AI MODEL TRAINING & FINE-TUNING

Your data. A considered model.

Dataset preparation, model development, training and evaluation — with a clear route to inference and deployment.

Discuss an AI Project

From dataset to deployment.

TECHPROG / DEVELOPMENT APPROACH

  1. Dataset preparation & annotation

    Review permissions, clean and structure data, define annotation guidance and separate training, validation and test sets to reduce leakage.

  2. Model development & baselines

    Select a suitable approach for computer vision, deep learning or another agreed task. Establish a baseline before adding complexity; consider transfer learning where appropriate.

  3. Training & fine-tuning

    Run documented experiments, tune model parameters and track configurations. GPU-accelerated experimentation is scoped against data size, availability and cost.

  4. Evaluation & benchmarking

    Compare against agreed baselines, examine error patterns and document limitations. Metrics and acceptance criteria are chosen for the intended use.

  5. Inference, deployment & handover

    Prepare inference workflows or an API, assess runtime requirements and document reproducible setup. Deployment and monitoring scope is agreed per project.

Clear criteria. Honest evaluation.

Dataset suitability, computational requirements, timelines and evaluation criteria are assessed individually. We do not promise a specific accuracy, clinical validity or a guaranteed research outcome.

Medical AI prototypes are research systems, distinct from clinically validated tools. Any clinical use requires separate validation and appropriate review.

A scope we can explain.

We agree the tools, frameworks, data handling, deliverables and technical handover during scoping. Share a summary first; do not send confidential datasets or identifiable patient records through the enquiry form.

Explore academic support

YOUR NEXT PROJECT

Let’s build something that works.

Start a projectDiscuss your project