Machine learning development UK businesses can put into production: recommendation systems, predictive models, classification, anomaly detection, and NLP built as production systems with the data pipeline, the evaluation, and the monitoring that keep them working after launch.
The accuracy looked good on a clean sample and everyone was pleased. Then it needed a data pipeline, a serving layer, monitoring, and someone to own it, and none of that was scoped. The notebook is still in a repo somewhere. Nothing about the business changed.
Machine learning is a data problem long before it is a modelling problem. Not enough examples, no labels, leakage between train and test, or a target variable nobody agreed on. Teams discover this in month three, after the budget is committed, instead of in week one when it is cheap to know.
A model deployed without drift detection degrades as the world moves away from its training data. Fraud patterns change, users change, the catalogue changes. Performance decays month over month and the first person to notice is usually a customer, not your dashboard.
Our machine learning development reaches production, with the pipeline and monitoring that keep the model honest afterwards.
See what you get →Every build ships with the same core package. It is the baseline any machine learning development UK businesses pay for should meet, whether it recommends, predicts, classifies, or flags anomalies.
Whether your data can support the model you want, delivered in week one. Volume, labels, leakage, bias, and the target variable, examined before anyone trains anything.
Ingestion, cleaning, feature engineering, and versioning, running on a schedule rather than on someone's laptop.
Benchmarked against a baseline you can understand, on a holdout set that reflects reality, with the metrics that matter to the business rather than to a leaderboard.
Deployed for batch or real-time inference with latency targets, versioning, and rollback when a new model underperforms.
Performance tracked in production against live outcomes, with alerts when the model degrades rather than a quiet decline nobody sees.
How the model works, what it assumes, how it fails, and how to retrain it. Written so your team can own it after we leave.
If any of these describe you, we will tell you on the first call rather than waste your time or ours.
Founder at Seconddate
Foundry 5 really impressed me with their work on my AI-powered dating app. They nailed the UI/UX, starting with core app screens and then translating that vision to the web. The React site they built is sleek and polished - exactly what I was hoping for. What I appreciated most was their thoughtful approach and how easy they were to work with. They got the job done without needing me to micromanage.
CEO at StreaksAI
Foundry 5 surpassed all expectations in delivering our web development product swiftly and flawlessly. Their speed and eye for detail, especially under tight time constraints, showcase their commitment to excellence.
CEO, Xcelsior Capital
Working with Foundry 5 has been a great experience from start to finish. They seamlessly translated our vision into a website that not only showcases our brand and operations but also enhances our online presence as an investor in natural resources. Nothing was too much for them and they always put the client first.
CEO of Ove
Working with Foundry 5 to create the Ove app has been an absolute pleasure. Their professionalism, work ethic, and willingness to always go the extra mile has really impressed me. I would recommend them to anyone.
Loom Founder
Foundry 5 have gone above and beyond to bring my vision of Loom to life. Not only this, but they continue to be an integral part of our team. They are lovely people to work with, and I recommend them to anyone looking for a software partner. Particularly for non-technical tech founders (like me!) who need to ensure that their business is in safe hands.
Chief Product Officer Gather
The team has been instrumental in driving both the design and development of Gather, pairing a proactive, highly responsive workflow with the technical depth needed to handle our platform's complexity. Their partnership continues to move the product forward in a reliable and impactful way.
| Feature | Foundry 5 | Data science freelancer | In-house |
|---|---|---|---|
| Data feasibility answered before the build | |||
| Model reaches production, not a notebook | |||
| Reproducible training pipeline | |||
| Serving infrastructure and rollback | |||
| Drift detection after launch | |||
| Benchmarked against a simple baseline | |||
| Documented so your team can own it | |||
| Honest answer when ML is not the tool |
Pricing for machine learning development UK teams can plan around: we scope the work, agree a price, and stick to it. The data feasibility work comes first, so if the data cannot support the model, you find out before the large commitment rather than after it.
Machine learning is iterative by nature: the second model is usually better than the first because of what the first one taught you. Reprioritise each sprint as the results come in.
Anything else? Just book a call. We'd rather talk than write another FAQ entry.
✓Risk-free first project · Full refund if you’re not satisfied
AI is the umbrella term. Machine learning is the part of it where a system learns patterns from your historical data rather than following rules somebody wrote. Large language models are one kind of machine learning, and the one everyone is talking about, but they are a poor fit for a lot of problems. Fraud detection, demand forecasting, and recommendations are usually better served by classical ML: cheaper to run, easier to explain, and far more accurate on structured data.
Historical examples of the thing you want to predict, enough of them, with the outcome recorded. The volume depends on the problem: a fraud model might need tens of thousands of labelled transactions, while a forecasting model may work with a few years of clean history. What matters more than volume is that the data reflects the real world, that the labels are accurate, and that you are allowed to use it. We assess all of that in week one, before anyone trains anything.
Recommendation systems, predictive models such as churn and demand forecasting, classification, anomaly detection including fraud, and NLP over text you already hold. The common thread is a decision that gets made repeatedly and that historical data could make better.
Then we tell you in week one, and we tell you specifically what is wrong: not enough examples, missing labels, leakage between your training and test sets, or a target variable nobody has agreed on. Sometimes the answer is a data collection phase before any modelling. Sometimes the answer is that a rules engine would serve you better and cost a fraction. We would rather say that than take a budget for a model we do not believe in.
Six to twelve weeks for most production builds, depending on the state of your data and how the model has to be served. Week one gives you a feasibility answer. Weeks two to four produce a baseline and a working prototype evaluated on a holdout set. The remaining time goes into the data pipeline, serving infrastructure, and monitoring, which is the part that decides whether the model still works in six months.
It will, and that is expected. Fraud patterns change, customers change, catalogues change. We deploy with drift detection and track live performance against real outcomes, so degradation shows up in an alert rather than in a complaint. Retraining is built as a routine pipeline job from the start, not a research project you have to fund again a year later.
Yes, and it happens often. A well-written rules engine beats a model on plenty of problems, and it is cheaper to run, easier to explain to a regulator, and far easier to debug. If your problem is one of those, we will say so on the first call rather than sell you a model you would come to regret.
Book a 30-minute call. No pitch. Tell us what you are trying to predict and what data you hold, and we will tell you honestly whether the machine learning development UK teams like yours invest in would beat what you already do today.
✓Risk-free first project · Full refund if you’re not satisfied
