If you run marketing at an early-stage SaaS company, you already have some content and you already know AI answers matter, but the two things probably do not connect yet. This guide is for founders and small marketing teams who want to build MOFU SaaS content, the pages that meet a buyer while they are actively evaluating a problem, not just discovering it. By the end you will have a way to find the right questions to answer, pick the right page type for each one, and structure every page so an AI engine can actually use it as a source. The goal is content for in-market SaaS buyers, people who already recognize the problem and are working out how to solve it.
What counts as middle-funnel content, and what does not
Awareness content introduces a problem or a category to someone who has not thought about it much yet. Bottom-funnel content compares named vendors and pushes toward a decision. Middle-funnel, or consideration-stage, content sits between the two. The reader already knows they have a problem. They are defining requirements, weighing approaches, and trying to build a case they can take to a boss or a cofounder.
A topic belongs in the middle of the funnel when the reader could use the page to brief a colleague, check whether their team is ready, or plan next steps, and when the page stays useful even if they never buy anything from you. It does not belong there if it only defines a term, or if it names a competitor and asks for a ranking. That second kind of question is a comparison or alternatives request, and it is a different job for a different page. This guide sticks to problem-solving and evaluation content on purpose, because trying to cover comparison pages here would blur two audiences that need different structures.
Three page types cover most of this ground. A how-to guide walks the reader through completing a real consideration-stage task. An evaluation framework hands them criteria and questions so they can judge an approach or a solution on their own. A category explainer, aimed at someone who already has a project in mind, explains what a category actually does in a buyer's workflow rather than just defining it. Knowing which of the three you are writing before you start is most of what keeps a page focused.
Buyers lean on this kind of content more than most founders assume. In the 2025 6sense B2B Buyer Experience Report, 94% of buyers said they used an LLM somewhere in the buying process, and that use peaks in the middle of the journey, when people are comparing offerings and pulling information together rather than just discovering who exists. By the time sellers get involved, the shortlist is usually already set: the report found the eventual winner was on the buyer's Day One shortlist 95% of the time, and 94% of buying groups had ranked that shortlist before talking to a single seller. Middle funnel content AI search engines can retrieve is one of the few ways to influence a buyer during that early, self-directed stretch.
Map the consideration-stage prompts your buyers are asking
What to do. Start with real questions, not keywords. Pull them from sales calls, demo questions, support tickets, onboarding friction, and conversations with whoever on your team talks to customers the most. Add anything useful from Search Console queries, existing AI answers about your space, and questions you see competitors getting cited for. Group what you collect by the task behind it, not by how similar the wording looks. Useful clusters include how to solve the underlying problem, how to figure out what capabilities matter, how to compare approaches without comparing named vendors, how to measure results, and how to build the internal case for doing anything at all.
How to tell it is done. Every question on your list should have a clear buyer stage, a role attached to it (who on the buying team is likely asking), a task or decision behind the wording, a likely page type, and a reason your company is a credible source for the answer.
Common mistake. A high-volume keyword can pull in students, casual readers, and existing customers along with active buyers, so keyword volume alone tells you nothing about buyer stage. Write down the buyer's actual task next to each question so you do not confuse traffic potential with consideration intent.
Find the gaps between those prompts and your current pages
What to do. For each prompt cluster, check for four kinds of gap. A no-page gap means nothing on your site answers the question at all. A coverage gap means a page exists but only handles part of it. A citation gap means competitors get cited for the prompt while you are invisible. A trust gap means your page exists but lacks the evidence or specificity to earn a citation. Log the exact page, the prompt, any competing source, what is missing, and the action you would take, so the backlog is something you can act on rather than a pile of ideas.
How to tell it is done. Every row in your backlog names the prompt cluster, the buyer stage, the existing page (or "no page"), what is missing, the recommended content type, and a rough priority with a reason attached.
Common mistake. Seeing a lot of competitor content on a topic is not proof you need a new page. Confirm the topic reflects a real task your buyers have and that you can add something they cannot already get elsewhere before you brief a new article.
