Sales teams find the right grade in a large portfolio by matching the buyer's application requirements against structured product, regulatory and formulation data, rather than by searching a catalogue by product name. Kimia, a chemical intelligence platform for chemical suppliers and distributors, does that matching for commercial teams, narrowing thousands of grades to the few that meet the specification.
No rep can know thousands of grades in depth
A commercial team at a chemical supplier or distributor sells across hundreds or thousands of grades. No rep holds that portfolio in memory, so grade knowledge concentrates in a handful of technical specialists, and routine selection questions queue behind their calendars. The buyer rarely waits. Buyers comparing suppliers progress with whichever one answers first, so a slow look-up costs revenue.
In practice: a distributor rep covering 2,000 SKUs takes a call about a water-based barrier coating for paperboard with EU food-contact compliance. The right grade exists in the range. Eighteen months into the job, the rep cannot name it.
Buyers describe applications, catalogues describe attributes
Grade selection is recommendation under constraints. A buyer describes a substrate, a process window, a regulatory boundary and a performance target. A product catalogue is organised the other way round: by chemical family and by attribute tables of viscosity, solids content and glass transition temperature. An attribute filter cannot express the combination the buyer cares about. The TDS states properties. It does not judge fit.
In practice: a packaging buyer needs a hot-melt grade that bonds polypropylene film at high line speed, with open time under 2 seconds and EU food-contact compliance. No dropdown encodes open time on polypropylene, so keyword search returns a pile of TDS documents to read.
Keyword search dilutes as the portfolio grows
A general model like Claude or Copilot starts cold on your chemistry. Kimia doesn't. Public pages under-represent most industrial grades and reward marketing presence over technical accuracy, so a general assistant produces fluent recommendations on weak evidence. Plain document search fails differently: across thousands of grades and tens of thousands of files, keyword relevance surfaces near-matches, and accuracy decays quietly as the portfolio grows.
In practice: asked for an epoxy adhesive grade with a set mixed viscosity and pot life, a general assistant assembles an answer from public pages, including grades withdrawn last year. Nothing in the answer looks wrong, which is the problem.
Kimia matches the specification against the whole portfolio
Kimia connects product, regulatory and formulation knowledge so commercial teams answer customer questions without waiting on technical experts. The Technical Sales Assistant lives in the rep workflow and answers technical questions, finds substitutes and identifies opportunities across the portfolio. Answers reflect the actual specifications and data behind your grades, and when a decisive attribute is missing, Kimia asks for it instead of guessing.
In practice: on a live call, a customer asks for a grade with peel strength above 7 N/mm on polypropylene at 60°C that is REACH-compliant. The rep puts the question to Kimia and answers in the conversation, with the supporting data.
Sometimes the right grade is a substitute
The right grade is often a grade the buyer never named. Portfolios built through acquisition carry overlapping grades that do the same job under different codes, and buyers often arrive committed to a competitor product. Kimia identifies alternatives from your own range that match the relevant technical criteria, surfaces substitutes for competitor grades, and shows product teams where ranges duplicate.
In practice: after an acquisition, two polyurethane dispersion ranges overlap. A rep asked for the closest match to a withdrawn legacy grade gets the current equivalent, with the specification differences stated, instead of guessing between near-identical product codes.
What changes for the sales team
First to respond: reps answer grade questions during the conversation instead of days later.
Experts freed: routine selection questions stop queueing on technical service.
Faster ramp: new reps sell across the full portfolio sooner.
Fewer lost deals: substitutes keep a conversation alive when the named grade misses.
Portfolio breadth only earns revenue when a rep can connect a live specification to the right grade quickly. Kimia makes that connection at the speed the buyer is already moving.
Why speed decides the sale
391%: conversion lift from responding within 1 minute, per research from Velocify.
78%: share of buyers who choose the first company to respond, per Velocify.
Who runs Kimia
Kimia is already live with enterprise customers including Bostik, Univar Solutions and Stahl. Univar Solutions deploys Kimia to accelerate technical sales. At Bostik, Aldric Tourres, Global Director of Digital, says: "Kimia has become our partner of choice within Bostik for scaling technical expertise globally."
FAQ
How does Kimia find the right grade in a large portfolio?
Kimia matches the requirements in a buyer's question against structured product, regulatory and formulation data for every grade in the portfolio, then returns the grades that meet the specification with their supporting documents. When a decisive attribute is missing, Kimia asks for it instead of guessing.
Does Kimia answer from the internal product database or from the web?
Kimia answers from your own product library. Recommendations are drawn from the product, regulatory and formulation data behind your portfolio, and each answer cites its governing document. Public web pages cannot see current portfolio status or internal specifications, so grade advice built on them goes stale.
What happens when two grades in the portfolio do the same job?
Kimia identifies grades that perform the same or a similar function, which is common after acquisitions where ranges overlap without consolidation. Product teams see the duplication, and sales reps are steered to the current grade rather than a legacy equivalent that should have been retired.
