What was breaking before?
Quote turnaround was measured in days, and deals were being lost to slow response. The mechanics of a manual quote explain why: a request arrives, a person has to identify the right suppliers, contact each one for current pricing, wait for replies, retype the numbers into a template, and check the maths before anything goes back to the customer.
Every one of those steps waits on a human being available, and every wait compounds. A request that lands late on Friday sits until Monday. A supplier who answers slowly stalls the whole quote. Meanwhile the customer is asking competitors the same question, and the faster answer often wins regardless of price. The distributor was not losing on product or pricing. The distributor was losing on the clock.
What did we build?
We built an agentic AI workflow that pulls supplier pricing and prepares the draft quote, with a human buyer approving before anything is sent. Agentic means the system does not just follow one fixed script: given a quote request, the agent works out which supplier sources to check, gathers the current pricing, and assembles a draft in the distributor's own format.
The trigger is the quote request itself arriving. From there the agent reads the line items, matches them against supplier pricing sources, and drafts the quote with every figure traceable back to where the figure came from. The draft then stops at an approval gate. Nothing reaches a customer without a buyer signing off. The system prepares; the human decides. That split is deliberate and runs through everything we build.
Client identity and specific commercial metrics stay under NDA. What we publish is the shape of the system and the outcomes the client has approved. On a call we walk through live systems, not slides.
How does the system decide what to do?
Decisions follow rules the distributor set, not guesses the AI makes on its own. The agent's job is bounded: read the request, find the matching supplier pricing, and prepare a draft. Where a line item matches cleanly, the agent fills the line item in. Where something is ambiguous, a product that could match several codes or pricing that looks inconsistent, the agent flags the line instead of guessing.
Escalation is built in rather than bolted on. Anything the agent cannot resolve confidently lands in front of the buyer with the context attached, so the human handles exceptions rather than the whole quote. The final gate never moves: every draft waits for buyer approval before leaving the building. Automation handles the repeatable middle, and judgement stays with the person accountable for the number on the page.
What changed for the team?
Quote turnaround dropped to under an hour, against the days the same work took before, and the buyer's job shrank to reviewing and approving. That is the whole published result, and the shape of the change matters more than any single number. The slow part of quoting was never the decision. The slow part was the gathering, the chasing and the retyping.
With the agent doing that middle work, a buyer opens a prepared draft instead of a blank template. Checking a quote takes minutes of attention rather than a day of coordination, so responses go back while the customer is still deciding, not after the customer has moved on. The buyer still owns every quote that goes out. The difference is what the buyer no longer has to do to get there.
How would this look in your business?
Any business that assembles quotes from supplier or cost data has the same shape of problem: a request comes in, someone gathers numbers from several places, and a customer waits while that happens. The supplier and quote agent pattern applies wherever the gathering is the bottleneck: distribution, wholesale, manufacturing, construction, services with subcontracted costs.
The parts adapt to what already exists. The trigger can be an email inbox, a WhatsApp message, a form or an ERP entry. The pricing sources can be supplier lists, portals or spreadsheets. The approval gate sits wherever accountability sits today. We start by mapping how one quote currently travels from request to send, then build the agent around that path. Tell us how quoting works now, and we will give an honest read on what an agent could take off the team's plate.
Related capabilities. The same parts, your business.
Keep reading. Pages close to this one.
Tell us what runs slow. We build what fixes it.
Send one message describing where quotes stall in your business. We reply with an honest read on what an agent could fix, what it would take, and whether it is worth building at all.
