Custom Model Development
When an off-the-shelf AI tool can't handle your data or your edge cases, we build the model that can. You keep the rights to what we build.
Custom ModelWhat this includes
What that means once we're actually building:
Built for your edge cases
Tested against your team's real exceptions, not a demo.
Data boundaries, set early
Storage, processing and access settled before we start.
Cheaper as you scale
Owned models cut your per-call cost as volume grows.
A real moat, not a prompt
Competitors can copy a prompt, not your model.
Built with
How a model gets built
Name the failure
We work out whether the problem is missing knowledge, inconsistent output, or a task the model genuinely cannot do. Each one has a different and much cheaper answer.
Settle the data boundaries
Where data lives, who can reach it, how long it is kept, and what happens at the end. These are cheap questions now and painful ones later.
Build the evaluation set
Real inputs with correct outputs, drawn from your awkward cases as well as your ordinary ones. Without it there is no way to tell whether a change helped.
Train, measure, repeat
We only climb to a trained model when the simpler options are shown not to hold, then measure every change against the same set.
What you get
A model tested against your real exceptions, not a demo dataset
An evaluation set you keep, so future changes can be measured
Data handling agreed in writing before any training starts
Ownership of the model and the work that produced it
A straight answer if a prompt or retrieval would have done the job for less
Lower per-call cost than a general-purpose API as volume grows
Common questions
Do we actually need a custom model?
Often not, and we will say so. Prompting changes behaviour, retrieval adds knowledge, and fine-tuning fixes format and consistency. Training from scratch is worth it when your data is genuinely unlike the public web, when per-call pricing stops making sense at your volume, or when the model is the product rather than a feature of it.
How much data do we need?
It depends far more on quality than quantity. Real inputs with correct outputs, covering the awkward cases, beat a large pile of clean but unrepresentative examples. We look at what you have before promising anything.
Who owns the model?
You do. That is much of the point of having one built rather than renting a general-purpose tool. A competitor can copy a prompt in an afternoon and cannot copy a model trained on data they do not have.
Can it run without sending data to a third party?
Yes, and that is one of the common reasons to build rather than buy. It changes the architecture, so it is a decision we make at the start rather than retrofit.
How do we know it is working?
By the evaluation set built at the start. Every change gets run against the same cases, so improvement is measured rather than felt.
Often paired with
AI Agents
Put the model in front of customers, with your team still holding the judgment calls.
Read more →AI Automation
Connect the model to the tools where the work actually happens.
Read more →Prompt, retrieval, fine-tune or train
How we decide which of the four your problem actually needs.
Read more →Thinking about Custom Model Development?
Tell us where it hurts and what you've already tried. We'll reply within one business day with a straight answer, not a sales pitch.