DeepSmith's Content Map is built for exactly this step. It crawls your site and your competitors' sites onto one shared taxonomy of topics and funnel stages, so you can see coverage gaps where you publish less than a competitor, and untapped topics where you have nothing at all. The AEO side of the platform tracks which of your prompts get you mentioned but not cited, and which pages competitors are winning citations on for those same prompts, which turns "we should probably write more" into a specific, evidence-backed list. That's the practical shape of saas consideration content AEO work: turning a hunch into a backlog line with a reason attached.
Match each prompt cluster to the right MOFU content type
Good MOFU SaaS content starts with a single job, not three.
What to do. Pick one primary job for every page before you brief it. Use a how-to guide when the reader needs to complete a process. Use an evaluation framework when they need criteria or a way to score options. Use a category explainer when they understand the problem but need to understand the category's practical role, its requirements, and the questions to ask before adopting it. Write a one-page brief for each piece: the buyer and role it is for, the primary question, what the page explicitly will not cover, the H2 sequence, what evidence you can bring to it, which product facts are fair to mention, and what the reader should do when they finish.
How to tell it is done. A writer, including a future version of you, should be able to draft the page from the brief without guessing who it is for, what it should leave out, or which of your own pages it should link to.
Common mistake. Do not fold "how to solve the problem," "best tools," "alternatives," and "pricing" into a single article. Those are different intents, usually at different buyer stages, and squeezing them together weakens all of them.
This is where DeepSmith's Opportunity Agents can save real time. They read your AI visibility data or your Content Map and hand back content ideas with the specific gap attached to each one, which turns prioritization into a review task instead of a brainstorm. Reject anything that is really awareness-stage, duplicative, or outside what your team can credibly speak to. The agent gets you a backlog faster; the editorial call is still yours.
Build each page around a direct answer and a usable framework
What to do. Say the answer in the opening paragraph, then define who the page is for and what it does not cover. Write descriptive H2 headings that name the actual question, and put the direct answer in the first sentence or two under each one, followed by the reasoning, steps, and caveats. Use numbered steps for processes, tables where they genuinely clarify criteria or trade-offs, and bullets for requirements and failure modes. A how-to section works well as: what to do, how to do it, what "done" looks like, what commonly goes wrong, and one concrete example. An evaluation-framework section works as a criterion, why it matters, a question to ask about it, and the minimum acceptable answer.
How to tell it is done. Read only your H2s and the first sentence beneath each one. If that skim alone reveals the buyer's task, the answer to each sub-question, the sequence of work, and the boundary between this page and the next one, the structure is doing its job.
Common mistake. Do not bury the answer under a long introduction or a story about your company. In-market buyers want the useful answer first and the reasoning after it. The practical test for middle funnel content AI search engines can actually use is whether it states the answer before it explains anything.
This is also where Content Studio earns a mention, since it is the step where structure actually gets built. It turns a planned idea into a researched, brand-grounded draft with the SEO and AEO formatting already in place: descriptive headings, an answer-first structure, and the internal linking to tie it to the rest of your site. Deep IQ, the layer underneath it, stores your positioning, your product details, and your voice, so the draft that comes out already reflects your product accurately instead of needing a full rewrite to fix what a generic AI tool got wrong.
Pro tip: write the one-sentence answer before you write the introduction. If your team cannot state the answer in one clear sentence, the page is probably trying to cover more than one intent, or the brief is not specific enough yet.
Add evidence that makes the page worth citing
What to do. A page that only rearranges what is already on page one of a search result is not worth citing. Add something that shows you actually know this territory: first-hand process detail, a worked example, an anonymized pattern from support or sales conversations, a clearly dated internal observation, or a named person standing behind the advice. Cite sources for outside claims, date anything time-sensitive, and say plainly where a recommendation does not apply.
How to tell it is done. Every major recommendation in the piece has at least one source, a labeled company observation, a worked example, or a stated limitation behind it, and the article has a named author or reviewer where readers would expect real expertise.
