Machine Learning
Lead scoring model for a fashion ecommerce brand in New York
Predictive model trained on historical data and deployed into daily operations for a fashion ecommerce brand.
Churn reduced by 25%
Machine learning models that predict what happens next
Forecast demand, predict churn, detect fraud and recommend products with custom machine learning models trained on your data and deployed into your systems.
At a glance
30%average forecast error reduction
18%typical lift from recommendations
40+models deployed to production
Overview
Your historical data already knows a lot about your future
Sales records, customer behaviour and operational logs contain patterns no person can spot at scale. Machine learning finds those patterns and turns them into reliable predictions.
We focus on models that change decisions: how much stock to order, which customers are about to leave, which transactions look suspicious and what each visitor is most likely to buy.
Models are deployed with monitoring and retraining pipelines, so accuracy stays high as your business and data change.
This service works best alongside our Deep Learning Solutions and Data Science Services services. For a deeper introduction, read our guide: AI vs machine learning vs deep learning.
What's included
- Problem framing and success metrics
- Data audit, cleaning and feature engineering
- Model selection, training and tuning
- Forecasting and time series models
- Classification, regression and clustering
- Recommendation engines
- Model APIs and system integration
- MLOps: monitoring, drift detection, retraining
average forecast error reduction
0
%
typical lift from recommendations
0
%
models deployed to production
0
+
Features
What you get with our machine learning service
Everything below is delivered by specialists who focus on machine learning every day.
Demand forecasting
Predict sales and inventory needs by product, store and week.
Churn prediction
Spot at-risk customers early and act before they leave.
Fraud detection
Flag unusual transactions and behaviour in real time.
Recommendations
Personalised product and content suggestions that lift order value.
Lead scoring
Rank leads by likelihood to convert so sales focuses on the best.
Explainable models
Clear drivers behind every prediction so teams trust the output.
Our process
Our Machine Learning process
A clear, repeatable process with a checkpoint at every stage, so you always know what is happening and why.
1
Frame
Define the decision, the metric and the data needed.
2
Explore
Clean data, engineer features and set a baseline.
3
Model
Train, validate and compare candidate models.
4
Deploy
Ship as an API or batch job with monitoring.
Machine Learning tools and platforms we use
Pythonscikit-learnXGBoostLightGBMProphetMLflowAWS SageMakerGoogle Vertex AIPythonscikit-learnXGBoostLightGBMProphetMLflowAWS SageMakerGoogle Vertex AIPythonscikit-learnXGBoostLightGBMProphetMLflowAWS SageMakerGoogle Vertex AIPythonscikit-learnXGBoostLightGBMProphetMLflowAWS SageMakerGoogle Vertex AI
Portfolio
Machine Learning projects we have delivered
Browse 50 recent machine learning projects. Click any project to see the details.
Showing 12 of 50 projects
Machine Learning
Machine Learning
Price optimisation model for a B2B software vendor in New York
Predictive model trained on historical data and deployed into daily operations for a B2B software vendor.
Model accuracy 93.2%
Machine Learning
Product recommender for an immigration consultancy in Sydney
Predictive model trained on historical data and deployed into daily operations for an immigration consultancy.
Model accuracy 98.9%
Machine Learning
Customer segmentation for an electronics store in Islamabad
Predictive model trained on historical data and deployed into daily operations for an electronics store.
Model accuracy 96.4%
Machine Learning
Customer segmentation for a wedding planner in Karachi
Predictive model trained on historical data and deployed into daily operations for a wedding planner.
Churn reduced by 9%
Machine Learning
Customer segmentation for an insurance broker in Multan
Predictive model trained on historical data and deployed into daily operations for an insurance broker.
Model accuracy 95.5%
Machine Learning
Product recommender for a solar energy installer in Doha
Predictive model trained on historical data and deployed into daily operations for a solar energy installer.
Forecast error down 29%
Machine Learning
Product recommender for a digital marketing consultancy in Peshawar
Predictive model trained on historical data and deployed into daily operations for a digital marketing consultancy.
Forecast error down 14%
Machine Learning
Price optimisation model for an electronics store in Faisalabad
Predictive model trained on historical data and deployed into daily operations for an electronics store.
Forecast error down 8%
Machine Learning
Customer segmentation for a restaurant group in London
Predictive model trained on historical data and deployed into daily operations for a restaurant group.
Model accuracy 98.3%
Machine Learning
Demand forecasting model for a fitness studio in Manchester
Predictive model trained on historical data and deployed into daily operations for a fitness studio.
Churn reduced by 28%
Machine Learning
Fraud detection system for a pharmacy chain in Multan
Predictive model trained on historical data and deployed into daily operations for a pharmacy chain.
Churn reduced by 26%
FAQ
Machine Learning questions, answered
Still have a question? Our team replies within one business day.
How much data do I need for machine learning?
It depends on the problem. Many forecasting and scoring models work well with one to two years of clean history. Our data audit tells you what is possible.
What is the difference between AI and machine learning?
Machine learning is a branch of AI where systems learn patterns from data to make predictions, rather than following hand-written rules.
Can you deploy models into our existing software?
Yes. We deploy models as APIs or scheduled jobs that plug into your CRM, ERP, website or BI tools.
How do you measure model accuracy?
We agree business metrics up front, validate on unseen data and track live performance against those metrics after launch.
Do models need maintenance?
Yes. Data changes over time, so we monitor for drift and retrain models on a schedule or when accuracy drops.
From the blog
Related reading
Guides from our team that go deeper on this topic.
AI vs Machine Learning vs Deep Learning vs Data Science: Explained for Business
A plain-English explanation of AI, machine learning, deep learning and data science, how they relate and which one solves which business problem.
Read article: AI vs Machine Learning vs Deep Learning vs Data Science: Explained for Business
Power BI vs Tableau vs Looker Studio: Which BI Tool Is Right for You?
An honest comparison of the three most popular BI tools on cost, ease of use, data connections, visualisation and sharing, with recommendations by business…
Read article: Power BI vs Tableau vs Looker Studio: Which BI Tool Is Right for You?
12 Business Processes You Should Automate in 2026 (Zapier, Make, n8n)
Twelve practical workflows you can automate today, how to choose between Zapier, Make and n8n, and how to calculate the time and money you…
Read article: 12 Business Processes You Should Automate in 2026 (Zapier, Make, n8n)Related services
Services that pair well with this one for faster results.
Deep Learning Solutions
Computer vision, NLP, speech and custom neural networks.
Data Science Services
Analysis, experimentation and insight from your business data.
Artificial Intelligence Solutions
AI strategy, generative AI apps, vision and predictive systems.