AI & intelligence
Decisions with the evidence attached
The intelligence layer reads the same ledger your team works in. It explains every recommendation and never acts on its own.
AI & intelligence
Decisions with the evidence attached
The intelligence layer reads the same ledger your team works in. It explains every recommendation and never acts on its own.
- 01
Business data
Purchases, stock, recipes, production, sales and waste from the ledger.
- 02
Analysis
Data-quality checks, anomaly detection and forecasts per item and location.
- 03
Intelligence
Procurement, production, waste and financial signals scored and ranked.
- 04
Insight & recommendation
Plain-language recommendations with the numbers behind them.
- 05
Decision support
Your team accepts, adapts or dismisses — and the outcome is tracked.
Anomaly detection
Flags unusual cost, usage, waste or receiving patterns before they become a month-end surprise.
Demand & usage forecasts
Item-level forecasts by location feed replenishment and production planning.
Explainable recommendations
Reorder, re-price, re-recipe or investigate — each with its evidence and expected impact.
Scenario modelling
Test supplier, price and menu changes against real cost structures before committing.
Assistant on your data
Ask questions about your own operation; answers cite the underlying reports.
Data-quality monitoring
Surfaces missing costs, stale prices and unmapped items that would distort every report.
In practice
What the intelligence layer does for an operation
Reorder before a stock-out
Cover days from forecast usage versus supplier lead time turn into a ranked reorder list per location.
Protect menu margin
Landed-cost drift on key ingredients flags the menu items whose margin fell below target.
Find avoidable waste
Waste tickets are compared against station baselines to surface outliers with the cost attached.
Plan production quantities
Forecast demand by outlet suggests batch sizes for the central kitchen and reduces over-production.
AI features use a configurable provider and can run fully disabled. Recommendations are advisory; nothing is posted to the ledger without a person.
In the product
Recommendations you can audit
Every signal shows the evidence that produced it and the impact it expects. Your team accepts, adapts or dismisses — and the outcome is recorded against the recommendation, so the system's usefulness is measured, not assumed.
Open recommendations
0
Accepted 30 d
0 %
Anomalies
0
| Signal | Evidence | Impact |
|---|---|---|
| Reorder lamb shoulder | 2.4 d cover vs 3 d lead | Prevents stock-out |
| Re-cost sea bass | Landed cost +6.8 % / 14 d | Margin +4.8 pts |
| Prep-trim outlier | 2.3× station mean | ≈190 / wk |
Run your operation on AKROUN Hospitality ERP
Purchasing, receiving, inventory, recipes and costing, production, transfers, sales and waste share one ledger — so reports, approvals and AI recommendations are built on what actually happened.