AI demand forecasting and risk models your planners will trust
We build models on your own sales, inventory, customer and equipment data that predict what comes next, then put the result in the dashboards and alerts your team already checks.
- From kickoff to your first forecast on real data
- 4–6 weeksFrom kickoff to your first forecast on real data
- Every model is scored against your current method before launch
- BacktestedEvery model is scored against your current method before launch
- Each forecast shows its range and the drivers behind it
- ExplainedEach forecast shows its range and the drivers behind it
You find out about problems after they happen.
Stockouts show up as lost orders, overstock as a write-down, churn as a cancellation email at renewal time, and equipment failure as a stopped line. Most planning still runs on last year’s numbers and a spreadsheet only one person understands. AI demand forecasting and risk models give your team an earlier, measurable signal while there is still time to act.
- Buyers order from last year’s sales plus a gut-feel adjustment
- Fast movers run out while slow movers fill the warehouse
- Customer success learns an account is at risk when it gives notice
- Maintenance is either on a fixed calendar or after something breaks
How AI demand forecasting fits into your planning
We start from the decision the forecast has to support, such as what to reorder, which accounts to call or which machine to service, and work back to the data. Models are tested against your history and your current method, and only go live if they do better. The output lands where people already plan, with alerts for what needs attention.
Forecasts at the level you plan
By SKU, store, warehouse, customer or region, by week or by day, with a likely range rather than a single number.
Signals beyond your sales history
Promotions, prices, holidays, weather, lead times and economic indicators are tested and kept only when they improve accuracy.
Churn and renewal risk
Each account gets a risk score built from usage, support and billing data, with the reasons listed so the account manager knows what to address.
Quality and maintenance prediction
Sensor readings, inspection results and work-order history used to flag likely defects or failures before they reach a customer or stop a line.
Dashboards and alerts people use
Results in Power BI, Looker, your ERP or a simple web view, plus alerts in Teams, Slack or email when something crosses a threshold.
Monitored and retrained
Accuracy is compared with actuals every month. When patterns shift, models are retrained and only replaced when the new one wins.
Forecasts that change a decision
Demand by store and SKU
Weekly demand per item and location feeds replenishment, so buyers adjust orders before stockouts or markdowns, not after.
For: Retail and e-commerce
Inventory and reorder points
Safety stock and reorder points recalculated from forecast demand and supplier lead times across warehouses.
For: Distribution and wholesale
Churn and renewal risk
Accounts likely to cancel or downgrade are flagged 60 to 90 days before renewal, with the usage and support signals behind the score.
For: SaaS, financial services and insurance
Predictive maintenance
Machines whose vibration, temperature or cycle data show early signs of failure are scheduled for service in the next planned window.
For: Manufacturing and fleet operations
Quality and defect prediction
Batches with process readings that historically led to rework or out-of-spec results are flagged for inspection before release.
For: Manufacturing, pharma and food processing
Volume and staffing forecasts
Call, claim or application volumes forecast by day and channel, so staffing matches the workload instead of last month’s average.
For: Contact centres, insurance and public sector
How we deliver your first forecast
- 01Week 1–2
Audit the data and set a baseline
We pull the history, check its quality, and measure how accurate your current method really is. That number becomes the bar to beat.
- 02Week 3–4
Model and backtest
We build candidate models and test them on past periods they haven’t seen. You see accuracy and bias next to your baseline.
- 03Week 5–6
Deliver into your tools
The winning model runs on an automated pipeline and feeds your dashboard, ERP or planning sheet, with alerts set up for exceptions.
- 04Week 7 onward
Run, monitor and improve
Forecasts refresh on schedule. We compare them with actuals monthly, retrain when patterns shift and add new signals that prove useful.
What you get
- Production forecasting models running in your cloud
- Automated data pipeline from your source systems
- Backtest report against your current method
- Dashboards and threshold alerts for planners
- Forecasts written back to your ERP or planning tool
- Model documentation and data dictionary
- Privacy impact assessment under PIPEDA and Law 25
- Monthly accuracy and drift report
What it’s built with
- Modelling
- PythonLightGBMProphetStatistical baselinesTime-series foundation models
- Data and reporting
- SnowflakeBigQueryMicrosoft FabricdbtPower BILooker
- Infrastructure
- AWSAzureGoogle CloudScheduled pipelinesPostgresMLflow
Frequently asked questions
Bring one decision you’d plan better with a forecast.
In a free 30-minute call we’ll look at the decision and the data behind it, tell you whether a model can beat your current method, and what a first forecast would take.
30 minutes with a senior engineer. No sales pitch, no obligation.
