Recommendations
A suggested next step, with the reasoning shown.
Which invoice to chase first, which supplier terms to renegotiate, which cost line has drifted. Suggestions ranked by what they are worth, each one traceable to the data behind it.
- Category
- AI
- Status
- Planned
- Depends on
- 1
- Connectors
- 0
What it does
Recommendations, in practice
The parts that matter day to day, not a feature matrix.
Anomaly
Gross margin fell 1.9 points. Two thirds of the movement is one customer group moving onto a discount tier; the rest is a freight cost that stopped being recharged.
Opportunity
€245,000 of receivables sit just past terms with customers who have never gone further. Chasing this group first is worth more than chasing the oldest.
Illustrative product screen — sample data, not a measured result.
Works with
Recommendations needs the ledger underneath it
Modules write through one engine, so what this one needs is a dependency rather than an integration project.
Often switched on together
- In developmentExplore
Automated analysis
The system reads the numbers before you do.
- In developmentExplore
AI assistants
Ask a question about your business and get an answer with its workings.
- PlannedExplore
Forecasting
Forward numbers built from the same data as the backward ones.
- AvailableExplore
Market intelligence
What is happening outside the business, filtered to what matters.
Industries
Where this does the most work
Problems it removes
What people come to us with
AI
Recommendations is on the way. Tell us how you would use it.
What gets built next is decided by the businesses that need it. That conversation is how a module moves up the list.