In AI search, building product manufacturers get understood only when specs are written for a careful reader: which product family and configuration, which value, tested to which standard, approved by whom, and when. Assistants do well with specs stated clearly in text, and badly with option-dependent values, lapsed approvals and catalog-only series names.
A rep can look at a project and know which series fits, which glass package hits the number, and which approval covers the county. A catalog, a configurator and a phone call fill the gaps for a person.
An AI assistant gets none of that. It gets what's published. If the published version only works with a rep in the room, the assistant either says it doesn't know or fills the gap itself.
Four places specs break
The value depends on the configuration. A window series doesn't have one U-factor or one design pressure. The rating depends on size, glazing and frame options. A page that shows the best value for the series, without saying which configuration earned it, invites someone to quote it for the wrong one.
The value has no test standard. "Excellent air infiltration" means nothing to an assistant comparing three products. An air-leakage value at a stated test pressure, tested to ASTM E283, is something it can compare. A number without its method is close to no number.
The approval has a date. ICC-ES evaluation reports, UL listings, Florida Product Approvals and Miami-Dade NOAs each have an issuer, an ID, a scope and often an expiry. An approval quoted after it lapsed is worse than none, because now the answer is wrong about code.
The name only makes sense inside your company. "Series 4500" means something to your reps. An assistant also needs the category in a buyer's words, such as thermally broken aluminum window or fluid-applied air barrier, and for commercial products the MasterFormat section a specifier would file it under.
What understandable looks like
For each product family, publish:
the category in plain words, and the MasterFormat section where it applies;
what it's for, and what it isn't for;
the materials;
the key performance values, each with its test standard and the configuration it applies to;
current listings and approvals, each with issuer, ID and date, linked to the issuer's own directory;
the documents: spec sheet, submittal, installation, CAD and BIM, each with a date or version;
whether it's current, discontinued, or replaced, and by what.
None of it is new information
It's the information your best rep already uses, written down once, kept current, and published the same way on your site, in your structured data, and in what your distributors receive.
How to tell whether AI gets it
Ask the assistants the questions your buyers ask:
Which [category] products from [your company] meet [value] tested to [standard]?
Does [product] have a current [approval] for [state]?
What's the difference between [series A] and [series B]?
Who supplies [product] near [city]?
Trace each wrong answer to its source
Compare each answer with your own record. Where an answer is wrong, trace it back to where it came from: an old PDF, a distributor page, a directory, or a gap on your own site. Fix it there, then ask again.
Check My Business runs this comparison for your company, with a person at Lehvel reviewing it. Lehvel Observatory keeps asking after the first fix, so you see whether the answers change.
Questions
Do we need a PIM to do this?
Not to start. Many companies keep this in spreadsheets, a PIM, the ERP and the website at once. The first job is deciding which source is right for each fact.
Should we publish every configuration?
Publish what a buyer needs to qualify the product, and route the rest to a person. "Ask us for the rating at your size" is a better answer than a wrong number.
What about values we don't want competitors to see?
Mark them internal or on request. An assistant should get "ask the company" for those, not a guess.
