Forecasts and risk scores an auditor can follow.
Demand, risk, anomaly and prioritisation models on governed data. Explainable enough for a regulator, fast enough for an operator, and monitored after they ship.
A model nobody trusts never changes a decision.
Plenty of organisations have a forecasting or scoring model sitting in a notebook. Few have one that the operations team relies on every morning.
The gap is rarely accuracy. It is data that shifts underneath the model, outputs nobody can explain, and no owner when performance drifts.
We build predictive models as operational products: on governed data, with explanations attached to each prediction, and with monitoring and retraining agreed before launch.
What we build and run for you.
Forecasting
Demand, volume and capacity forecasts with ranges, not just a single number.
Risk and prioritisation
Scores that rank cases, inspections or transactions for human attention, with reasons.
Anomaly detection
Alerts on unusual transactions, readings or behaviour, tuned to keep false alarms manageable.
Feature and data pipelines
Governed features and training data on your lakehouse, reproducible end to end.
Production, not pilots. Built to run inside your operations.
Every agent we ship has an owner, an evaluation set and an audit trail before it touches a live case.
From data to a decision someone acts on.
Frame
decisionStart from the decision the model supports and what a wrong answer costs.
Prepare
dataBuild governed features with lineage, so every input can be traced.
Model
explainTrain, validate against a baseline, and attach reason codes to each prediction.
Operate
monitorWatch drift and accuracy in production, with a retraining plan and a named owner.
- Baseline comparison before launch
- Reason codes on every prediction
- Data lineage from source to feature
- Drift and performance monitoring
- Model versioning and rollback
Where it earns its keep.
Illustrative patterns from the sectors we work in. Client details stay anonymised.
Inspection prioritisation
Rank sites for inspection by risk, so field teams start where problems are most likely.
Transaction anomaly detection
Flag unusual activity for review with the factors that triggered each alert.
Demand and exception forecasting
Forecast volumes and predict which partner shipments are likely to slip.
Service demand planning
Forecast application volumes by type and season to staff counters and queues.
Chosen for the job, not the vendor.
Work this builds on.
Real-time event streaming feeding AI and ML pipelines across business units
Manual data exchange replaced with monitored, automated flows across a federal ministry
Client details anonymised per delivery agreements.
Questions we get asked.
Is your data work AI-ready?
It depends what you mean. If you mean "can your data foundation feed RAG and ML pipelines" — yes, that is the default deliverable shape. If you mean "will the dashboards become AI-native" — that is our AI Insight product roadmap conversation, and it is genuinely different work. We separate the data engineering investment from the AI-application investment and price them on their own merits.
What is your data governance opinion?
Data governance is an operating model, not a tooling decision. Tools matter — we deliver on Informatica Axon, Collibra, and the cloud-native equivalents — but the governance only works when the data stewardship roles are defined, accountable, and tied to real business outcomes. We design the operating model alongside the tooling, and we will not start a tooling rollout if the role definitions are absent.
Do you need a data platform in place before building models?
Not a perfect one. We need access to the data the decision depends on and a place to run governed pipelines. Often we build the first model and the minimum data foundation together, then expand the platform as more use cases arrive.
What would you hand to an agent first?
Bring one workflow. In 60 minutes an AI architect maps where an agent helps, what it needs to reach, and what a first working version would take.