Computer vision
Models that look at photographs the way your best inspector does: finding damage and defects, counting objects, checking quality, on every image, every time, without getting tired.
Machine learning
Models trained on your business.
We were building machine learning for industrial clients years before the AI wave: vision models like DeepLabV3 finding damage in photographs, counting objects and doing quality control. We train models on your data to predict, spot and score the things your business runs on.
Trusted by established UK businesses
What it is
Your data already knows the answer. We teach it to speak up.
Machine learning is different from the chatbot kind of AI. Instead of language, it learns from your history: photographs your inspectors have graded, orders you have shipped, faults you have fixed. Then it applies that learning at a speed and consistency no team can match.
It is the right fit when the job is looking, counting, predicting or ranking: checking product photos for damage, forecasting next month's demand, spotting the invoice that doesn't look right, or scoring which leads deserve a call first.
We have been doing this since before the AI wave, and the models we train belong to you, trained on your data and built into the software your team already uses. Read exactly how we use AI.
What you get
The kinds of models that pay for themselves.
Real capabilities we have trained and shipped for clients, each tied to a number the business cares about.
Forecasting
Demand, sales, capacity, stock and cash, predicted from your own history so you can plan with numbers instead of gut feel, and see busy periods coming before they arrive.
Anomaly detection
The odd transaction, the sensor reading that's drifting, the job that doesn't fit the pattern. Flagged early, while it's still a question rather than a cost.
Classification and scoring
Lead scoring, risk grading, prioritisation. Models that rank the queue so your team spends their time where it will actually pay off.
Recommendations and matching
The right product for the customer, the right engineer for the job, the right price for the quote. Matching that learns from what worked before.
Labelling and data pipelines
The unglamorous part that makes it all work. We set up labelling workflows and data pipelines so training data stays clean, and your team can keep improving the model without us.
How we work
From your data to a model you can rely on.
No black boxes and no leaps of faith. We prove accuracy on your real data before anything goes near production, then keep measuring once it's there.
1
Data first
We audit what you have: photos, records, history. Then we set up the cleaning and labelling needed to make it trainable, honestly flagging any gaps.
2
Train and prove
We train and tune the model, then test it against real examples it has never seen. You see the accuracy numbers before you commit to the build.
3
Deploy into your tools
The model goes into the software your team already uses, not a separate dashboard nobody opens. Predictions arrive where the work happens.
4
Monitor and retrain
Accuracy drifts as your business changes, so we watch it in production and retrain when the numbers say so. The model stays sharp instead of quietly rotting.
FAQ
Questions we hear about machine learning.
Straight answers on data, accuracy and what it takes to get a model working for your business.
How much data do we need to get started?
Less than you might think, but it depends on the job. Some models need thousands of labelled examples, others work well with hundreds. We're honest early about whether you have enough and how to fill any gaps, so you never invest in something that can't work.
Our data is messy. Is that a dealbreaker?
No, it's normal. Almost every project starts with data spread across spreadsheets, systems and photographs in inconsistent formats. Cleaning, structuring and labelling it is part of the work, and we build pipelines so it stays clean from then on.
How accurate will the model be?
We won't promise a number before seeing your data. What we do promise is honest measurement: we test against real examples, agree what accuracy is good enough for the job, and tell you plainly if it falls short and what it would take to close the gap.
Do we need our own data scientist?
No. We handle the modelling, training and deployment, and we build the result into software your team already uses. If you do have technical people, we work alongside them and hand over knowledge as we go.
Is our business data safe?
Data protection is built into how we work. We're clear about where your data is processed and stored, we keep it within trusted UK and EU infrastructure where that matters, and models trained on your data belong to you. We handle everything in line with UK GDPR.
Does the model stay accurate over time?
Only if someone watches it. Products, seasons and processes change, and accuracy quietly drifts if a model is left alone. We monitor performance in production and retrain when the numbers say so, not when something finally breaks.
Related services
Where machine learning fits into the bigger picture.
A model on its own changes nothing. Built into the right software, it changes how the business runs.
AI development
Assistants, document readers, drafting tools and agents built on large language models. The other half of this story.
Learn more →Custom software
Bespoke systems built around your processes, giving your models somewhere useful to live.
Learn more →Legacy software modernisation
Bring ageing systems up to date so they can hold the data and connections your models need.
Learn more →Book a discovery call
Talk through what your data could do in a free, no-obligation call with our team.
Book a call →Got data doing nothing?
Tell us what your business collects: photos, orders, jobs, readings. We'll give you honest, practical advice on what a model could do with it, with no obligation.