Every B2B distributor has a bottleneck nobody puts on a slide: order entry. For our client, a regional industrial supply distributor moving $90M in annual product, that bottleneck was a room of people retyping purchase orders. Orders arrived by email, PDF, fax, and EDI, in dozens of formats, and a team of six spent most of their day copying line items into the ERP by hand. Average handling time was 7 minutes per order. Error rate was high enough that returns and credits were a standing line on the P&L. They didn't need a new website. They needed the retyping to stop.

The Problem

The company received roughly 1,400 purchase orders a day across five channels. No two customers formatted an order the same way. Some sent clean CSVs. Most sent PDFs with the part numbers buried in a table. A stubborn few still faxed handwritten sheets.

The order entry team manually opened each one, matched every line to an internal SKU, checked pricing and stock, and keyed it into the ERP. It was slow, and worse, it was error-prone. A transposed part number meant the wrong product shipped, which meant a return, a credit, and an angry buyer.

The numbers were rough:

• Average processing time: 7 minutes per order
• Order entry team: 6 full-time staff
• Daily volume: ~1,400 orders
• Entry error rate: ~4.5%, driving returns and credits
• Peak season backlog: orders sitting 6+ hours before entry, delaying fulfillment

The real cost wasn't just labor. It was the compounding damage of errors: reshipping, restocking, and customers who quietly moved volume to a competitor after the third wrong delivery.

Our Approach

We proposed a system that reads inbound orders, understands them regardless of format, validates them against the catalog, and only involves a human when it genuinely needs one. Three phases, one principle: automate the routine, escalate the ambiguous.

Phase 1: The Format Problem (Weeks 1-2)

Before modeling anything, we collected two months of real inbound orders across every channel. Roughly 60,000 documents. We sorted them by format and channel to understand the actual distribution, not the imagined one.

The finding that shaped the build: 82% of order volume came from just 40 recurring customers, each with a consistent format. The long tail of one-off formats was loud but small. That meant we could get most of the value fast by nailing the top formats first, then widening coverage.

Phase 2: The Extraction Engine (Weeks 3-5)

We built a pipeline that ingested any inbound document and pulled structured line items out of it. An LLM layer handled the messy reasoning: matching a customer's shorthand part reference to the correct internal SKU, reading a table even when columns shifted, and interpreting quantities written three different ways.

The key design choice was confidence scoring on every line. The engine didn't just extract a part number, it scored how sure it was. High-confidence lines flowed straight through. Anything below the threshold got flagged for review with the uncertain fields highlighted.

That distinction mattered. A system that silently guesses on a fuzzy fax is worse than the manual process, because now the error is invisible until it ships. By making uncertainty explicit, we turned the AI into a fast first pass rather than an unaccountable black box.

Phase 3: Validation and ERP Integration (Weeks 6-8)

Extraction was only half the job. Every order still had to be checked against business rules before entry:

• Does the SKU exist and is it active?
• Is the requested quantity in stock or on backorder?
• Does the price match the customer's contracted pricing tier?
• Are there minimum order or pack-size constraints?

We wired these checks into the pipeline so validated orders wrote directly into the client's ERP through its API. Clean orders posted in seconds. Orders that failed a rule, or carried a low-confidence line, landed in a review queue for the order team, with the exact issue called out.

The team's job changed from typing every order to handling only the exceptions. Same people, radically different work.

The Technical Stack

For teams interested in the implementation details:

• Ingestion: Unified intake across email, PDF, fax-to-image, and EDI, normalized into a single processing queue
• Extraction: Vision and text models for document parsing, with an LLM layer mapping customer part references to internal SKUs
• Validation: Rules engine checking catalog, stock, pricing tier, and pack constraints before any write
• Integration: REST API posting validated orders directly into the ERP, with a review queue for exceptions
• Monitoring: Daily accuracy reports and drift alerts if any customer format starts failing above threshold

Total build time: 8 weeks from kickoff to production. The system covered 82% of volume in the first two weeks live and climbed as we added formats.

Results

We tracked performance over the first 90 days post-launch.

Processing time: 7 minutes down to 2 minutes per order. A 71% reduction in average handling time. For the high-confidence orders that flowed straight through, human touch time was effectively zero.

Entry errors dropped 83%. Because the system validated against live catalog and pricing data before writing, the transposed-part-number problem largely disappeared. Returns and credits tied to entry errors fell sharply the same quarter.

Straight-through processing hit 74%. Roughly three of every four orders posted to the ERP with no human touch at all. The remaining quarter flowed through the review queue, where the team resolved flagged items far faster than entering full orders from scratch.

The peak-season backlog vanished. Orders that used to sit 6+ hours during rush periods now entered within minutes. Fulfillment stopped waiting on data entry.

The team was redeployed, not cut. The client moved four of the six order-entry staff to customer account management and exception handling, higher-value work that had been chronically understaffed. Capacity went up without new headcount.

Key Takeaways

This project reinforced a few things we believe about AI in operations:

1. The best automation targets a boring, expensive bottleneck. There was nothing glamorous about order entry. That's exactly why it was underinvested and ripe for a large, measurable win.

2. Confidence scoring is what makes AI trustworthy. The difference between a helpful system and a dangerous one is whether it knows when it doesn't know. Explicit uncertainty plus human review is what drove errors down instead of hiding them.

3. Follow the volume, not the edge cases. Nailing the top 40 customer formats delivered most of the value in weeks. Chasing every rare format first would have delayed the payoff for months.

4. Redeploy the people. The win here wasn't cutting a team, it was freeing one. The same six people now do work the business actually needed and never had time for.

If your operation is losing hours and margin to manual data entry, the pattern is probably already visible in your inbox. The orders are structured enough to automate, and the errors are expensive enough to justify it. We can help you build the system.