Common mistake. Do not invent a customer result, a benchmark, or a quote to fill a gap. If you have no evidence for a claim, either state the limitation honestly or cut the claim. In-market SaaS buyers researching a purchase are unusually alert to content that overreaches, and it costs you more than a thin page would. Evidence is what turns an ordinary page into ai surfaced consideration content worth citing, rather than one more rewrite of the same search results.
Make the page technically discoverable and citation-ready
What to do. None of this needs a special AI-search format. Publish the full answer as crawlable text, make sure the page is indexable, and link to it from other relevant pages with descriptive anchor text. Write an accurate title and meta description, make your headings describe what is actually in each section, and keep the core answer in visible text rather than locked in an image or a form-gated PDF. Use structured data only where it accurately reflects what is on the page. Google states plainly that AI Overviews and AI Mode rely on the same foundational SEO practices as ordinary search results, with no extra AI-specific technical requirement for eligibility.
Google has also said that AI Overviews and AI Mode can use query fan-out, issuing several related searches across subtopics while building an answer. That is a reason to build one strong, well-structured page for a real buyer task rather than a pile of thin pages chasing every phrasing of the same question. That gap between ranking and citation is widening: an Ahrefs analysis of 863,000 search results found that only 38% of AI Overview citations came from top-ten pages in March 2026, down from 76% a year earlier.
How to tell it is done. The page is indexable, a crawler can reach the main content, the answer sits in visible text, the title and headings match the actual content, and Search Console shows no indexing blocker.
Common mistake. Do not tell yourself that FAQ schema or a particular word count guarantees an AI citation. Google is explicit that there is no such shortcut, and chasing one wastes effort that a genuinely useful page would reward instead.
Publish, measure, and improve using real visibility data
What to do. Once a page is live, track it against the prompt cluster it was built to answer: whether your brand gets mentioned, whether the page gets cited, which prompts actually produce visibility, and which competitors show up instead of you. Give it time before judging it, since crawling, indexing, and AI data collection all take longer than a same-day check. If a page gets no visibility, look first at intent fit, indexing, and whether it reads as too generic. If it gets mentioned but not cited, sharpen the direct answers and evidence. If it gets cited but is not helping buyers move forward, work on the next step and the internal links around it.
How to tell it is done. The page has a baseline set of prompts, an owner, a review date, at least one visibility metric, and a rule for when you would refresh or fold it into another page.
Common mistake. Do not collapse mention rate, citation rate, rankings, and revenue into a single "AI visibility" number. They measure different things, and mixing them makes it hard to know what actually needs fixing. Bing's Webmaster Tools team makes a similar point about its own AI Performance report: it says plainly that the report does not measure rankings, authority, or traffic, only citation activity, so treat it as one input rather than the whole picture.
DeepSmith's AEO tracking exists to make this step less manual. It connects your tracked prompts to mention rate, citation rate, share of voice, and the specific pages earning citations, so you can see saas consideration content AEO performance change over time instead of guessing from occasional spot checks. That is the same data Content Map and Opportunity Agents use earlier in this process, which is the point of keeping visibility and production in one place: the same evidence that flagged the gap tells you afterward whether the page actually closed it.
A quick example of what this looks like end to end: say your prompt map turns up "what should I look for in an AI content tool for a two-person marketing team." That is a clear consideration task with an obvious page type, an evaluation framework, and a clear boundary against turning it into a vendor ranking. You would brief it around the criteria a lean team actually needs (accuracy grounded in your real product details, output that does not need a rewrite, and a workflow that survives a team of one), publish it with the answer stated up front, and then watch whether it starts getting mentioned or cited for that exact question over the following weeks. That loop, from ai surfaced consideration content back to the data that shaped it, is what keeps a MOFU program from turning into a pile of articles nobody checks on again.
None of this replaces editorial judgment, but it does turn content for in-market SaaS buyers into a system you run every week instead of a one-off project. If you want to see this whole workflow running on your own prompts and pages rather than reading about it, DeepSmith's free trial gives you real data and a real draft before you pay anything, with no long-term contract attached.
