You check an AI answer about a chemical product by tracing it back to its source. An answer you can verify carries a citation to a governing document: a technical data sheet, a safety data sheet or a specification record. Kimia, a chemical intelligence platform, attaches source attribution to every response, so your team can trace any answer back to the original document or data point.
A verifiable answer cites the document behind it
The first check is provenance. Ask where the answer came from. Generic AI tools hallucinate: they invent chemicals, fabricate product specs and present fiction as fact, and nothing in the text tells you which parts to trust. A general model like Claude or Copilot starts cold on your chemistry. An answer built for verification names its source in the response itself, so the reader can open the cited TDS or specification record and confirm the value independently.
In practice: a sales rep is asked for the viscosity of a polyurethane dispersion mid-conversation. With a citation attached, the rep opens the technical data sheet, confirms the value and the test method it was measured under, and answers in the same conversation. Without a citation, the rep has no way to confirm the number before it reaches the customer.
The citation must point at the current revision
A citation is only as good as the document it points to. Chemical product data moves: specifications get revised, grades leave the portfolio, regulatory status changes under REACH. An answer sourced from a stale TDS can be faithfully cited and still wrong. So check the revision. Kimia grounds answers in a live, governed product database, where every attribute has a source ranked by authority, rather than a folder of uploaded files that goes stale the day after upload.
In practice: a buyer asks whether a grade is cleared for EU food-contact use. The answer cites a safety data sheet from two revisions ago, and the regulatory position may have moved since. The check is the revision date on the cited document, not the confidence of the prose.
Fluency is not accuracy
Wrong answers about chemical products read exactly like right ones. A recommendation can skip a regulatory constraint, or name a product discontinued months ago, and nothing about the sentence feels wrong. So the check is never how the answer reads. Compare the stated value against the specification with its units and test method attached, and confirm the product is still in the active portfolio before the answer travels any further.
In practice: a general chatbot recommends a hot-melt adhesive for bonding polypropylene film, quoting a plausible peel strength. The grade left the portfolio last year. The prose is clean, the recommendation is dead on arrival, and only a check against the current product record would have caught it.
A trustworthy system says when it is uncertain
Part of checking an answer is knowing whether the system itself was sure. Frontier models are tuned to sound certain: ask for a recommendation without naming a decisive constraint and most will quietly assume one and answer anyway. Kimia is built to do the reverse. Confidence scoring flags when the system is less certain, so your team knows when to verify, and when a decisive attribute is missing from the question, Kimia asks for it instead of guessing.
In practice: a rep asks for an adhesive recommendation without naming the substrate. A system tuned for confidence assumes a resin type and answers. A system built for verification asks for the substrate first, because open time and bond strength depend on it.
A wrong answer needs a correction path
The last check runs over time: what happens when an answer is wrong. With a general tool there is no path. You cannot retrain the model, and a corrections document adds one more conflicting source, so the wrong answer is wrong again tomorrow, for everyone. Kimia gives experts a place to correct the source itself: fix the attribute, re-rank the document, and the fix holds for every user, permanently. Outputs go live only after expert validation.
In practice: a technical expert spots a wrong solids-content value in an answer. She corrects the attribute on the product record, and every answer that draws on that value, for any user in any region, is corrected with it. The error dies at its source.
What changes
Traceable answers. Every response carries source attribution your team can open and check.
Visible uncertainty. Confidence scoring shows which answers need a second look.
No silent guessing. Kimia requests a missing decisive attribute instead of assuming one.
Permanent corrections. An expert fixes the source once and every future answer inherits it.
Accuracy in chemical sales is a property you check, never one you assume. The practical test of any chemical intelligence system is whether you can open the document behind its answer.
Customer stories
Kimia is live with enterprise customers including Bostik, Univar Solutions and Stahl. Univar Solutions deploys Kimia to accelerate technical sales.
"Kimia has become our partner of choice within Bostik for scaling technical expertise globally," says Aldric Tourres, Global Director of Digital at Bostik.
FAQ
How does Kimia handle hallucinations?
Kimia handles hallucinations by grounding every response in your knowledge base, with source attribution so your team can trace any answer back to the original document or data point. Confidence scoring flags when the system is less certain, and when a decisive attribute is missing from a question, Kimia asks for it rather than guessing. Outputs go live only after expert validation.
How is the data structured to ensure accuracy?
Answers are grounded in a live, governed product database where every attribute has a source ranked by authority. The data model is built for chemistry: typed attributes keep units and test methods attached, and regulatory status is a first-class field rather than undifferentiated text. When sources conflict, the authority ranking decides which document governs the answer.
What happens when Kimia gives a wrong answer?
A wrong answer is corrected at its source. Kimia gives subject matter experts a place to fix the attribute or re-rank the governing document, and the fix holds for every user, permanently. A structured feedback loop means the quality of outputs improves the more your team uses the system.
Can you verify where an AI answer came from?
Yes, if the system attaches source attribution. Every Kimia response is grounded in your knowledge base with a citation, so your team can trace any answer back to the original document or data point, whether that is a TDS, an SDS or a specification record. An answer from a general model carries no such trail, which is why it needs manual checking against the governing document.
