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Four systems that didn't talk to each other. A finance team cross-referencing spreadsheets by hand. This decision engine sits in the middle — payment arrives, engine analyzes, Siigo generates the document, Bitrix24 updates the status. No human opens any of them. Full audit trail on every transaction.
The Politécnico de la Costa Atlántica manages tuition payments, fees, and student enrollment across multiple campuses in Colombia. When a student pays, the finance team had to manually cross-reference four systems — Mercado Pago for the payment, Siigo for the accounting, Bitrix24 for the student status, and bank flat files for wire transfers. Every payment meant someone physically checking balances, generating documents, and updating records.
These are the operational bottlenecks that AI automation eliminates in a 2-week pilot.
Manual payment processing — every tuition payment, fee, or service charge required a finance team member to check Siigo, verify the payment source, decide what document to generate, and create it by hand. Hours of work per day.
Four disconnected systems — Mercado Pago tracked payments but not balances. Siigo tracked invoices but not payment status. Bitrix24 tracked enrollment but not financial history. Bank files arrived as flat text with no API. The finance team was the human glue between them all.
No automated decision logic — should this payment generate a receipt or an invoice? Should the student move to ADM or Matricula? What if they have a credit balance? What type of negotiation applies? Every decision was a judgment call based on scattered data.
No CRM synchronization — when a student's financial status changed, Bitrix24 didn't update. Admissions didn't know enrollment was confirmed. Support staff didn't know a payment was pending. Departments worked from stale data.
An integration hub with a business rules decision engine at its core. Four enterprise APIs connected through one orchestrator. When a payment arrives — via Mercado Pago webhook, bank flat file import, or treasury API — the engine activates. It pulls the student's financial history from Siigo: outstanding invoices, credit balances, payment history, and negotiation type. It checks Bitrix24 for CRM status and service type. It cross-references the payment amount against what's owed. Then it decides: generate a recibo de caja (payment receipt) in Siigo, generate a factura de venta (sales invoice) in Siigo, update Bitrix24 to ADM or Matricula status. Every action executed through API calls. No human opens Siigo. No one manually updates Bitrix24. The finance team monitors — the engine processes. Edge cases handled automatically: partial payments generate receipts for the paid portion while keeping the balance outstanding, credit balances are applied before new invoices, and negotiation types that override standard routing are honored. Every decision logged for audit.
Every technology in this stack is production-proven across CAMTECH AI client engagements.
Payment arrives via Mercado Pago, bank file, or treasury → decision engine pulls financial history from Siigo, checks CRM status in Bitrix24, cross-references negotiation type → auto-generates receipt or invoice in Siigo → updates student status in Bitrix24. Every action traced. Every decision logged. Nothing manual.
Architecture diagram coming soon
The finance team went from manually cross-referencing four disconnected platforms for every payment to monitoring a single automated flow. Processing time collapsed. Errors from manual data entry disappeared. Student status updated instantly when payments cleared. Departments stopped working from stale data. Payment volume no longer limited by team capacity — the engine scales with the institution.
Every CAMTECH AI engagement starts with a scoped 2-week pilot and a measurable outcome. Book a discovery call and we'll scope your first project.
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