Overview
I help organisations identify where machine learning or AI will create measurable value, and where a simpler approach is the better decision. The work starts with business objectives and operational constraints, not with models for their own sake.
Projects can cover classical machine learning, predictive analytics, decision support systems, NLP workflows, or AI-enabled product features. Depending on the case, I can support strategy, prototyping, technical delivery, or model operationalisation.
What the engagement can include
- Use-case assessment and feasibility analysis
- Data audit, feature design, and dataset preparation
- Baseline modelling and benchmark comparison
- Model evaluation with business-relevant metrics
- Explainability, risk review, and stakeholder communication
- API, workflow, or product integration planning
- Handover documentation and team enablement
Typical use cases
- Demand forecasting and propensity modelling
- Lead scoring and conversion optimisation
- Customer segmentation and recommendation systems
- Document classification and NLP pipelines
- Clinical or operational decision support
- Internal AI workflows for content, search, or process automation
Delivery principles
- Start with a narrow, testable problem
- Use interpretable methods when governance matters
- Validate against operational constraints early
- Design for maintenance, not just a demo
Best fit
This service is a good fit when you need a senior technical partner to scope an ML or AI initiative, build a working prototype, or move an existing model closer to production.