Most businesses do not need a research lab. They need a specific, repetitive judgement made faster and more consistently than a person can make it all day. That is where machine learning earns its cost, and it is the kind of work we take on.
Where AI actually pays for itself
- Document and invoice extraction — pulling structured data out of PDFs, scans and photographs so staff stop retyping it.
- Demand and inventory forecasting — using your own sales history rather than generic models.
- Search and recommendations — helping customers find the right product or page inside a large catalogue.
- Retrieval-augmented generation (RAG) — assistants that answer from your own documents, policies and records and cite where each answer came from, instead of a generic chatbot that invents them. The gap between a RAG demo and one that holds up on a real document set is mostly retrieval quality, and that is where the engineering effort goes.
- Classification and routing — sorting incoming enquiries, tickets or applications to the right person automatically.
How we approach it
We start by asking what decision the model is supposed to improve and how you will know whether it worked. If that cannot be answered clearly, the project is not ready, and we will say so before taking your money.
From there the work is ordinary engineering: understand the data you already hold, establish a baseline a simple rule could achieve, and only add complexity where it beats that baseline measurably. A model that is 3% better but nobody can operate is not an improvement.
Putting models into production
A model is worth nothing until it runs reliably against live data. We build the surrounding system — data pipelines, APIs, monitoring, retraining and rollback — so the thing keeps working after launch. That surrounding system is usually the larger part of the job, and we price it honestly rather than treating it as an afterthought.
Being straight about limits
AI is a newer part of our practice than website and software development, which we have done since 2006. We will tell you plainly which parts of your problem are well-understood engineering and which are genuinely uncertain, and we would rather scope a small paid pilot than promise an outcome we cannot yet evidence.
Frequently asked questions
- Do we need a large amount of data to start?
- Less than people expect for many tasks. Document extraction and classification can often start with a few hundred labelled examples. Forecasting needs history — usually at least a year or two of consistent records. We can tell you after looking at what you hold.
- Will our data be sent to external AI providers?
- Only if you agree to it. We will set out which parts of the system would use a third-party model and which can run on infrastructure you control, along with the cost and privacy trade-off between the two, before anything is built.
- Can you add AI features to our existing software?
- Usually yes. Most of our AI work attaches to systems that already exist rather than replacing them. We have built and maintained business software for years, so integrating with an existing database or ERP is familiar ground.
- How do you charge for this?
- Typically a fixed-price discovery and pilot phase first, so you can judge the results before committing to a full build. Contact us and we will scope it against your actual data.