14 Use Cases. One Predictive Database.
The ERP that learns — built on Aito's _predict, _relate, _evaluate, _search, _match
Procurement, intelligence, products, services — every view in this open-source reference is built on Aito's predictive operators. Three industry profiles (industrial maintenance, multi-channel retail, professional services) drive the same code.
14 ERP Features Grouped by Function
Procurement, intelligence, product/pricing, services. Pick a feature to read its implementation guide on GitHub.
Procurement
Predictive routine that pre-fills your AP team's morning — POs route themselves, smart entry predicts four fields from one supplier pick.
PO Queue
Predicted account, cost center, approver — visualised in three confidence tiers
View Use Case →View Source →
Smart Entry
Pick a supplier, four fields predict in parallel with cross-highlight
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Approval Routing
Predicted approver via _relate; nothing becomes policy without explicit signoff
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Automation Overview
Real learning curve from routed_by × order_month — €220K savings YTD
View Use Case →View Source →Intelligence
Anomalies, supplier risk, pattern discovery — surfaced, not coded.
Anomaly Detection
Inverse prediction. Three types — amount spike, unknown vendor, mis-coded account
View Use Case →View Source →
Supplier Intel
Spend leaderboard plus delivery-risk discovery via _relate
View Use Case →View Source →
Rule Mining
High-lift patterns ranked for governance; promote with audit trail
View Use Case →View Source →
Recommendations
Cross-sell + similar-product discovery via _search match — Aurora tenant
View Use Case →View Source →Product & Pricing
Catalogue gaps, fair-price bands, seasonal demand, replenishment.
Catalog Intelligence
Multi-field gap-fill on workflow-blocking products — bulk apply across the catalog
View Use Case →View Source →
Price Intelligence
Fair-price band + Purchase Price Variance with annualised exposure
View Use Case →View Source →
Demand Forecast
Seasonal blend, same-month aggregation, stockouts-prevented and excess-avoided in €
View Use Case →View Source →
Inventory Intelligence
Critical / Low / OK / Overstock with tied capital and weekly margin at risk
View Use Case →View Source →Services & Operations
Project success, utilisation, capacity — beyond procurement.
Implementation Details
Explore the source, the three tenant profiles, and the architecture decisions
{
"from": "purchases",
"where": {
"supplier": "Lindström Oy",
"description": "Workwear order"
},
"predict": "cost_center",
"select": ["$p", "feature", "$why"]
}
Why This Pattern Works for ERP SaaS
One predictive database, three industry profiles, every override learned from instantly
SQL-Like Predictions
- _predict, _relate, _search, _recommend
- No feature engineering required
- Confidence + $why on every prediction
Three Profiles, One Codebase
- Metsä Machinery (industrial maintenance)
- Aurora Retail (multi-channel commerce)
- Helsinki Studio (professional services)
Production-Ready Architecture
- Next.js 16 App Router + FastAPI
- 2-layer cache, schema link traversal
- 26 pytest tests, TypeScript strict
Evaluate in 15 Minutes
From live demo to your own data in three steps
Address
Episto Oy
Putouskuja 6 a 2
01600 Vantaa
Finland
VAT ID FI34337429
