Engineer
Machine Learning Solutions
Models built for production decisions — forecasting, language, recommendations and anomaly detection that stay accurate under live load.
The problem
Most machine-learning work ends as a notebook. Without baselines, deployment paths and monitoring, models never become part of how the organisation decides or operates.
Outcomes
- Models that reach and remain in production
- Predictions wired into decisions operators already make
- Drift, risk and performance under continuous watch
- Measurable effect on planning, risk or experience
Lifecycle
From decision framing to monitored production
We treat machine learning as an operating system for predictions — not a one-off model handoff. Feedback from live performance returns to retrain.
Capabilities
Where models earn their place
Concrete prediction surfaces — forecasting, language, marketing decisions, recommendations and anomaly detection — built end to end for your environment.
- 01
Predictive models
Forecast what will change — demand, behaviour, risk — so planning and intervention happen before the outcome is fixed.
- Time-series and demand forecasting
- Behaviour and propensity models
- Risk scoring with stated assumptions
- 02
Language systems
Extract structure from text and dialogue so unstructured signal becomes operable — classification, entities, assisted response.
- Sentiment and intent classification
- Named-entity and document structuring
- Assisted workflows with human control where it matters
- 03
Marketing decision models
Models that allocate attention and spend against measured contribution — segmentation, attribution and mix — not vanity metrics.
- Campaign and channel performance models
- Segmentation grounded in behaviour
- Attribution and marketing-mix analysis
- 04
Recommendation engines
Personalisation that serves the next action in context — collaborative, content-based or hybrid — with latency and governance fit for production.
- Collaborative and content-based filtering
- Hybrid systems under explicit business rules
- Real-time serving into existing products
- 05
Anomaly detection
Surface patterns that deviate from expected baselines — fraud, quality, security, maintenance — early enough to act.
- Fraud and transaction monitoring
- Operational and quality outliers
- Alerting with audit trails for investigation
Production discipline
Custom work, same controls
Bespoke models still follow one standard: frame, prove, deploy, watch. That is how custom solutions stay useful after the first release.
Problem before algorithm
Every engagement starts with the decision, constraint and data reality — not a preferred model class.
Baselines that can lose
Simple, honest baselines first. A model ships only when it beats them under evaluation that mirrors production.
Deploy into the workflow
Integration, approvals and failure modes designed with the systems operators already use.
Monitor and retrain
Drift, latency and outcome quality watched continuously. Retraining is part of the system, not an afterthought.
How we work
Narrow the decision. Prove the lift. Ship the loop.
We do not start with a platform of models. We start with one decision worth predicting, beat a baseline, then put the model where work already happens.
- 01
Frame the decision the model must serve
- 02
Establish baselines and success criteria
- 03
Build, evaluate and harden against failure modes
- 04
Deploy into workflow and monitor drift
Next step
Name the prediction that would change a decision
Bring a forecasting gap, a fraud or quality blind spot, or a personalisation path that is still rule-of-thumb. We will say whether a model belongs there — and what it takes to keep it alive.