Solutions · 05

Your data deserves
its own model.

Design, training, and fine-tuning of custom AI and deep-learning models, built on your data, for your use case, deployed where you need them.

4–10 weekssee the whole system

Architecture, training, fine-tuning, deployment. Your data, your model.

What this is

General-purpose models are remarkable, and generic. They don't know your domain's vocabulary, your edge cases, or your definition of correct.

When prompting stops being enough, we build the model that fits: fine-tuned on your data, evaluated against your ground truth, and deployed behind an endpoint you control.

Sometimes that means a small, fast, private model that beats a giant one at your specific task, at a fraction of the inference cost.

When you need this
Prompt engineering has plateaued below the accuracy you need
Your domain has language or patterns general models get wrong
Inference costs are scaling faster than usage
Data can't leave your infrastructure, so the model has to come to it

What we actually do.

The work itself
  • 01
    Architecture & selection

    A straight analysis of what the task needs: fine-tune, distil, or train from scratch, and when an API model is still the right answer.

  • 02
    Training & fine-tuning

    Reproducible training pipelines with experiment tracking, on our infrastructure or yours.

  • 03
    Evaluation suite

    Your ground truth turned into a benchmark, so 'better' is a number, not a feeling.

  • 04
    Deployment & inference

    Optimised serving (quantisation, batching, autoscaling) behind an endpoint your systems already know how to call.

How it runs
01Baseline

We measure what off-the-shelf models achieve on your task first. If that's enough, we stop there.

02Train & evaluate

Iterative training runs, each one scored against the benchmark.

03Deploy

The winning model shipped to your cloud, your servers, or ours.

What you get
  • Model + weights
  • Training pipeline
  • Eval suite
  • Inference endpoint
Timeline
4–10 weeks
Tooling we reach for
PyTorchTensorFlowHugging FaceDatabricks

We’ll tell you straight.

Fit check
A good fit if
  • A clear task with measurable success
  • Enough proprietary data to matter
  • Privacy, latency, or cost constraints that rule out APIs
Not a fit if
  • If a prompt and a good eval solve it, we'll tell you before you spend training money.
Where it connects

Custom models are only as good as the data behind them, and most useful with an agent in front.

03Data Preparation04Agentic AIthe whole system
04Agentic AI06Research & R&D

Start with
custom models.

Book a 20-minute call. We’ll tell you honestly whether this is the right starting point — or whether another door fits your problem better.

Book a call