Automate Customer Invoice & Payment Questions with AI

Many finance teams rely on QuickBooks Online or Xero's built-in email templates. These systems send automated reminders but cannot understand a customer's specific reply. When a customer emails back, “Can you resend the invoice from June?” the automation stops. A human must log in, search for the invoice, attach a PDF, and manually write a reply. This process takes 5-10 minutes per inquiry.

Next, companies try generic chatbots from platforms like Intercom or Drift. These tools are excellent for sales but lack native integration with financial systems of record. To answer, “What’s my current balance?” the chatbot relies on manually programmed, static Q&A pairs that go stale instantly. The bot cannot access live data from your ledger, so it can only deflect the ticket, not resolve it, creating a frustrated customer and a support ticket that still requires manual handling.

Consider a 15-person B2B service company that sends 200 invoices a month. Their finance assistant spends nearly an hour every day answering simple status questions. A client emails, “What’s our outstanding balance and when is our next payment due?” The assistant must leave her primary work, log into Xero, find the client, sum three separate invoices, check their due dates, and compose an email. This 10-minute task, repeated six times a day, fragments focus and delays critical work like cash flow reconciliation.

The structural problem is the data gap. Customer communication tools are disconnected from financial systems. Off-the-shelf connectors can trigger simple actions but cannot perform the dynamic, multi-step lookups needed to answer natural language questions. They lack the logic to parse a question, query a database with joins, and synthesize a human-readable answer. This requires custom code.

The process would begin by auditing your financial data source and communication channels. Syntora would map the schemas in your QuickBooks, Xero, or custom database to identify where invoice numbers, due dates, and client IDs are stored. We would also analyze a sample of 100-200 past customer emails to understand the most common question patterns. This audit creates a clear blueprint for the AI agent's required capabilities.

Syntora would build an AI agent using the Claude API for natural language understanding and a FastAPI service as the secure intermediary. When a customer asks a question, the FastAPI endpoint receives it, authenticates the user, and uses the Claude API to extract intent and entities like an invoice number. The service then runs a prepared SQL query against a read-replica of your Supabase or PostgreSQL database to fetch the live data. This architecture ensures the AI never has write-access and responses are always based on the ground truth in your ledger.

For our own operations, Syntora built the financial plumbing connecting Plaid, Stripe, and a PostgreSQL ledger, with an Express.js API for transaction processing. We would apply this same engineering discipline to build your agent, ensuring fast, reliable, and secure data access. The delivered system is a private API endpoint that plugs into your existing chat widget or email server, providing instant answers without disrupting your workflow.