How to structure a solutions page for AI answers
Guide · SEO / AEO / GEO · 5 min read · last verified 2026-07-27
A solutions page is the page that maps a buyer's problem to your product — the door a buyer walks through when they know what hurts but not yet what fixes it. That makes it structurally different from a product page, which describes what you built, and from a homepage, which establishes who you are. And it explains why most solutions pages earn nothing from AI assistants: they are written as internal taxonomy — Solutions for Enterprise, Solutions by Industry — which describes how the vendor segments its market, not how any buyer describes a problem. An assistant retrieving sources for a problem-shaped question matches question language against page language. A page organized around your org chart matches nothing a buyer actually asks. The fix is a page built from five blocks, each doing one job in the buyer's own words.
Why solutions pages fail
Walk through a typical one: a headline naming a segment, three paragraphs of aspirational copy about transformation and scale, a grid of feature icons, a demo button. Now consider the question it theoretically exists to answer — something like how do teams like ours stop losing deals to a problem like this. The page never states the problem. It never says who specifically it serves or how the product concretely intervenes. There is nothing extractable: no sentence an assistant could lift that answers anything. The page was written to reassure a prospect already in a sales conversation, not to be found by one who is still describing symptoms to an assistant at midnight. Both audiences matter; only one was served.
The question your page has to match
Before writing a word, name the question the page answers — in the vocabulary a buyer uses before they know your category exists. Buyers with the problem say things like our reps keep quoting outdated prices, or we find out about competitor launches from customers. They do not say revenue enablement solutions. Your support tickets, sales-call notes, and the questions buyers put to assistants are the source vocabulary; the discipline of mining them is covered in How to build a FAQ from real buyer questions, and buyer-question research of exactly this kind is the raw material Magrios is built to surface. One page per problem. If two problems share a page, neither gets answered cleanly and the page matches neither question.
Block one: the problem, in the buyer's words
Open by stating the problem plainly, in the language the buyer would recognize as their own — symptoms, consequences, the workaround they are currently suffering through. This is the block that makes the page retrievable, because it is the block that shares vocabulary with the question. Two honest paragraphs beat ten aspirational ones. A buyer who reads the opening and thinks that is exactly our situation will keep reading; an assistant matching a problem-shaped query has, in that opening, something to match against.
Block two: who this is for — and who it is not
Name the team, the situation, the stage. Specificity here is a courtesy to both readers and machines: for product marketing teams at companies selling into competitive categories says something checkable; for forward-thinking organizations says nothing. Stating who the page is not for costs a little top-of-funnel vanity and buys credibility — with buyers, who trust pages that draw boundaries, and in AI answers, where a scoped claim is likelier to be repeated accurately than a universal one.
Block three: how it works, concretely
Describe the mechanism — what the product actually does about the problem, step by step, in plain declarative sentences. Not the full feature tour; that belongs on the product page, structured as described in How to optimize a product page for AI. Here you need just enough mechanism that a reader can form a causal picture: it watches these sources, it flags this change, your team sees it here. Vague pages get summarized vaguely, when they get summarized at all. Concrete mechanism is what an assistant can restate without inventing.
Block four: proof
One or two pieces of evidence that the mechanism works, linked rather than inlined — a case study built to be readable on its own, per How to optimize a case study for AI. Resist the wall of logos: logos are images, carry no extractable claims, and say nothing about this problem. A single named customer with a described outcome outweighs a carousel.
Block five: one next step
End with exactly one action, matched to the visitor's likely stage. A buyer on a problem page is usually early; a low-commitment step — see how it works, read the relevant guide — fits better than a demo form with eleven fields. One step, because a page that ends in five buttons ends nowhere.
How the family fits together
The page-type family divides labor. The homepage establishes identity — How to optimize a homepage for AI. The product page owns capability questions. Solutions pages own problem questions. Integration pages own does-it-work-with questions, handled in How to build integration pages that answer buyer questions. When each page owns one question shape, internal links between them read as structure rather than plumbing, and an assistant landing on any one page can follow the seams to the rest.
Mistakes that keep solutions pages invisible
Naming pages after segments instead of problems. Writing one mega-page for every use case, so no single question is answered cleanly. Opening with the product instead of the problem, which breaks the vocabulary match. Publishing proof-free pages, which read as assertion. Burying the page three clicks deep with no internal links pointing at it. And letting the page rot: problems drift, vocabulary drifts, and a solutions page written three years ago in last cycle's jargon quietly stops matching the questions buyers ask now. Revisit the buyer's words at least yearly; the page should always sound like this year's version of the problem.