GL coding that learns: how vendor memory replaces rule tables
Legacy AP tools code invoices with rules someone has to write and maintain. Slingstone learns from your team instead — and shows its work.
2 min read · Slingstone
The rule-table treadmill
Most AP systems code bills with lookup rules: this vendor goes to this account, this keyword to that department. The rules start helpful and end as a brittle maze nobody wants to touch, because changing one breaks three others.
Vendor memory
Slingstone keeps a memory of how your team codes each vendor in each company — accounts, departments, classes, locations and how lines are usually split. When a new bill arrives, AI combines that memory with what's actually on the invoice to propose coding.
Because it learns from approvals and corrections, the memory improves on its own. There's no rule table to maintain.
Confidence and reasoning
Every suggestion comes with a confidence score and a short explanation: 'Coded to Repairs & Maintenance because the last 14 bills from this vendor in this company were coded there, and the line describes HVAC service.' Reviewers can see at a glance which bills need attention.
Earned autonomy for coding
Teams typically start with AI in suggest mode. As accuracy is proven for a vendor or category, coding can move to automatic for low-risk bills, while anything unusual still goes to a person.
Legacy tools vs. Slingstone
| Legacy tools | Slingstone |
|---|---|
| Hand-written coding rules | Learned vendor memory per company |
| Silent rule matches | Confidence score and reasoning on every line |
| Rules decay as the business changes | Memory updates from every approval |
| All-or-nothing automation | Autonomy earned vendor by vendor |