The pilots have run in specialty chemicals. The quote still takes five days.
Every specialty deal runs on documents — the RFQ, the spec sheet, the SDS, the customer-specific price — and that is exactly where commercial AI stalls in this industry: a model reads them fast and misreads them invisibly. We diagnose where an AI investment actually stands, build the verification it shipped without, and prove the return on live deals. Where nothing is built yet, we build it so it never needs rescuing.
The chemical industry's AI conversation has moved from the laboratory to the plant — agents on shift checklists, anomaly flags, work orders. The commercial function is next, and it is the part of the business where specialty players actually defend their margin: formulation advice, application knowledge, documentation, the technical quote that proves expertise before the first delivery.
That work runs almost entirely on unstructured documents. A commercial team handling a hundred RFQs a month matches SKUs, pulls safety data sheets, applies pricing logic, and formats quotes by hand — hours of senior sales-engineering time per quote, in a market where the fastest credible answer often wins the order.
A dozen vendors now sell quote automation into this industry, and many commercial teams have already bought one. What most of those purchases shipped without is the layer that makes the output safe to act on: verification against the ERP and the regulatory record, calibration on live deals, a handover the team accepts. Whether the right move is to fix what you bought, replace it, or build — that is a judgement question, and answering it is the first week of our work.
Four places AI pays back in a chemical commercial operation.
RFQ to technical quote
An inbound RFQ parsed, product codes matched, customer-specific pricing rules applied, and a complete technical quote drafted — safety data and compliance references included — for the account manager to review rather than assemble, turning the slowest document in the building into one of the fastest.
Pricing and margin intelligence
Your own deal history, read properly: which prices held, where discounting is habit rather than strategy, which accounts pay for technical service and which only consume it. The highest-margin change available, and it requires no new tooling on the customer side.
Compliance documentation in the commercial flow
SDS sheets, REACH references, certificates of analysis — assembled into the quote and the order confirmation automatically, not hunted across shared drives per deal. Regulated-document fluency is where generalist AI deployments typically stall — this one is built to handle it.
Account and channel signals
The unstructured record of an account — order patterns, service emails, complaint tickets — read continuously for the signals that say an account is drifting to a competitor or ready to expand, while there is still time to act on either.
A €120M specialty chemicals manufacturer in the Basel area, eight-person commercial team, ~120 RFQs per month, quotes produced entirely by hand. We re-framed how the company sold, then engineered the quote process around an AI agent integrated into email and ERP — drafting complete technical quotes with safety data for account-manager review.
Every engagement runs on the same arc — diagnose, harness, calibrate, demonstrate. We start with one unit of work: a real RFQ, traced end to end, asking where correctness gets decided and what happens when the AI is wrong. Within days, that tells you whether the investment is recoverable and where the value actually sits — usually in the quote flow, the pricing logic, and the compliance documentation a human assembles by hand.
Where we build, it ships inside a harness: the AI reads the RFQ and drafts the quote, but the totals, the pricing rules, and the safety-data references are checked against sources the model cannot invent — your ERP, your catalog, your regulatory references. We build into the process you already run, stay through the first live cycles until the metric has actually moved, and are independent of every vendor in this market — including the one you may already have bought.
The partner who scopes the engagement runs it.
What commercial leaders in chemicals usually ask first.
Why does commercial AI stall in specialty chemicals when the plant-floor pilots worked?
Because the commercial side runs almost entirely on unstructured documents — the RFQ, the spec sheet, the SDS, the customer-specific price — and that's exactly where a model misreads quietly: an SKU, a column, a compliance reference. A dozen vendors sell quote automation into this industry, and many teams have already bought one; what most of those purchases shipped without is verification against the ERP and the regulatory record.
We already bought a quote-automation tool. Should we replace it?
Not automatically. Whether the right move is to fix what you bought, replace it, or build is a judgement question, and answering it honestly is the first week of our work — we're independent of every vendor in this market, including the one you may have already bought.
What return has this actually produced in chemicals?
For a €120M specialty chemicals manufacturer near Basel, an eight-person commercial team handling roughly 120 RFQs a month by hand: quote turnaround went from five days to eleven hours, RFQ conversion rose 26%, and 1.4 FTE of senior commercial capacity was freed.
How do you keep an AI-drafted quote from including a wrong price or a stale safety data sheet?
The AI reads the RFQ and drafts the quote, but the totals, the pricing rules, and the safety-data references are checked against sources the model cannot invent — your ERP, your catalog, your regulatory references. That's the harness: it ships with the quote, not as a separate review step someone has to remember to run.
Bring us a quote that took too long, or a price that didn't hold.
Twenty-five minutes. We'll tell you whether the pattern we've deployed in this industry fits your operation — and if it doesn't, we'll say so plainly.
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