowing:
So we build the system around the actual problem — and keep it narrow enough to evaluate.
A narrow system that works beats a broad one that almost works. We start with the single use case carrying the most manual load.
Success criteria are written down before the build, and the prototype is measured against them on your data.
Documentation, architecture notes and access to the code. Your team can extend the system without us.
Where a model is unreliable for your task, that goes into the report — before it goes into production.
Stack
What these systems are made of. The outcome matters more than the framework — this section is here so your technical people can see we speak the same language.
Security & privacy
In medical and research work, how a system handles data decides whether it can be used at all. We treat that as an architecture question from the first conversation, not a checklist at the end.
Data flows, storage and processing boundaries are defined before the build starts and written into the specification.
Role-based access, so that people and services reach only what their role requires.
Authenticated access to every interface, with session and credential handling appropriate to the deployment.
Transport encryption throughout; encryption at rest applied according to the sensitivity of the data and the environment.
Separation between projects, tenants and environments, so test work never touches production material.
Scoped keys, logged calls and rate limits — including toward the model providers a system depends on.
Cloud, your own infrastructure, or open-weight models running entirely inside your environment when the data cannot leave it.
What we do not claim
We do not advertise HIPAA, GDPR or any other compliance status as a blanket badge. Compliance is a property of a specific system, its hosting and the processes around it — not of a vendor. If your project requires a particular regime, we scope that work explicitly, state what we can and cannot cover, and put it in writing before the project starts.
Who we work with
The common thread is a workflow that has outgrown manual handling — not a particular size of organisation.
Large volumes of literature, documents and data, and a workflow that repeats.
Teams that need an internal tool built around their own knowledge and process.
Internal workflows, document handling and knowledge retrieval that could be automated.
Complex scientific data and research workflows that outgrew spreadsheets.
Document analysis, knowledge management and AI-assisted research support.
Custom AI technology, or a prototype that has to prove feasibility before the next round of work.
Project inquiry
Tell us what you are trying to solve. We will help determine where AI can realistically fit.
What you send is used to answer your inquiry. It is not sold, and it is not added to a marketing list — see the Privacy Policy.
FAQ
Yes. Projects range from a focused prototype around a single workflow to a production system used daily by a team. Which one is appropriate depends on the use case, the data available and how much of the workflow you want covered — that is what the first assessment establishes.
Yes, and this is the most common way engagements start. The first step is understanding the problem and determining whether AI is technically appropriate for it. Some problems are a good fit, some are better solved with conventional software, and some are not solvable with the data that exists. You get that answer in writing either way.
Potentially — it depends on format, accessibility, quality, volume, the security requirements attached to it, and the scope of the project. Part of the evaluation step is looking at a representative sample and telling you what is realistic with it, including whether preparation work is needed first.
Yes, and we usually recommend it. A prototype validates feasibility on real data against criteria agreed in advance, before anyone commits to a larger implementation. It is the cheapest point at which an idea can fail.
No. We develop software and AI systems. Medical decisions remain the responsibility of qualified professionals and the organisations they work in, and we scope every system so that it supports those people rather than substituting for them.
We do not claim compliance that has not been established for a specific system. Compliance depends on the architecture, the hosting, the contracts and the operational processes around it — not on a vendor label. If your project requires HIPAA or another regime, say so in the form: we will scope that work explicitly and state in writing what is and is not covered before the project starts.
Yes. Depending on the architecture, AI can be integrated through APIs, internal tools, databases, document stores or existing workflow systems — usually as a layer alongside what you already run, rather than a replacement for it.
Sending your problem through the form costs nothing, and neither does our written reply about whether it looks feasible. A scheduled working session with an engineer is billable and prepaid — that is engineering time rather than a sales call. Project pricing is scoped once the problem is understood, not before.
Start with the problem. We will explore what is technically possible.
Interested in collaborating on AI, healthcare or medical research — as a research group, a vendor, or a technical partner?
Aunimeda is a software company. We build AI systems, research tools and automation. We do not provide medical advice, diagnosis or treatment, and the systems we build are designed to support qualified professionals and organisations — never to replace their judgement.
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