WhatsApp AI Sales Agent with M-Pesa Checkout
An AI sales agent that runs entirely inside WhatsApp — search a real catalogue, get a grounded recommendation, and pay by M-Pesa — with the owner SMS’d the moment a sale lands.
Small businesses sell over WhatsApp by hand — answering "do you have...", quoting prices, collecting delivery details across scattered messages, and sending M-Pesa links manually. It doesn’t scale past a handful of conversations at once, and putting a free-roaming LLM in front of it is worse: left to decide what happens next, it can invent a product that doesn’t exist, misquote a price, or claim a payment succeeded when it didn’t — a liability the moment the AI is one step from moving real money.
Built a deterministic state machine, not an autonomous agent: ten explicit conversation states in Postgres, one function as the sole writer of state, and the LLM used for exactly one job per message — classify intent and extract entities as strict JSON — never seeing a price, a total, or a payment outcome. Product search, stock and order totals come straight from the database through a four-tier search ladder, so the model can recommend but never invent. M-Pesa checkout is fully verified — signed STK push, callback verification, duplicate-payment protection, and a scheduled job that reconciles payments even when Safaricom’s callback never arrives. Credentials live in Supabase Vault, inbound webhooks are signature-verified, and 176 automated assertions across five test suites run before every deploy.
A working demo takes a customer from "I need a laptop for programming" through product selection, delivery details, M-Pesa payment and confirmation, with the owner automatically SMS’d the order and receipt. Refactored from an earlier build that ran two conflicting AI agents across 11 workflows and 172 nodes — and had processed zero successful payments — down to 6 focused workflows with a single point of AI involvement, plus the payment-recovery safety net the original design lacked.
Source is private — the owner does not permit public access to this codebase. A walkthrough is available on request.
More automation work
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