Case Study
6 hours of manual quoting, replaced by AI that does it in under five minutes
How The Buying Network, a commercial marine purchasing agent, went from manually deciphering messy RFQs to an AI-powered matching system that turns unstructured requests into quote-ready data, almost instantly.
6 hrs → <5 min
RFQ processing time per request
~98%
Reduction in manual data entry per RFQ
~90%
First-pass match accuracy
~$80K+/yr
In labor reallocation
The Problem
Quoting was the bottleneck. It was all manual.
The Buying Network is a purchasing agent and distributor serving the commercial marine industry — fishing vessels, seafood processors, and ships operating in remote locations like Alaska. They carry everything from galley supplies and rain gear to marine chemicals and engine room filters. When a vessel or processor needs supplies, they send an RFQ.
The problem is how those RFQs arrive. A fishing vessel captain preparing for a season in the Bering Sea doesn't send a neatly formatted spreadsheet with part numbers. RFQs come as PDFs, Excel files, scanned documents, or long emails — filled with inconsistent item descriptions, missing part numbers, shorthand, and industry jargon.
Before this project, processing a single RFQ meant Clay's team had to read through every line, interpret what the customer was actually asking for, search their inventory and vendor catalogs to find the right match, and manually enter each item into their quoting system. Some RFQs took up to six hours to process.
RFQs arrived in wildly different formats (PDFs, spreadsheets, emails) with inconsistent descriptions, missing part numbers, and marine industry shorthand
A single complex RFQ could take up to 6 hours of manual processing before quoting could even begin
Matching requested items to the correct products required deep institutional knowledge that was difficult to scale or delegate
No off-the-shelf quoting tool could handle the unstructured, unpredictable nature of real-world commercial marine RFQs
The Solution
An AI-powered RFQ matching engine built for how the industry actually works
We built RFQ Match Pro, a custom AI-assisted tool designed specifically for the way The Buying Network processes requests. The system takes the messy, unstructured reality of incoming RFQs and converts them into clean, structured, quote-ready data with minimal manual effort.
AI Line Item Extraction
The system reads incoming RFQs regardless of format (PDFs, Excel files, emails) and extracts each line item automatically. No more manually copying descriptions row by row. The AI interprets inconsistent naming, shorthand, and partial descriptions to understand what the customer is actually requesting.
Multi-Source Matching Engine
For each extracted line, the system searches across multiple data sources in priority order: the customer's past purchase history first, then the internal item master database, then any loaded vendor catalogs. Each suggested match is labeled with its source, so the team knows exactly why a particular product was recommended.
Confidence Scoring & Fast Review
Every match comes with a confidence score. High-confidence matches can be approved quickly; low-confidence or unmatched lines are flagged for human review. The interface places each RFQ line alongside its suggested match for fast side-by-side comparison, turning what used to be a research task into a simple review-and-approve workflow.
Confirmed Match Learning
When the team approves a match, the system remembers it. The next time a similar line item appears in a future RFQ, whether it's from the same customer or a different one, the tool draws on that confirmed history to suggest the same match automatically. Over time, the system gets faster and more accurate, building institutional knowledge into the software itself.
The Results
From a six-hour slog to a five-minute review
The impact was dramatic and immediate. RFQs that previously consumed up to six hours of manual processing now move through the system in under five minutes. With ~90% first-pass match accuracy, the team's role shifted from manual data entry and product research to reviewing and confirming AI-suggested matches — a fundamentally different and faster workflow.
But the time savings are only part of the story. The reduction in manual processing freed up enough staff capacity to redirect roughly $80,000 a year in labor toward higher-value work. By codifying the team's institutional knowledge into the matching engine, The Buying Network removed a critical dependency on individual expertise — product matching no longer requires decades of marine supply experience.
For a 25-year-old business operating in a demanding, relationship-driven industry, RFQ Match Pro didn't just save time — it turned the quoting process from a bottleneck into a competitive advantage.
“I was brought in to help The Buying Network find someone who could pull off an AI solution that felt like a real stretch. I really didn't know if it was possible, but Jason absolutely delivered. He was on time, on budget, and super clear. Most importantly, he stuck with us through the real-world challenges to get it adopted by the people who needed to use it. If you're looking for someone solid who follows through, I'd highly recommend Jason.”
Related Insights
How AI Is Actually Being Used in Mid-Market Operations (Not Just Hype)
AI in operations isn't about robots replacing your team. It's about eliminating the manual data entry and document processing that buries skilled people in grunt work.
What to Expect from a Custom Software Engagement (And How to Not Get Burned)
Most bad custom software projects fail in discovery, not development. Here's what a good engagement looks like from first call to go-live — and the red flags to watch for.
Spending hours on work that should take minutes?
If your team is buried in manual data entry, document processing, or repetitive operational tasks, let's talk about what AI-assisted automation could look like for your business.
Book a Free AI + Ops Review →