AI & Intelligent Systems
Predictions your operators actually use
Forecasting, scoring, and anomaly detection embedded where decisions happen.
Demand surprises the supply chain, churn surprises sales, fraud surprises finance — while the signals sat in your data all along. Classic ML remains the workhorse of enterprise intelligence; the craft is putting predictions inside the workflow, not beside it.
- Forecast error cut enough to change purchasing behaviour
- Risk decisions that are faster and explainable
- Alerts your team trusts because false positives were engineered down
Capabilities
What the work actually involves
Demand & revenue forecasting
Time-series models tuned to your seasonality, promotions, and market — feeding planning systems directly.
Risk & propensity scoring
Credit, churn, conversion, and fraud scores with the explainability your governance requires.
Anomaly detection
Transactions, sensors, and operations monitored for the outliers that matter — with alert fatigue engineered out.
Optimisation models
Routing, pricing, scheduling, and inventory decisions computed instead of guessed.
Decision integration
Scores surfaced inside the ERP screen, the CRM record, the dispatch console — where the decision is made.
Feature engineering and stores
The features that carry the signal, computed the same way in training and in production — because the gap between those two is where most models quietly stop working.
Monitoring and retraining
Drift detection on inputs and outputs, a retraining trigger tied to measured degradation rather than the calendar, and a rollback path when a new model is worse.
Deliverables
What you are handed.
Yours to keep, and written so another team could pick them up.
A model with a baseline it beat
Compared against the simple heuristic it replaces before it ships.
Feature pipeline in production
The unglamorous half — the same features computed identically at train and serve time.
Drift monitoring
Alerts on the input distribution moving, because that is how a good model quietly becomes a bad one.
A retraining runbook
Who retrains it, on what trigger, and how the new version is approved.
We do not publish prices — scope drives them. Everything else, here.
- Starts with
- A data review and a baseline built from the existing rule
- Typical duration
- 8–14 weeks
- Who you get
- A data scientist, a data engineer, an ML engineer
- Commercial model
- Fixed-scope baseline, then phased
How to decide
What the answer depends on.
Two sets of conditions. Read both against your own situation — most organisations recognise themselves in one column within a sentence or two.
This is the right call when
- You have historical outcomes to learn from, not just historical records.
- A prediction would change a decision somebody actually makes.
- You can wait for a proper baseline comparison before committing.
A different approach fits better when
- You want ML because it is expected, not because a decision needs it.
- There is no labelled history and no route to creating one.
- A rule everybody understands would do the job.
Before you ask
Questions about ML & predictive intelligence
Sectors
Where this comes up most.
The regulatory context and the systems already in the building change the build. Each sector page says how.
Next step
Bring us the problem. We will bring the architecture.
A discovery call takes forty-five minutes. You leave with our read on the problem, the shape of the system we would propose, and a straight answer on whether we are the right team for it.
- No sales deck
- An engineer on the call, not an account manager
- NDA before you share anything