AI and machine learning
Classical machine learning, forecasting, classification, recommendation and computer vision — from feasibility to monitored production models.
Models that solve a defined problem and are measured against it.
6 deliverables, a 4-stage approach and 5 related technologies.
Overview
Machine learning is valuable when there is a clear prediction to make, enough representative data, and a way to act on the prediction. Without those, simpler rules often perform better.
We assess feasibility honestly, establish a baseline, train and evaluate models on held-out data, and deploy them with monitoring for drift and a fallback when confidence is low.
What we deliver
- Feasibility and data assessment
- Baseline and evaluation metrics
- Trained model with evaluation report
- Inference service or batch pipeline
- Monitoring for drift and performance
- Model documentation and limitations
Use cases
Typical situations where this service is the right fit.
Demand forecasting
Plan stock and staffing from historical patterns.
Document classification
Route incoming documents automatically.
Visual inspection
Detect defects or read meters from images.
How we work
The stages we follow. Each ends with something you can review.
- 01
Define the decision and its cost of errors
- 02
Beat a simple baseline first
- 03
Evaluate for bias across relevant groups
- 04
Keep a human in the loop where errors are costly
Things to consider
Questions worth answering before you commit budget.
- Model quality depends on data quality and representativeness.
- Personal data used for training needs a lawful basis and minimisation.
Technologies and engagement
Technologies we often use
We choose the stack with you; this is a starting point, not a requirement.
Ways to engage
- Fixed-scope project with milestones
- Dedicated team billed monthly
- Ongoing support and improvement retainer
Frequently asked questions
Related services
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Ready to talk about ai and machine learning?
Share a few details and we will reply with questions, options and an honest view of effort.