Don’t just understand the past. Anticipate what’s next.
Reporting explains a period that has already closed. Predictive analytics produces forward signals while there is still time to change the outcome.
Forecasts your operation can actually plan against.
Demand Forecasting
What customers will need, by product, channel and location, before the orders arrive.
Sales Forecasting
Expected revenue by period and segment, with the assumptions behind the number visible.
Inventory Forecasting
Stock positions projected forward so replenishment happens before a shortage, not after.
Production Forecasting
Expected output and plan attainment per line, with the constraints that will bind first.
Capacity Forecasting
Where capacity runs short across machines, labour and the supply network.
Risk Prediction
Stock-out, supplier, margin and delivery risks scored with a probability, not a hunch.
Anomaly Detection
Deviations found in operational data as they emerge, including the ones nobody thought to alert on.
Models already built for each part of the business.
Each model trains on your own operational history, refreshes on a schedule and reports how accurate it has been.
Retail
- Demand forecast with safety stock by service level
- Store replenishment plan
- Lost demand recovery after stock-outs
- Customer and loyalty member segmentation
- Customer and loyalty churn probability
- Product recommendations
Production
- Machine failure and downtime risk
- Quality deviation days
- OEE deviations
- Process scrap risk
Supply chain
- Supplier risk band with probability
- Delivery delay risk
- Reorder point recommendations
Sales
- Opportunity win probability
- Quote conversion probability
- Quarterly revenue forecast in three scenarios
- Customer lifetime value
- Customers breaking their order pattern
Finance
- Payment delay probability
- Collection prioritisation
Marketing
- Lead conversion probability
- Campaign return projection
Human resources
- Employee attrition risk
- Hiring success probability
Energy
- Energy consumption forecast with scenarios
- Energy intensity ranking
- Load shifting plan
Support and assurance
- Support ticket category and priority triage
- Accuracy validation report for every model
A forecast is not a decision. It is the third step towards one.
Prediction only pays off when it is connected to the decision and the action that follow it.
ERP, POS, MES, WMS, CRM and IoT — into one place.
Operational data related and put in business context.
Demand, stock, production and performance, ahead of time.
Decision models and AI weigh the options with you.
Decisions executed through workflow and automation.
Predictive analytics for operations, not for reports
Most predictive analytics software stops at the forecast: a number, a confidence band, and a dashboard someone still has to interpret. That is the point where operational value usually leaks away, because a forecast nobody acts on costs exactly as much to produce as one that changes a decision.
Veraius treats prediction as the third step of a chain rather than the end of one. Demand, capacity, lead time and risk are forecast against your own operational history, and each forecast is carried forward into the decision it informs — with the assumptions behind it visible, so a planner can argue with the model instead of taking it on faith.
For operations teams, that means predictive analytics arrives attached to a specific proposed action: reorder this SKU, move this production run, review this supplier. The forecast is the reasoning; the decision is the output.
What your teams gain from Veraius agents.
A forecast grounded in your history
A hierarchical 12-month sales forecast is built from your own orders and reconciled with external factors.
See the Sales agents Supply ChainStock where demand will be
Replenishment is driven by the demand forecast, so stock moves before shortages do.
See the Supply Chain agents ManufacturingAnswers from the real query
Every figure comes from a query against your production data and links back to the records behind it.
See the Manufacturing agentsIn every department
- Agents prepare the work on your own data; your people approve what happens next
- Plain-language questions answered from live ERP data, with tables and interactive charts
- Grounding check: numbers that cannot be traced to a source are not shown
- Explainability, confidence and risk scores with decision rationale
- Slack, Teams, WhatsApp and Signal channels with an approval inbox
- Department-based access policy with personal data masking
Frequently asked questions
Straight answers about integrations, deployment, security and cost — including where the honest answer is "it depends".
Ask your own questionWhat does the platform forecast?
Demand, sales, inventory position, production capacity and lead times — plus risk and anomaly detection across all of them. Predictive analytics software is only useful here to the extent its output reaches a decision inside the window where that decision is still open.
How accurate are the predictions?
Accuracy depends on your data and your category, and quoting a number without seeing either would be marketing. The more useful question is what the forecast is for: a slightly worse forecast that arrives in time to change an order is worth more than an excellent one that arrives after it is placed.
Does it show the uncertainty behind each prediction?
Yes, and that is the part most systems discard at the interface. A point estimate throws away the one thing that would have changed the action; a wide interval should produce a more conservative order or a held review, not the same number with a confidence badge next to it.
Can we plug in forecasts our own data science team built?
Yes. A forecast your data science team already trusts is worth more than a better one they did not build. The platform consumes it as an input and takes responsibility for the layer above — turning the prediction into a decision, with its constraints and its record.