Why the Landing Page Fails
A landing page is built to persuade a human who is already looking at it. An agent arrives with a different job: it is shortlisting three options against a specific requirement, and it needs facts it can compare.
Hero copy, testimonials and gradients carry no extractable facts. A page that converts beautifully can be nearly empty to a model, which is why sites with excellent marketing sometimes never appear in AI comparisons while a plainer competitor does.
What an Agent Is Actually Looking For
What it is, in one sentence
A plain statement of the job the product does. Not a category, not a slogan. If the model has to infer this from three paragraphs, it will get it wrong sometimes.
What it costs, including the free tier
Price is the most requested field and the most commonly hidden one. A missing price is frequently read as expensive or as unknown, and both lose the comparison.
Hard limits
Rate limits, file sizes, seat counts, supported formats. These decide fit faster than any feature list.
What it does not do
The single most undervalued field. A model that knows your boundary recommends you for the right job instead of the wrong one, which raises the quality of every recommendation you get.
What Belongs in the Feed
A feed is not a marketing document with tags on it. It is the set of facts a buyer would need to rule you in or out, published in a form that does not require reading prose.
| Field | Why an agent wants it |
|---|---|
| Name and one line description | Identifies the product without inference |
| Pricing tiers including free | The most requested and most often missing field |
| Hard limits | Decides fit faster than features do |
| Supported inputs and outputs | Answers most compatibility questions outright |
| Integrations | Places you inside somebody else's stack question |
| Explicit non capabilities | Prevents the wrong recommendation, which protects your reputation |
Publish the Limits, Not Just the Features
This is the part most teams resist. Stating what your product cannot do feels like handing the competition an argument. In an AI comparison it does the opposite.
Why the boundary helps you
A recommendation that turns out to be wrong costs the model trust and costs you a bad first experience. A model that knows exactly where your product stops will put you forward confidently everywhere inside that boundary, and leave you out of the queries you would have lost anyway.
Formats That Matter in 2026
Structured data on the page itself remains the baseline, because it travels with the content and needs no separate discovery. A dedicated feed at a stable URL is the addition, and it matters most when your catalogue is larger than a handful of items or changes often.
Do not treat schema markup as a ranking lever. It removes ambiguity rather than buying position. A page marked up perfectly that still says nothing quotable gains very little, which is the usual disappointment after a schema project.
Where to Put It
At a stable, predictable URL that you do not move, linked from the places an agent already looks. A feed that changes address is worse than no feed, because the entire value is that something can rely on it. Version the contents rather than the location.
A Test You Can Run Today
Ask an assistant to compare you against two competitors
Use a real buying question rather than your brand name. Brand name queries flatter you and tell you nothing.
Read what it got wrong about your product
Wrong pricing, missing limits, invented features. Every error is a fact you failed to publish in a form it could use.
Publish those exact facts as structured data
Fix the specific errors rather than rewriting the page. The gap between what it said and what is true is your work list, already prioritised.