Decisions backed by data
Forecasts and scores your team can act on, explained in terms the business understands.
Machine Learning & Data
Machine learning that moves your numbers.
Custom models for forecasting, scoring, anomaly detection and computer vision — plus the data pipelines and MLOps needed to keep them accurate in production.
Machine Learning & Data
We don't stop at a demo. Every Machine Learning & Data engagement ships to production with evaluations, monitoring and documentation — measured against the outcomes that matter to your business.
Outcomes
What this means for your business
Reliable data foundations
Clean, well-modelled pipelines that feed models and dashboards alike.
Models that stay accurate
Monitoring and retraining workflows catch drift before it affects results.
What's included
Typical deliverables — tailored to your scope in our proposal.
- Data audit and strategy
- Data pipelines and warehousing
- Model development and validation
- Computer vision models
- MLOps and model monitoring
- Dashboards and reporting
Example use cases
Common projects our clients start with.
Demand forecastingChurn and lead scoringFraud and anomaly detectionVisual inspectionDocument and image classificationPricing optimisation
Typical technology
Chosen per project based on your requirements.
PythonPyTorchscikit-learndbtSnowflakeBigQuery
How we deliver
From kickoff to launch
Discover
1–2 weeksWe map your goals, users, data and constraints, then define the highest-value use case and how success will be measured.
Prototype
1–3 weeksA working prototype tests the riskiest assumptions with real data — so you see results before committing to a full build.
Build
4–10 weeksWe engineer the production solution in short sprints with weekly demos, evaluations and full transparency on progress.
Launch & scale
OngoingWe deploy, monitor quality and cost, and keep improving — or hand over to your team with documentation and training.
How long does it take to build an AI MVP?
Most MVPs take six to twelve weeks, depending on scope, integrations and data readiness. We agree milestones up front so timelines stay predictable.
Who owns the code and IP?
You do. All code, prompts, models, documentation and intellectual property created for your project are transferred to you.
How do you handle data privacy and security?
We follow least-privilege access, keep your data in your environment wherever possible, sign NDAs and DPAs on request, and use model providers configured not to train on your data.
How is pricing structured?
Discovery Sprints and MVP builds are fixed-price against an agreed scope. Dedicated teams are billed monthly. You'll receive a detailed proposal after our first call.
Related services
Often combined with
Let's talk about your Machine Learning & Data project.
Tell us what you're working on. We'll reply within one business day with honest advice on the fastest path to results.