Machine Learning · London
Award-winning machine learning agency building and training models for demand forecasting, classification and scoring, recommendation and prediction, then deploying them into live software and retraining them as the data moves.
Solutions we've built power teams at:
The common worry is spending months on a model that never leaves the laptop it was built on. We have delivered more than 50 projects, and we guide you through every stage of this one.
We start with a free call to go through the question you want answered, the data you hold and how AI models would be used. You leave with our view of whether machine learning is the right tool, and we will say plainly when a simpler method would do.
Before anything is built and before you commit any money, you get a written breakdown of what the work will cost, what the model should be worth in practice and how long it takes to pay for itself. The scope is fixed and so is the price.
We agree the stages of the build with you before we start, and the first is a model trained on your data and measured against a simple benchmark, so you see what it is worth early. You pay for each stage once it is finished and working.
We stay on hand after launch to retrain the model as your data shifts, watch how its accuracy holds up and extend it to new questions. All code and intellectual property belongs to you on payment, so continued support is not a condition of keeping it.
Each of these turned something one person knew how to judge into a model the whole business could rely on.
A financial analysis firm had a proven chart-reading method that only one analyst could run, taking hours a day and capping how many instruments the firm could cover. CodeLeap turned that method into a machine-learning platform serving daily predictions through an API.
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Read moreWe pick the smallest model that answers the question, because a simpler one is cheaper to run, easier to explain and usually accurate enough.
Python is where models are built and trained, and where the code that prepares data for them lives, which keeps the whole pipeline in one language.
For most problems on ordinary business data, scikit-learn gives a working model quickly and a result you can explain to somebody who has to sign it off.
On tables of numbers and categories, XGBoost usually beats more complicated approaches, which makes it the first thing to try on most forecasting and scoring work.
PyTorch is used where the data is images, audio, text or long sequences, and where the model has to learn patterns too involved for a simpler method.
Hugging Face holds models already trained on large collections of text and images, which can be adapted to your task with far less of your own data.
Training produces many versions of a model, and MLflow records what each was trained on and how it scored, so the one in production can always be traced back.
The data a model learns from and the predictions it produces both need storing, and PostgreSQL holds them together so results can be checked against what happened.
Training needs graphics hardware briefly and serving needs very little, so we train and host on AWS and pay for each part only for as long as it runs.
These are the tools that come up most often rather than the limit of what we work with. If your data sits in a warehouse, a spreadsheet or a system built in-house, we can train on it.
Real reviews from clients we have delivered for, on Clutch and elsewhere.
The enthusiasm, understanding of our mission, and passion from the first few minutes of the call were the deciding factors in choosing CodeLeap. Within 3 months of our app launching, we had over 1,500 downloads, appeared in leading publications from Forbes, The Metro, Marie Claire, Women's Fitness and many more, and started raising further investment.
They are incredibly diligent in the quality of work they produce, and go above and beyond when it comes to solving issues faced within the project, and to ensure we meet strict project deadlines. You never feel like you are one of many clients, and they ensure they are always there to help with any need you have, no matter how big or small.
CodeLeap made me feel like my options were limitless in terms of what could be achieved, which was critical to me as we were creating something very unique. I'm glad I chose Codeleap over a large corporation as the personable approach resulted in a smooth and pleasant experience.
Working with CodeLeap was a very good experience from both a professional and personal point of view. These people know what they are doing and are both efficient and clever and made a good partner. I wouldn't hesitate to recommend CodeLeap.
CodeLeap adds so much value and goes above and beyond what I expected. CodeLeap's team is very flexible with all the different requests we've put in front of them. They've also been great with the whole business side of things, and being able to forecast for this industry is really helpful. Overall, I'm completely satisfied with their work — they've been incredibly helpful.
Their flexibility and focus on quality were outstanding.
Marcelo has been wonderful throughout the process and we are very happy.
Absolutely FANTASTIC. This is very much what I had in mind. We're very pleased with your team, thank you all very much.
From £2,500
Book a free consultationFrom £15,000
Book a free consultationFrom £40,000
Book a free consultationEverything you might want to know before starting a project with us.
One model trained on your data and deployed into the software that uses it usually starts at £15,000, and a platform running several models together starts at around £40,000. What moves the figure is the state of the data, how many systems the prediction has to reach and how often it has to be retrained. A data review costs from £2,500.
For ordinary business predictions, a few thousand past records with the outcome recorded is often enough to get a useful result. What matters more than volume is whether the data covers the situations you want predicted. A short data review answers this before any money goes into a build, which is the point of doing one.
A single model usually takes eight to twelve weeks from agreed scope to live use, with a data review beforehand running one to two weeks. A platform with several models runs in phases over several months. Preparing the data is normally the longest part, and the modelling itself is rarely what holds a project up.
In most cases yes, and where explanation matters we choose a method that allows it. Models built on tables of business data can show which factors pushed a prediction one way or the other, at the level of an individual case. Deep learning on images or text is harder to explain, so we use it only where nothing simpler works.
Accuracy falls quietly rather than breaking, which is why a deployed model is monitored against what happened. When the gap widens, the model is retrained on recent data, and that work is set up during the build so your team can run it. Everything needed to do so is handed over with the code.
A small, experienced team of designers, engineers and problem-solvers working closely with you from first idea to final delivery.
Victor
CTO / Co-Founder
Vivian
COO / Co-Founder
Matt
Project Manager
Amábili
Designer
Konrad
Sales Representative
Kenzo
Tech Lead
Paulo
Tech Lead
Marcelo
Project Manager
Felipe
Tech Lead
Vinny
Advisor
Laura
Commercial Analyst
Takuya
Tech Lead
Kaella
Backend Developer
Mario
Designer
Our team of experts can help you evaluate innovation opportunities for your business.
Prefer to reach us directly? Email us at hello@codeleap.co.uk or call us at +44 (0) 330 122 1101
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