01
Customer and revenue
Analyse retention, customer value and opportunity quality to support commercial prioritisation.
Data, Predictive Models & BISee what is changing.
See what is changing.
Plan what comes next.
We build data pipelines, predictive models and business intelligence around the decisions your business makes. Customer behaviour, demand and operational performance become signals your team can evaluate and use.
Demand forecasts · retention models · business intelligence
Related workDistil.ai2 to 4 weeksto a focused working proof
Senior engineeringresponsible for the implementation
Magus.DEMAND & REPLENISHMENT MODELS
Demand planning
ExampleHistorical demandForecast range
01
HistorySales + availability + promotions
02
ForecastRange over supplier lead time
03
PlanReplenishment exceptions for buyers
Back test against a baseline before rollout.
Join sales with availability, promotions and lead times.
01 · THE WORK
Built for the work your business needs
We build data pipelines, predictive models and business intelligence around the decisions your business makes. Customer behaviour, demand and operational performance become signals your team can evaluate and use.
02 · THE IMPLEMENTATION
Where it can make a difference
02
Demand and capacity
Forecast product demand, workload or resource needs at the horizon the business can act on.
03
Business visibility
Connect agreed metrics, trends and exceptions in reporting that leads to an operational decision.
03 · THE IMPLEMENTATION
What we implement
01
A reliable baseline
Reconcile the history, agree business definitions and check whether the data can support the intended decision.
02
Models that can be tested
Compare predictions with a simple baseline and evaluate performance on unseen periods or records.
03
A route into action
Put the signal into the team’s workflow and monitor data quality and performance over time.
IMPLEMENTATION EXAMPLE
Replenishment that accounts for lead time and lost demand
A buying team plans replenishment across products and locations.
THE EVIDENCE
What the system needs
- Sales history and stock availability
- Promotions, seasonality and product changes
- Supplier lead times, open orders and stock policy
THE CHECKS
What this example checks
- Separate low demand from periods when the product could not be bought.
- Forecast over the lead time horizon, not an arbitrary monthly average.
- Expose uncertainty and cold start products rather than pretending all lines behave alike.
THE WORKING RESULT
What reaches the team
A replenishment view with expected demand, stock out exposure, excess stock risk and the exceptions that need a buyer.
The decision boundaryBuyers approve commitments and inventory trade offs. Model recommendations remain subject to commercial constraints.
WHAT THIS EXAMPLE WOULD TEST
Back test against a simple baseline using a time based holdout. Compare forecast error, bias, stock out exposure and excess stock implications at the relevant horizon.
Illustrative implementation scope. This describes a possible build, rather than a claimed client result.
05 · THE WORK
From a useful proof to daily use
Choose one decision and a meaningful baseline. Test predictive performance alongside the practical cost of a wrong recommendation and the benefit of acting earlier.
A focused proof of concept ships in 2 to 4 weeks. We use the findings to scope production integrations, access, monitoring, rollout and support.
DELIVERED THROUGH OUR SERVICES
The work behind the solution.
YOUR NEXT STEP
What would you do differently with a better signal?
Bring us one workflow, real examples and the people who know the work. We’ll identify a practical first build.