An AI assistant recommends competitor products only when its answers can come from the open web. Kimia's Customer Concierge cannot: it answers from your governed product record, cites the governing document behind every answer, and asks for missing information instead of guessing. A product outside your portfolio is outside its answer space.
The answer source decides what gets recommended
A general model carries the public internet in its weights: rival grades, rival datasheets, marketing claims, and whatever it misremembers about all three. Ask it for a recommendation and it draws on everything at once. Kimia scopes Customer Concierge differently. Answers are grounded in a live, governed product database, where every attribute has a source ranked by authority, and every response cites the document it came from. The recommendation space is your portfolio. Nothing else is retrievable.
In practice: a buyer asks a hot-melt adhesive supplier's website for an alternative to a rival product that delaminates on polypropylene above 200 m/min. Customer Concierge reads the requirement, open time and adhesion on polypropylene, and recommends grades from the supplier's own portfolio, citing each technical data sheet.
A retrieval boundary holds where an instruction does not
The obvious control is a system prompt: tell the chatbot not to mention competitors. That instruction sits on top of a model that still holds open-web knowledge of every rival product, and a persistent visitor can talk around it. Kimia enforces scope at retrieval instead. Answers come only from retrieved data, retrieval reaches only your governed product record, and the same question under the same context returns the same answer every time. The assistant cannot recommend what it cannot retrieve.
In practice: a visitor spends twenty minutes probing a coatings supplier's assistant for a specification of a competitor's polyurethane dispersion range. Customer Concierge holds no data on that range, so no phrasing produces one. The conversation stays on the supplier's own dispersions and what their records support.
A competitor name in the question is a signal worth capturing
Buyers name competitor products constantly, because replacing one is usually why they are searching. An assistant should not pretend the product does not exist. Customer Concierge treats the mention as input: the named product is a statement of the application and the performance the buyer expects, and that requirement gets mapped onto your portfolio. The mention itself is logged as market intelligence, so commercial teams see which rival products keep appearing as replacement targets.
In practice: a formulator asks for a drop-in replacement for a competitor's REACH-compliant adhesion promoter. Customer Concierge captures the replacement target, matches the stated substrate and cure conditions against the supplier's portfolio, and hands sales a lead that names the product being displaced.
Missing data produces a question, not a guess
The dangerous moment for any assistant is a gap in the record. General models are tuned to sound certain, so they quietly assume the missing constraint and answer anyway. Customer Concierge is built to the opposite standard. When a decisive attribute is missing, the system asks for it instead of guessing, and confidence scoring flags when the system is less certain, so your team knows when to verify. An honest question beats a fluent wrong answer.
In practice: a buyer asks whether a grade suits food-contact packaging in the EU. The product record holds no food-contact status for that grade. Customer Concierge states that the record does not confirm it and asks which regulation the buyer needs to meet, rather than inventing a compliance position.
Your experts set the standard and hold the controls
Control does not end at go-live. Nothing goes live until it is accurate: your own technical experts validate outputs before anyone uses them, so recommendations reflect your portfolio strategy rather than a model's habits. When an answer is wrong, Kimia gives experts a place to correct the source: fix the attribute, re-rank the document, and the fix holds for every user, permanently. Source attribution keeps every answer traceable to the document behind it. You stay in control of the standard.
In practice: during validation, an application chemist finds the assistant recommending an epoxy grade the supplier discontinued last year, sourced from a stale application guide. She re-ranks the guide and corrects the product record. The recommendation disappears for every user, permanently.
What changes
A bounded answer space: recommendations come only from your governed product record
Scope that holds: no phrasing pulls open-web competitor knowledge into an answer
Captured signal: competitor mentions become market intelligence for commercial teams
A visible trail: every recommendation cites the governing document
An assistant recommends what it can retrieve. Scope retrieval to your governed product record and the only products it can put in front of a buyer are your own.
FAQ
Will Kimia's Customer Concierge recommend competitor products to your website visitors?
No. Customer Concierge answers from your governed product record, and a product outside your portfolio is not in its answer space, so there is nothing to ground a competitor recommendation in. When a visitor names a rival product, the assistant maps the stated requirement onto your portfolio and recommends your grades, citing the governing document for each.
How does Kimia handle hallucinations?
Kimia handles hallucinations by grounding. Every response draws on your knowledge base with source attribution, so any answer traces back to the original document or data point. Confidence scoring flags when the system is less certain, and when a decisive attribute is missing, the system asks for it instead of guessing. A structured feedback loop lets your subject matter experts keep improving output quality.
What happens when a buyer names a competitor product in a conversation?
The mention is treated as an application requirement and captured as market intelligence. Customer Concierge reads the named product as a statement of what the buyer needs to replace, matches that need against your portfolio, and logs the replacement target so commercial teams can see which rival products keep appearing in buyer conversations.
What does Customer Concierge do when your product data has a gap?
It asks instead of guessing. When a decisive attribute is missing from the product record, Customer Concierge requests the missing information, and confidence scoring flags answers the system is less certain about, so your team knows when to verify. A gap produces a question or a flagged answer, never an invented specification.
Can your team correct a wrong answer?
Yes. Kimia gives experts a place to correct the source: fix the attribute or re-rank the document, and the fix holds for every user, permanently. Before launch, your own technical experts validate outputs, and nothing goes live until it is accurate. You stay in control of the standard.
