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AI Solutions for Healthcare

We build custom AI systems for healthcare providers, digital health startups and medical platforms: private LLMs that run on your own servers, medical computer vision for imaging, and wellness products that people use every day.
Discuss your use caseView case studies
  • Private, on-premise and air-gapped deployment
  • Built around your workflow
  • You own the code and the models
Healthcare AI we have shipped
3 areas

Input

  • Internal reports
  • Trial updates
  • Research documents

On your servers

  • Retrieval over a local index
  • Quantized Mixtral 8x7B

Output

  • Answers with sources
  • 5–7 seconds
  • No external APIs

Case study: PharmaBrain for Atacana

Solutions and use cases

Healthcare AI solutions we have shipped

Each of these is running work, not a concept: a private research assistant, an orthopedic screening pipeline, and AI inside consumer wellness apps.

  • Private LLMs for healthcare

    A research assistant that answers questions over your own documents, running on your servers with no calls to outside APIs.
    Answers with sources in 5–7 seconds
    PharmaBrain case study

  • Medical imaging and 3D reconstruction

    Medical computer vision that segments bones, finds anatomical landmarks and estimates 3D geometry from a standard 2D X-ray.
    Clinical AI analytics: CPAK, mHKAA, LDFA, MPTA
    X-ray to 3D case study

  • Wellness recommendations

    Meal, exercise and meditation recommenders that adapt to a user's goals, restrictions and progress, on web, iOS and Android.
    Five AI modules in one app
    Hā Health case study

  • Personalised nutrition

    A meal-planning agent that turns an intake form into weekly plans, everyday recipes and a shopping list, delivered as a PDF by email.
    Guardrails on ingredients and quantities
    gbMeals case study

  • Medical document processing

    Extraction and classification for the paperwork around care: lab reports, prescriptions and claims, validated before anything is written back.
    95%+ field-level accuracy on typical documents
    Document AI

  • LLM safety guardrails

    Input and output filters, retrieval that keeps answers tied to real sources, and human review where it matters.
    For chatbots, documentation and decision support
    Guardrails guide

Our work

Three healthcare builds, three very different constraints

A medical research assistant that never goes online

Atacana's database grows daily with news reports, clinical trial updates and drug discovery announcements, and none of it could go to a public cloud. PharmaBrain indexes those documents on local servers and answers questions from them using a quantized Mixtral 8x7B model.

Because retrieval runs first, answers stay tied to the documents rather than to whatever the model remembers.

  • Runs entirely on the client's own servers
  • 8-bit quantization for 5–7 second answers
  • No calls to external APIs
Read the PharmaBrain case studyPrivate and on-prem LLMs
Research assistant · local server
Air-gapped

Incoming reports

  • trial-update-ax214.pdf
  • news-digest-0412.html
  • pipeline-review-q2.pdf
  • conference-abstracts.pdf
  • regulatory-notice-17.pdf

Vector index on local server

5 new documents chunked and embedded

Offline

1/4New reports are chunked, embedded and added to a vector index on the client's own servers. Nothing is sent to an outside service.

Orthopedic screening from an ordinary X-ray

A CT scan costs several times more than an X-ray, which can delay diagnosis. For a hospital in the US we built a pipeline that segments the femur and tibia, finds landmarks such as the femoral condyles and the flexion-extension axis, estimates 3D bone geometry with a GAN-enhanced network, and calculates the alignment angles used in preoperative planning.
  • CPAK, mHKAA, LDFA and MPTA calculated automatically
  • Approximately 75–80% accuracy, for initial screening
  • Deployed on AWS for clinical turnaround
Read the X-ray to 3D case study
Standing long-leg X-rays with the mechanical axes drawn, and a knee close-up marking the LDFA and MPTA angles
Leg alignment measured from a standard X-ray: mechanical axes, LDFA and MPTA.

