Sales, demand and cash flow forecasts; risk scores for customers, suppliers and applications; anomaly alerts for unusual activity. All from your own data, with the reasoning.
Decision support systems use models that learn from historical data to produce forecasts and risk assessments. How much of a product will sell, how much cash will be on hand next month, or how risky a loan application is gets calculated by a model instead of estimated in Excel spreadsheets.
ErucaAI trains these models with your own data in Eruca ERP and shows the reasoning behind every result: you can clearly see which factors drove a score the most. The final decision always stays with people; the model makes decisions faster and more consistent.
Demand forecasts by product and warehouse, accounting for seasonality, campaigns and trends.
30-60-90 day cash projections from receivables and payables due and past collection behavior.
Risk scores for customers, suppliers or applications from your own historical data.
The most influential factors behind every score and forecast are shown.
Unusual invoices, payments, prices or stock movements are reported to the right person, with the reason.
A loan application, quote or complaint file is turned into a committee-ready summary.
The impact of price, exchange rate or demand changes on outcomes is calculated in advance.
Forecast accuracy is measured continuously; the model is retrained when the data changes.
The model learns from historical data, results are validated and placed in front of the decision-maker with their reasoning.
Institutions looking to shorten decision time with application scoring, early warnings and committee summaries.
Businesses planning inventory and orders with demand forecasts by product and branch.
Factories that want to see material requirements and capacity load in advance.
CFO teams that want to monitor cash flow and collection risk weeks in advance.
The model learns past sales together with seasonality, price, campaigns and other factors, and produces next-period forecasts by product or warehouse. Forecast accuracy is measured every period and the model is updated.
ErucaAI shows the most influential factors behind every score and keeps the model version, training data and decision in the audit trail. That way, why a decision was made can be explained later.
For demand forecasting, at least 12–24 months of transaction data is usually recommended. Risk scoring needs a sufficient number of past records with known outcomes; we assess your data together during the pilot.
No. The model produces scores, forecasts and reasoning; the final decision always rests with an authorized person. If you wish, rule-based automatic approval can be defined for low-risk transactions.
Situations such as unusual amounts, duplicate invoices, unexpected price changes, transactions at unusual hours and unexplained stock differences are reported to the right person.
Pick a real business problem; in a short pilot we connect your data, measure in shadow mode and review the results together.
Describe a real business problem; we'll define the pilot scope together.
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