Specialty margin is defended with technical service. The documents that carry it have never been engineered.
The RFQ, the spec sheet, the SDS, the customer-specific price: every specialty deal runs on documents a human assembles by hand. AI is the first technology that reads them. We find where that moves your number, build it into the commercial process you already run, and prove it on live deals.
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, which has changed the buyer's problem rather than solved it. The question is no longer whether the technology exists. It is which workflow to engineer first, with which tool — bought or built — integrated into which systems, adopted by which team, and measured against which number. That is a judgement problem. It is the one we take.
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. The slowest document in the building becomes 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 stall. It is where ours start.
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.
Most engagements start with a four-week Revenue AI Audit inside your commercial operation: we interview the team, shadow the quote and order flows, and map where deals slow, leak, or get under-priced. You leave with a ranked list of use cases with modeled payback, a clear read on your data and integration posture for each, and a buy-versus-build recommendation we have no stake in either way.
Where we build, we build into the process you already run — your ERP, your email, your pricing rules — and stay embedded through the first live cycles until the metric has actually moved. We are independent of every vendor in this market. The recommendation is on the merits or it is worthless.
The partner who scopes the engagement runs it.
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.