Wellness AI people use daily

Hā Health, an AxcelerateAI client, wanted one app that plans meals, workouts and meditation. We built separate recommenders for each, plus an agentic recipe creator with anti-hallucination guardrails and a meal plan generator that produces a shopping list with prices compared across retailers. The same AI services run behind the web app, iOS and Android.
  • Recipes checked against dietary restrictions before users see them
  • Routines that adapt as users log progress
  • Plans turn into a priced grocery list
Read the Hā Health case studygbMeals AI nutritionist
Three rounded panels showing a woman exercising at home, a basket of fresh vegetables and fruit, and a man relaxing with headphones
The three habits the Hā app plans for: movement, food and mindfulness.

Safety

Guardrails, not good intentions

Healthcare is high stakes: an unchecked model can leak identifiers or state something that is confidently wrong. We treat safety as engineering, with layers you can test and log.

The same layers apply whether the model drafts clinical documentation, answers patient questions or supports a clinical decision. Our engineering guide covers the details, including the regulatory questions to ask early.

Safety guardrails for LLMs in healthcareHow we handle data and IP
  1. Stage 1

    Filter what goes in

    Prompts are scanned for identifiers and for requests the system should not answer, such as asking for a diagnosis. Identifiers are removed or pseudonymised before the model sees them.
    Clean, minimal prompt
  2. Stage 2

    Run the model where the data lives

    Self-hosted or private-cloud models keep records inside your environment. Nothing is sent to a consumer chat interface.
    No data leaving your network
  3. Stage 3

    Ground answers in real documents

    Retrieval puts the source text in front of the model, so answers cite documents instead of recalling them. This is the pattern behind PharmaBrain.
    Answers with sources attached
  4. Stage 4

    Filter what comes out

    Output checks catch identifiers, unsafe advice and claims that do not match the retrieved sources. Blocked responses are logged with the reason.
    Auditable refusals
  5. Stage 5

    Keep a human in the loop

    A qualified person reviews anything that touches care, and every prompt, response and guardrail trigger is logged so decisions can be traced later.
    Reviewed, traceable output

Built for trust

Deployed inside your compliance environment

We do not ask you to move patient data to us. We deploy into your environment, for example SOC 2 or HIPAA-aligned infrastructure, and follow your security policies.

Security, data handling and IPSovereign and private AI

  • Your infrastructure

    Private VPC on AWS, GCP or Azure, air-gapped on-premises servers, or edge devices.

  • Encryption in transit and at rest

    The pipelines we build encrypt data as it moves and where it is stored.

  • Role-based access

    Access to project data is limited to the people who need it for the work.

  • No training on your data

    Your data is used for your project only, not to train other models, unless you agree.

  • You own the output

    Source code, trained model weights and deployment configuration are handed over at the end.

  • Paperwork first

    We sign your NDA before data is shared and answer security questionnaires or a DPA.

How we work

From use case to production, with a checkpoint at every step

Healthcare teams work with us because the system is built for their workflow and validated against it before it goes anywhere near production.

  • Custom systems built for your workflow, not off-the-shelf tools
  • Secure cloud, private infrastructure or fully on-premise deployment
  • Production APIs and pipelines, monitored and maintained, not just prototypes
  • Encryption, role-based access and no training on your data without permission
  • Source code, model weights and deployment configuration handed over to you
  • Output validated against your own workflows and reviewed by your specialists

  1. Step 1: Use case discovery

    30 minutes

    We look at the data you have, the decision it should support and what is realistic, then agree the success criteria.

    • NDA on request

  2. Step 2: Proof of concept

    4–6 weeks

    A working system on your own data, measured against the benchmark your team already trusts.

    • Accuracy targets agreed up front

  3. Step 3: Production deployment

    Ongoing

    Deployed into your environment, monitored, and retrained as your data and workflows change.

    • You own the IP

Not sure which use case to start with? An AI opportunity audit maps the options in 3–5 business days.

FAQ

Questions, answered

What healthcare and digital health teams ask us first.

Need the model to stay inside your network? See private and on-prem LLMs.

  • We deploy into your compliance environment (for example, SOC 2 or HIPAA-aligned infrastructure) and follow your security policies. The pipelines we build encrypt data in transit and at rest, access to project data is role-based, and we do not use your data to train other models without your permission. Where data cannot leave your network, the system runs on your own servers.

Clients

Teams we build with

Related

Keep exploring

Case studies

Guides from our engineers

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