AI SaaS for Wholesale and B2B Ordering

B2B ordering portals exist; customers still send emails

Most wholesalers have tried a B2B portal. Some customers use it. Plenty do not. A catering supplier's chefs send a text at eleven at night, a builders' merchant's customers email a photo of a handwritten list, a pharmacy wholesaler receives spreadsheets in each group's own format. A sales office of eight people spends its morning typing these into the ERP.

Forcing customers onto a portal rarely works when the customer is the one with choices. The opportunity for an AI product is to accept orders however they arrive and make them look, to the ERP, as if they had come through the portal all along.

Product ideas with obvious returns

• Multi-channel order capture. Read orders from email bodies, PDFs, spreadsheets, photos and voice messages, and create draft sales orders in the ERP.
• Product matching. Map a customer's description, such as "the usual blue gloves, large", to the correct SKU using order history, the customer's own codes and the catalogue.
• Order anomaly checks. Flag quantities that look wrong against the customer's history, such as 100 cases instead of 10, before the order is confirmed.
• Reorder prompts. Notice when a regular customer has not ordered their usual lines and draft a friendly prompt for the sales rep.
• Supplier price file ingestion. Read supplier price lists and promotions in varied formats and prepare catalogue updates for approval.
• Customer query assistant. Answer order status, delivery slot and invoice copy questions using live ERP data.

We describe the single-business version of this in order processing automation for distributors.

Product matching is where accuracy is won or lost

Reading the order is the easy half. Knowing which of 12,000 SKUs a customer means is the hard half. Customers use old codes, nicknames, competitor product names and pack sizes that differ from the catalogue.

Every confirmed match should be stored as a customer-specific alias. After a few weeks, most orders from regular customers match with very high confidence because the product has learned their language.

What the model must never decide

1. Prices. Customer-specific pricing, contract prices and promotions come from the ERP.
2. Stock availability. The ERP or warehouse system confirms stock; the model only reports it.
3. Credit limits and account holds. Deterministic checks, with anything on hold routed to credit control.
4. Substitutions. Suggest alternatives if a line is out of stock, but a person or the customer approves.

The model reads what the customer wanted. The ERP decides what the customer gets and what it costs.

The ERP integration reality

Wholesalers and distributors run everything from modern cloud ERPs to heavily customised on-premise systems installed many years ago. Some have clean APIs for creating sales orders; others need EDI, database-level integration or an integration partner. Customisations mean two companies on the same ERP can still have different order structures.

For a product company, this suggests choosing an initial market defined by ERP as much as by industry. Selling to food wholesalers on one specific ERP is narrower but much faster than supporting every food wholesaler. Our note on software for wholesale and distribution describes the systems we typically encounter.

Ideas that sound good and struggle

Dynamic pricing for wholesale is often pitched as the big AI opportunity. In practice, B2B prices sit inside negotiated agreements, rebates and long relationships, and a sales director will not hand them to a model. Margin analysis that shows reps where discounts have crept is far easier to sell.

A fully conversational ordering bot for trade customers is the other. Regular customers want speed, not conversation. A reply that says "got it, 12 lines, delivery Thursday, one item substituted" beats a chat every time.

A worked example for a distributor

Take an illustrative distributor receiving 300 orders a day, of which two-thirds arrive by email, PDF or photo. At three minutes per order to key in, that is around ten hours a day of data entry. If capture and matching handle most orders with a quick confirmation, the sales office recovers several hours daily and cut-off times can move later in the afternoon, which customers notice.

Errors drop too, and in wholesale an error is expensive: a wrong delivery, a return, a credit note and an irritated customer. Measure picking errors and credits caused by order entry mistakes before and after, alongside time saved.

How SpiderHunts would build it

At SpiderHunts we would begin with email and PDF order capture into one ERP, with product matching based on customer history and a review screen for anything uncertain. The review screen's confirmations train the alias table, and the evaluation set is built from real orders with known correct lines. Reorder prompts come later, once the order data is flowing cleanly.

The capture and matching sit within our automation work, and the multi-tenant product within SaaS development. Pricing per order line processed or per order tends to suit wholesalers, who already think in cost per order.

Frequently asked questions