Silk Quote Agent

The routine quotes answered in three minutes. Your reps on the rest.

An AI agent that reads inbound RFQs — email, PDF, spreadsheets — checks them against your catalog, and sends back a priced quote with a checkout link. The ones it shouldn't answer go to your rep with the whole thread attached.

Book a 30-minute demo See how it decides

Six weeks to production · Built on the catalog you already maintain

Lands in the quotes inbox · email · PDF · spreadsheet · web form
"need 40 of the 1/2 3000# thrd ball, plus whatever's on the attached sheet"
The agent evaluates it — in seconds
Ask is clear SKUs correlate Price assignable Stock known
Answered
Priced quote, checkout link
Back in about three minutes, from your own inbox.
Clarified
A question, not a guess
Which of these two did you mean? Asked, then priced.
Escalated
To a rep, already prepped
Parsed request, original message, recommended action.

Two of those three close without a rep opening the message. The third reaches one with the work already done.

The cost of a slow quote

Thirty minutes to four hours. Sometimes the next day.

That's where most distributors we talk to land on a routine quote. Not because anyone is slow — a person has to open the email, work out which SKU "1/2 3000# thrd ball" means, check whether the price is still current, and type the reply.

Twelve minutes of someone's attention, and only during business hours. In most quote inboxes, the bulk of those requests needed no judgment at all — a clear ask for stocked items the catalog can already price.

Meanwhile the competitor who answered first is taking the order.

How it works

Every request evaluated in seconds. The routine ones answered.

Email, PDF, spreadsheet or web form — it reads whatever arrives, from a named account or a buyer you have never heard from. Before anyone is involved, it establishes four things.

The ask is clear
Quantities, units, and intent are unambiguous.
SKUs correlate
Partial part numbers and plain descriptions resolve to catalog products.
Pricing can be assigned
Contract pricing for a named account, catalog pricing for a first-time buyer.
Availability is known
Stock position is current, and can be stated on every line.
Then one of three things happens
All four clear
A priced quote, sent from your inbox
Every line priced against the catalog, with a pre-populated checkout link — back in about three minutes, in your team's voice.
Close, but ambiguous
A clarifying reply, not a guess
It asks the customer to confirm between close matches — the same thing a good rep would do before pricing it.
Anything else
To a rep, already prepped
Non-catalog items, ambiguous quantities, and anything it can't price or confirm stock on arrive with the parsed request, the original message and a recommended action attached.

Thresholds are configurable and start conservative. Every decision is logged — what it read, what it concluded, and what it did.

Real requests, not clean ones

Customers don't send clean POs.

They send partial part numbers, descriptions instead of SKUs, and spreadsheets where a third of the rows are yours. Rules-based automation breaks there. This doesn't — it matches on description, asks the customer to confirm between close matches, and hands off to a person when it shouldn't guess.

"1/2 3000# thrd ball — qty 40"
Answered Matched to a catalog SKU on the description, priced, and added to the quote.
"the blue handled one, same as last time"
Clarified Two close matches in the catalog. Replied asking the customer which one.
"XJ-9920, see attached spec sheet"
Escalated Not in the catalog. Routed to a rep with the spec sheet and the full thread.
What distributors ask first

Four questions, up front.

Do we need a new database?
No. It reads the ecommerce catalog you already maintain for products, pricing, and customer records. No second dataset, no nightly extract, nothing to keep in sync.
How does it decide what to answer?
Four checks on every request: the ask is clear, every line correlates to a SKU, pricing can be assigned, and availability can be stated. Named account or first-time buyer, all four clear and it answers. Any one doesn't and a rep picks it up with the work already done.
What if it gets one wrong?
Thresholds are configurable and start conservative, so early on a person reviews most responses. Every decision is logged: what it read, what it concluded, what it did.
Are we locked into one AI vendor?
No. The harness is model-agnostic and runs on the cloud. The reasoning model swaps underneath without rebuilding the system.

Silk Commerce has built B2B commerce for distributors and manufacturers for over a decade. Premier Epicor Agency Partner.

Storefronts
BigCommerce · Shopify · Adobe Commerce · Epicor ECC
ERP
Prophet 21 · Epicor Kinetic · NetSuite · Sage X3
The agent runs on
AWS Bedrock and AgentCore · read-only against your catalog

Bring us your ugliest RFQ

Thirty minutes. We'll run your own request formats through it live — the messy spreadsheet, the partial part number, the one nobody wants to quote.

Book a 30-minute demo

We'll reply within one business day.

No slides. Your inbox formats, your catalog, live.