Service
Automating Receivables Control
Overdue receivables are the most expensive money in a company: it’s already been earned, but it isn’t there. While the debtor list is compiled by hand once a week, every day of delay is an interest-free loan to the customer at your expense. And calling a debtor a month later is a lot harder than on day three.
The automation here is simple in concept and demanding in execution: every morning the agent reads 1C (the ERP/accounting system standard in Russia), checks payments against contract terms, and assigns the sales rep a task on a specific debtor — with the amount, due date, and history. On its very first run on a wholesale company’s real data, this loop surfaced 18 debtors nobody had seen.
What's included
Scope of work
- Daily reconciliation. Every morning, a full pass over payments and due dates in 1C — no exports, no manual work
- A task per debtor. The sales rep gets a task in Bitrix24 with the amount, days overdue, and customer history
- Escalation. A repeat delay or a large amount — the task goes to the manager instead of getting lost in the list
- Digest for the manager. Total overdue and its trend — in Telegram, readable in a minute
- Access boundaries. Read-only to 1C; the agent can’t damage anything in your books
Indicative: receivables control is included in the AI Sales Employee (implementation 240,000 – 540,000 ₽) or the AI Agent Fleet (the whole fleet — 45,000 ₽/mo). As a standalone loop — from 180,000 ₽; exact cost after discovery.
How it works
Four steps from a conversation to a working system
Discovery
First run
Live loop
Support
Proof
How it has already worked
AI Sales Employee on 1C + Bitrix24
tasks from memory20 deals a dayAn autonomous agent reads 1C, finds debtors, dormant customers and stock shortages — and creates tasks for sales reps in Bitrix24 by itself, each with a ready-made call brief. The very first run surfaced 18 debtors and 143 short-stocked SKUs. 1C is read-only; writes to the CRM go through an allowlist of nine operations.
read the case study →Multi-agent systemAn AI Agent Fleet runs the product on its own
2–3 days of manual worka 3-line report at 08:30Eight autonomous AI employees — an economist, a CRO, a data analyst, virality, customer support, a product strategist, a content marketer and a supervisor — track the product's metrics, run the unit economics and send the owner a short digest every morning. The whole fleet of 8 agents — 45,000 ₽/mo instead of an entire department's payroll.
read the case study →Questions
What people ask about this service
How much does automated receivables control cost?
How does the agent find debtors?
Can we see results before implementation?
Is it safe to give access to 1C?
What happens with repeat delays?
Want to see your debtor list as early as discovery?
I reply the same day, usually within a couple of hours.