The Situation
The wholesaler supplied roughly 120 retail accounts, and every one of them ordered differently. Some emailed PDF purchase orders. Some sent a photo of a handwritten order over WhatsApp. A handful used a basic web form on the company site. None of it arrived in a consistent format, and all of it landed in the same place: a person's inbox, waiting to be read, understood, and typed into the system by hand.
That manual re-keying was the bottleneck for the entire business. An order couldn't be fulfilled until someone transcribed the line items, matched product names to SKUs, checked pricing against the account's agreed rates, created an invoice, logged it in QuickBooks, and finally told the warehouse to pack it. During busy weeks orders stacked up for hours — sometimes a full day — before anyone got to them. Mistyped quantities and mismatched SKUs led to wrong shipments, credit notes, and irritated retailers.
The ops team was spending close to 25 hours a week just moving information from one format into another. Leadership knew the work added zero value; it was pure friction between a customer placing an order and the warehouse shipping it. They wanted that friction gone without forcing 120 accounts to change how they buy.
What We Did
We built an end-to-end order-to-cash pipeline that meets each customer where they already are, then carries the order all the way through to fulfillment and accounting with no manual transcription.
The pipeline has five stages:
- Multi-channel intake. Orders are captured from every source — email PDF attachments, WhatsApp messages and images, and the web form — and funneled into a single processing queue regardless of origin.
- AI extraction. An LLM reads each order and pulls out structured line items: product, quantity, and any notes. It handles messy free-text, abbreviations, and photographed handwriting, normalizing everything into a clean order record.
- Validation and matching. Extracted items are matched against the product catalog in Postgres, priced against each account's agreed rate card, and checked for obvious errors — unknown SKUs, impossible quantities, or pricing that drifts from the agreement. Anything ambiguous is flagged for a quick human review instead of being silently guessed.
- Automated invoicing and accounting sync. Once an order passes validation, the workflow generates the invoice and writes it straight into QuickBooks via its API, so the books stay current without a second data-entry step.
- Fulfillment trigger and status updates. A confirmed order automatically signals the warehouse to pack and ship, and the customer receives a status update on the same channel they ordered from — closing the loop without anyone chasing it.
We orchestrated the whole flow in n8n so each stage is visible and editable, and we ran it in parallel with the manual process for the first ten days. Every AI-extracted order was reviewed against the human version until accuracy was high enough to trust the pipeline on its own.
The Results
Within five weeks the pipeline was auto-processing 94% of incoming orders with no human involvement, and the ops team's manual data-entry load dropped from about 25 hours a week to roughly three — reclaiming 22 hours every week for work that actually grows the business.
The downstream effects mattered just as much. Because extraction, pricing, and SKU matching are now validated automatically, invoicing errors fell by 71%, which meant far fewer wrong shipments and credit notes. Orders that once waited hours to be keyed in are invoiced within minutes of arriving, and customers get a status update on the same channel they used to order. The wholesaler now runs its entire order-to-cash cycle as a quiet background process instead of a daily scramble.
