Brazos Valve Distributors supplies industrial valves and actuators to refineries, chemical plants, and water treatment facilities across the Texas Gulf Coast. They run a 15-person commercial team handling roughly 40 quote requests per day — half from supplier PDFs, half from customer email specifications.
Before Quotery, the process was spreadsheet-based. A sales rep opened the supplier's PDF, manually typed line items into a Google Sheet, looked up product codes against a shared Excel catalog, applied margin formulas, and formatted the output into a customer-facing PDF. Management reviewed quotes over email. The average quote took three hours from request to customer delivery. During peak season (spring turnaround), the backlog stretched to two days.
The trigger In October 2025, a supplier changed their PDF layout. Columns shifted, merged cells appeared where single cells used to be, and the SKU column moved from position 3 to position 6. The team's copy-paste workflow broke silently — sales reps were pasting wrong prices into wrong columns for three days before anyone noticed. Two quotes went to customers with incorrect pricing. One customer caught the error and questioned the company's reliability.
That incident made the case. The team needed a system that didn't depend on supplier document layouts staying stable, and that made pricing errors visible before they reached a customer.
Implementation Brazos Valve onboarded in November 2025. The implementation involved three steps:
- **Catalog import.** Their existing Excel product catalog (4,200 SKUs) was imported in a single CSV batch. The four-code matching system (SKU, import_code, internal_code, export_code) allowed each supplier's naming conventions to coexist without renaming anything in the master catalog.
- **User setup.** The 15-person commercial team was assigned the 'commercial' role — quote creation, product lookup, customer assignment. Three warehouse staff got the 'warehouse' role — stock receipts, delivery notes, inventory adjustments. Two managers got 'manager' — quote approval, plan configuration, reporting.
- **First import.** The team uploaded their backlog of 12 pending supplier PDFs. The AI importer processed all 12 in under 10 minutes. 68% of line items matched deterministically (exact code match). 27% resolved via AI matching against the catalog. 5% (mostly custom-fabricated valves with non-standard codes) landed as 'not found' and were manually assigned.
The team reviewed the results in 20 minutes. What previously took a full afternoon took under half an hour.
What they measured After five months of daily use (November 2025 — March 2026), Brazos Valve tracked specific metrics:
- **Quote time: 8 minutes average.** Down from 3 hours. The importer handles the data entry; the rep handles the review and pricing. The review UI's 'exact match / AI decision / not found' classification lets reps focus only on the lines that need attention.
- **Pricing errors: zero.** No incorrect prices have reached a customer since switching to Quotery. The combination of deterministic code matching (which prevents cross-SKU errors) and the review step (which surfaces unmatched lines) catches mistakes before quotes go out.
- **Quote volume: up 40%.** The same 15-person team now handles 55-60 quotes per day instead of 40. They didn't hire anyone. They freed the time that was going to data entry.
- **Peak season backlog: eliminated.** The spring 2026 turnaround season (February-March) processed all quotes same-day. The previous spring's two-day backlog didn't recur.
What surprised them The team expected the AI importer to save time. They didn't expect the stock reservation system to change how they quote.
Before Quotery, quoting and inventory were separate workflows. A rep quoted a valve, then separately checked whether it was in stock by calling the warehouse. If stock wasn't available, the quote went out anyway and the warehouse team dealt with the consequences later — partial shipments, backorders, apologetic phone calls.
With Quotery's reservation system, every quote line reserves stock at the moment of quoting. The rep sees real-time availability inline. If a valve has 12 units on hand and the quote needs 20, the rep knows before the quote goes out — they can split the line, flag a backorder, or adjust the quantity. The warehouse team sees reserved stock separately from available stock, so they know what's committed.
The warehouse manager described it as 'the feature I didn't know I needed.' The warehouse went from reactive (dealing with quotes that promised stock that didn't exist) to proactive (knowing exactly what's committed and what's available).
The numbers | Metric | Before Quotery | After Quotery | Change | |--------|---------------|---------------|--------| | Average quote time | 3 hours | 8 minutes | -95% | | Quotes per day (15 reps) | 40 | 55-60 | +40% | | Pricing errors reaching customers | 2-3 per quarter | 0 in 5 months | -100% | | Peak season backlog | 2 days | Same-day | Eliminated | | Supplier document formats handled | 1 (Excel template) | PDF, XLSX, CSV | Expanded |
What's next for them Brazos Valve is piloting the customer portal for their top five accounts. These customers currently request quotes via email, then call to check status. The portal gives them a self-service view of their open quotes, order history, and delivery tracking. The goal is to reduce status-check calls by 50% — freeing the commercial team to focus on new quotes rather than answering 'where's my order?'
They're also evaluating the consigned stock feature for three large refinery customers who keep Brazos Valve inventory on-site. Currently, consigned stock is tracked in a separate spreadsheet. Bringing it into Quotery would give both Brazos Valve and their customers a single view of what's on the shelf, what's been used, and what needs replenishment.
This is the kind of case study we want to write more of — not aspirational, but operational. Real companies. Real numbers. Real workflows. If your team has a story like this and you're willing to share it, let us know.