Deep learning solutions development
Streamlining business processes and moving the metrics that matter by tuning the performance of algorithms built on neural networks.
Machine learning models built to survive production
Custom machine learning development from feature engineering through deployment, retraining and the monitoring that catches drift before your users do.
CRISP-DM deliveryPython / PyTorchRetraining included
What we do
Our machine learning development services
Custom web application development
Expert help on custom ML-based web development, so the model reaches users through something they can actually use.
Enterprise machine learning as a service
A hand-picked team building ML for enterprises — automating and optimising operations by deriving insight from raw data with human intervention removed.
Outcomes
Implement a model and scale it up
What the right-sized custom ML application actually opens up.
Better decision-making
Exploiting large volumes of information to improve processes, optimise the workforce and deliver strategic growth.
Boost in production efficiency
Identifying patterns, making accurate predictions about market trends and customer behaviour, then building to fit.
Reduced human error
Eliminating the human factor where it causes analysis errors, and maximising performance at speed.
Enhanced customer experience
AI-powered chatbots and virtual assistants built on ML, improving satisfaction and conversion.
Better security
ML helps predict malicious activity — malware, phishing, app and authentication attacks — and detect threats before they land.
Sales support
Anticipating and responding to user demand, with chatbots providing quality real-time, human-like support.
Higher employee productivity
Automated jobs performed by algorithms, freeing people for higher-value and more complex work.
Comprehensive research
A model can decide on the best delivery date and pricing, and analyse buying habits well enough to act on them.
Our approach
How an ML build actually proceeds
An approach shaped by years of hitting the pitfalls that hold back performance, and getting the most out of automation.
Free consultation
First a no-obligation conversation to understand the business need, then a technical call to get to the core of the project and pick the right starting point for it.
Data preparation
We clean and validate your data so it is actually usable — checking nothing is missing, and masking anything private before it goes near a model.
Model building
A model built for your requirements and adapted to the data you actually hold. We can equally fine-tune an open-source model you already run.
Testing
Models are tested comprehensively against the KPIs agreed at the start, so performance is demonstrated rather than asserted.
Lifecycle
Model development life cycle
A streamlined system for turning an ML idea into something real. From planning through maintenance, adhering to the business need rather than to the process document.
5Monitoring and maintenance
Delivery is not the end. Updates and changes as the environment around the system shifts, which it always does.
4Model deployment
Automated pipelines, scaling, and deployment into production, with algorithms adjusted to meet the benchmarks you set.
1Discovery
You explain the business specifics, the goals and the pain points. We come back with what ML can and cannot do about them, and the approach we would take.
2Preparation of the model
Everything that goes into an effective model: data preparation, data mining, data engineering, training and verification.
3Model evaluation
Proving whether the chosen use cases can deliver tangible value to your organisation — and identifying which are worth carrying forward.
The project lifecycle at a glance
We take the project from day one, starting with clarifying requirements and ending with a fully automated system handed over.
The team applies current ML tooling and development approaches, and works to CRISP-DM — the Cross-Industry Standard Process for Data Mining — so the quality of the process is not dependent on who happens to be on the project.
What that means in practice: business understanding, data understanding, modelling, evaluation and deployment, each with an exit you can inspect before funding the next one.
Why us
Work with seasoned ML developers
Companies across countries and industries choose us for expertise combined with a view of the real world it has to work in.
Business-first approach
Your business needs come first, which is why planning and consulting get particular focus. Research prototyping through testing and deployment is aimed at your goals, not at a showreel.
Vetted team of professionals
A strong team of ML specialists who think past the obvious answer, so the software you get is genuinely high quality.
Trustworthy partner
A track record indicating that most business domains can benefit from custom ML. Your idea and data stay safe while the accrued expertise works in your favour.
Affordable and on-time delivery
Time and money matter in a competitive environment, so we work to hit deadlines and maximise the pay-off from the final product.
Client participation
Whatever stage the solution is at, you will not be left unaware of status. Updates on completed and ongoing work, changes implemented when required.
Sophisticated technology
Current tools and capabilities applied to reinforce your business model by building high-end algorithms rather than fashionable ones.
Related
Other ways we can help
Start here
Tell us the problem. We'll tell you if it's worth solving.
Thirty minutes with an engineer, not a salesperson. You leave with a rough scope, a cost band and an honest read on feasibility.
30 minutes with an engineer · No slide deck · Reply within one business day