AI, machine learning & deep learning

Machine learning, deep learning and AI assistants built on your own data, documents and images.

What this means for your business

Your business already produces valuable data: sales records, documents, images and customer messages. Machine learning turns structured data into forecasts and recommendations, and deep learning handles harder problems like recognising images and understanding text. We start with a business problem, check whether AI is the right answer, and build solutions that fit into the tools your team already uses.

Business team reviewing AI predictions on a large screen

What’s included

Machine learning

Predictive analytics

Forecast sales, demand, costs or customer behaviour from historical data.

Recommendation systems

Suggest the right products, content or actions to each user.

Customer segmentation

Group customers by behaviour so marketing, pricing and service can be targeted.

Deep learning

Computer vision

Image classification, object detection, OCR and visual inspection.

Natural language processing

Text classification, sentiment analysis, search and summarisation.

Custom neural networks

Models designed and trained on your own data, from dataset preparation to evaluation.

Applied AI

AI assistants & chatbots

Assistants that answer questions using your own documents and data.

Document processing

Extract information from invoices, forms and documents automatically.

Deployment & monitoring

Models in production on the cloud, phones or small devices, monitored so they keep performing.

What you get

Data assessment and feasibility report
Proof-of-concept results
Trained, tested model with evaluation metrics
Integration into your app, API or workflow
Monitoring and retraining plan
Documentation and handover

Ideal for

Businesses with data they are not using yet
Teams spending hours on repetitive manual work
Companies that want better forecasts
Quality inspection and visual checks
Reading and sorting documents or images
Understanding customer feedback at scale

How a project runs

Four clear stages, with a working demo at the end of each one.

01
Discover
We learn your business, your users and the problem worth solving.
02
Design
Screens, architecture and a plan with milestones you can track.
03
Build
Short sprints with regular demos, so you see progress every step.
04
Launch & grow
Deployment, monitoring and improvements long after go-live.

AI & machine learning: common questions

What is the difference between machine learning and deep learning?

Both are part of AI. Machine learning finds patterns in structured data like sales records. Deep learning uses multi-layer neural networks and works best on complex data such as images, audio and text. We pick whichever fits your problem.

Do we have enough data for AI?

We start with a short assessment of your data and tell you honestly what is possible today. Techniques like transfer learning let us start with smaller datasets.

Can AI fit into our existing software?

Yes. We integrate models into your current apps, websites and workflows.

Can you start with a small trial?

Yes. A proof of concept is often the best first step before a full build.

How do you measure success?

We agree on clear business metrics at the start, such as time saved or forecast accuracy, and track them.