# AI Payment Collection and Reminders | Super Cat > AI payment collection that recovered US$1M in its first 3 months: timed reminders with a secure payment link that stop the moment the customer pays. Source: https://www.super-cat.tech/portfolio/ai-payment-collection Last modified: 2026-10-22 [← All case studies](https://www.super-cat.tech/portfolio)US$1M collected in the first 3 months # AI Payment Collection and Reminders The companion product to the AI front desk: it chases unpaid invoices with polite, timed reminders and a payment link, and stops the moment the customer pays or replies. It collected over US$1 million for the client in its first three months. ## The problem Small businesses are owed money by customers they want to keep. Chasing payment is awkward, easy to postpone and usually done late, by the same person who should be doing billable work, so receivables age and cash flow suffers. ## What we built An automated collections flow: reminders sent on a schedule the business sets, over the channels customers actually read, each carrying a secure payment link; replies are read and routed, disputes go to a person, and the sequence stops as soon as an invoice is paid. Before: Chasing payment is awkward and late; Billable time lost to collections; Receivables age, cash flow suffers. After: US$1M collected in the first 3 months.Before - Chasing payment is awkward and late - Billable time lost to collections - Receivables age, cash flow suffers How it works - 01 Remind on the business’s schedule. Reminders go out on a cadence the business sets, over the channels its customers actually read. - 02 Pay through a secure link. Each reminder carries a payment-provider link; the agent never handles card details. - 03 Read the replies. Replies are read and routed, and disputes go to a person. - 04 Stop the moment it is paid. The sequence ends as soon as the invoice is paid or the customer replies. After US$1M collected in the first 3 months Stack AI Agent Payments Automation Messaging ## Why it holds up Collections is where an AI agent can do real damage, so the agent holds no payment credentials — the payment provider takes the card, and the agent only ever sees status. Tone, frequency and stop rules are set by the business, not improvised by the model, so a loyal customer is never chased like a stranger. Related service: [Secure AI agent development](https://www.super-cat.tech/secure-ai-agent-development) ## More case studies Big 4 client · fully on-premise ### Enterprise RAG Chatbot for a Big 4 Accounting Firm Multimodal retrieval over a Big 4 firm’s full compliance library, tuned for citation instead of summary — every answer arrives with its source paragraph attached. - Juniors reading thousands of pages per question - Seniors acting as search engines Read the case → 200k calls/month · 67% booking rate vs 77% human ### 24/7 AI Agent Call Center: 200k Calls a Month Across 60 US HVAC Brands Voice agents orchestrating 200,000 calls a month across 60 US HVAC brands for a 5,000-person enterprise, booking 67% of calls against 77% for human agents — within 10 points of a trained team, around the clock. Built as a product, an AI front desk that answers, books and routes, not a pilot. - Night and weekend calls unanswered - Staffing every shift hard to justify Read the case → Ask the EDI in plain English ### Natural-Language EDI for a US EDI Solutions Vendor Business users ask questions of raw X12 — “what are the net terms on invoice 4471?” — and get answers in plain English, from a Llama 3 model fine-tuned on EDI and grounded in the parsed transactions. - Business and EDI teams speak different languages - Rejected transactions decoded by hand Read the case → Get started ## Talk to the engineer who will build it. You will speak with me directly. Bring the use case, the data and the constraints, and I will give you a straight view on feasibility, effort and the first thing to ship. [Book a call](https://calendly.com/super-chain/catyung)Hong Kong & London time zones