Custom ML Models
Built on Your Data,
Deployed in Production
We engineer, train, and deploy machine learning systems that predict churn, detect fraud, optimise pricing, and automate decisions — integrated directly into your product or operations.
ML That Moves the Metrics Your Business Cares About
Every industry has its highest-value prediction problem. We have built production systems across these six verticals — each with measurable business outcomes.
Recommender System
Hybrid collaborative-filtering + deep neural network trained on 18 months of purchase history, click streams, and product metadata.
Six ML Disciplines, One Engineering Team
From classic supervised models to deep learning and NLP — we match the right technique to your problem rather than forcing every problem into the same framework.
Predict labelled outcomes from historical data
When to use
You have labelled historical data and a clear target variable to predict.
Common Applications
Algorithms & Tools We Use
Our End-to-End ML Engineering Process
We do not just train a model and hand it over. Our process takes you from raw data to a monitored production system — with full documentation and team knowledge transfer.
We audit your existing data sources, assess quality, identify gaps, and map the signals most predictive of your target outcome.
Raw data is cleaned, joined across sources, and transformed into analysis-ready feature tables. Pipelines are automated and versioned.
We explore distributions, correlations, and interactions. Then we engineer domain-specific features that substantially improve model performance.
Multiple algorithm families are trained and compared (tree ensembles, neural networks, linear models). Hyperparameters are tuned via Bayesian optimisation.
Models are evaluated on held-out test sets with business-relevant metrics. Bias audits, fairness checks, and adversarial tests are run before any deployment.
The champion model is containerised and deployed as a low-latency REST API or batch inference pipeline — integrated with your systems.
We monitor prediction quality, input feature drift, and model degradation in production. Automated alerts trigger retraining when performance drops below thresholds.
Best-in-Class Tools, No Vendor Lock-in
We choose the right tool for each job. Our stack spans the full ML lifecycle — from raw data to monitored production inference.
From Data Audit to Production Model in 14 Weeks
Our structured delivery process turns an ML problem into a live, monitored system — with clearly defined milestones and deliverables at each stage.
From Proof of Concept to Full MLOps System
Whether you want to validate an idea first or go straight to production, we have an engagement model that fits where you are today.
What Could ML Predict
in Your Business?
Share your data setup with our ML team. We identify the highest-value prediction problem, assess data readiness, and give you an honest feasibility report — no commitment required.
Frequently Asked Questions
Technical and commercial questions answered directly.
Complete Your AI Stack
ML models work best alongside these complementary AI and data services.
AI Workflow Automation
Operationalise your ML predictions — trigger automated workflows when the model surfaces a risk or opportunity.
Generative AI Solutions
Combine predictive ML with generative AI for systems that both predict outcomes and produce content or recommendations.
AI Chatbot Development
Embed ML-powered intent detection and personalisation into your conversational AI layer.
AI Strategy Consulting
Not sure where to start? Our AI strategy team helps you identify the highest-ROI ML use case for your business.