AI reads the RFQ. Your team prices it.
Reps spend hours retyping RFQs from emails, PDFs and spreadsheets into the ERP. AI can do the reading and the first draft. A person still checks the match and sets the price.
Sound familiar?
- RFQs arrive as PDFs, scans, spreadsheets and email text, and someone retypes them line by line.
- Customer part numbers have to be matched to yours by memory or a lookup sheet.
- Quote turnaround depends on how busy the inside sales team is that day.
- Simple repeat RFQs take as long as complex ones.
Built inside the tools you already use.
RFQ intake
Requests are picked up from a shared inbox or upload, and the line items, quantities and dates are pulled out.
Part matching
Customer part numbers and descriptions are matched to your catalog, with low-confidence matches flagged for a person.
A draft quote
A draft is created in your ERP or quoting tool with the matched items, ready for pricing.
A person approves
Nothing is priced or sent without a person. Every AI step is logged so you can see what it read and why.
How the build runs.
- 01
Sample your RFQs
We collect a few weeks of real RFQs and measure how they arrive and how long they take today.
- 02
Build and test
We build the intake and matching, then test it against those real RFQs before anyone relies on it.
- 03
Go live with review
It runs alongside your team, with a person checking every draft, and we tune it on the misses.
Work our team has delivered.
Paper invoice approvals replaced with AI checks and a human safety net
Read the case → Healthcare billing · USClaims checked before they are sent, not after they are denied
Read the case → Enterprise · Fortune 500Governed AI answers over company data, rolled out to an enterprise
Read the case →Common questions.
Can the AI set prices?
We don't build that. AI reads, matches and drafts. Pricing, discounts and anything that goes to a customer are approved by a person.
What if the AI reads an RFQ wrong?
Low-confidence lines are flagged, not guessed. Every draft is reviewed before it becomes a quote, and the corrections are used to improve the matching.
Does our data leave our systems?
We agree this before building. Options range from a mainstream AI provider under a no-training agreement to models deployed in your own cloud.
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Book a free review