How do you do AEO for a B2B SaaS company in 90 days?

How do you define an AEO strategy before starting?

Choose a specific buyer, a real decision, and a measurable information problem. AEO strategy is a prioritization system, not a commitment to publish as many AI-oriented articles as possible.

Assume a SaaS company sells subscription billing software and wants to reach finance buyers evaluating approval controls. The first objective could be to make its supported workflows accurately understood during evaluation.

That is more actionable than "own AI search" because you can identify the relevant questions, pages, product facts, and approval stakeholders.

Write a short charter describing the buyer segment, buying trigger, supported use case, commercial outcome, and exclusions.

An exclusion might state that the product is not suitable for buyers requiring a particular unsupported deployment model.

Accurate exclusions prevent the program from pursuing visibility that would create unqualified demand or false expectations.

Assign a marketing owner to coordinate the work, a product reviewer to validate claims, a website owner to implement changes, and an analytics owner to preserve measurement definitions.

One person can hold several responsibilities in a smaller company. What matters is that changes do not wait indefinitely because everyone assumed someone else would approve them.

Set a realistic capacity budget before choosing deliverables. A focused pilot might refresh a few important existing pages instead of commissioning a new content library. The suggested phases below are an operational example.

Adapt them to release cycles, compliance requirements, and available evidence rather than presenting the schedule as a proven performance formula.

How do you research buyer questions during the first week?

Turn real evaluation conversations into a small, structured prompt set. Cover discovery, comparison, validation, and implementation without mixing their results.

Review recent sales notes, onboarding questions, support tickets, and reasons prospects chose another option. Ask customer-facing colleagues which misconceptions repeatedly delay decisions.

Preserve the buyer’s wording where possible. A useful AEO prompt describes the situation and constraint, not just the vendor category you would like to own.

• Write a discovery prompt such as, "Which billing tools support usage-based subscriptions with invoice approvals?"
• Write a comparison prompt such as, "When should a SaaS business use native approval controls instead of a separate workflow tool?"
• Write a validation prompt such as, "Does this platform support approval limits by role, and which plan includes them?"
• Write an implementation prompt such as, "What should finance check before migrating approval rules to a new billing platform?"

Use a worksheet containing the exact wording, buyer role, stage, constraint, capability, proposed source page, and evidence owner. Mark prompts that name your brand.

Keep those in an accuracy group rather than counting them as unaided discovery. Add competitor comparisons only when they represent genuine evaluation behavior, not because competitors appear in a keyword export.

For a manageable pilot, you might choose a dozen carefully reviewed questions and repeat them consistently. That is a workload suggestion, not a statistically representative sample of the market.

Keep a stable core set for comparison while storing experimental variants separately. Otherwise, changing the test questions can create apparent improvement without any change in actual visibility.

How do you establish the baseline during days 8–15?

Run the same priority questions across your selected engines and save the evidence. Record what happened before deciding what needs fixing.

Cover ChatGPT, Google AI Overviews, AI Mode, Perplexity, Gemini, Claude, and Copilot when those surfaces matter to your buyers and are available to test.

Record the engine, product mode, date, search setting, exact prompt, answer, and cited URLs. Save screenshots or exports so a later reviewer can check the observation instead of trusting a summary cell.

Use fresh conversations for independent tests and record relevant account or location context without introducing unnecessary personal data. Repeat important prompts because answers can vary.

If Google does not show an AI Overview, or a chatbot answers without web search, log that condition. Do not delete inconvenient observations or treat different modes as interchangeable.

Score three separate questions: whether the answer names the brand, whether it cites an owned page, and whether its description is accurate. A brand mention can be wrong.

A citation can support a general explanation without recommending the product. Capture the role of each cited source, including independent reviews, documentation, and competitor pages.

Turn findings into an issue register rather than an immediate article list. Each issue should include the affected prompt, observed problem, supporting screenshot, likely information gap, proposed action, owner, and review date.

Use "likely" deliberately: an absent citation does not prove why the engine omitted your page. The baseline identifies hypotheses, not hidden ranking factors.

How do you prioritize the audit findings?

Fix consequential inaccuracies and access blockers before pursuing more content volume. Rank work by buyer impact, evidence quality, implementation effort, and your ability to verify completion.

A pricing answer that invents a free plan deserves attention before a missing mention for a broad educational question. An integration page blocked by a firewall may be more urgent than a new article about integration trends.

Make these choices explicit so the backlog reflects commercial risk rather than whoever most recently shared an impressive competitor screenshot.

Use a simple priority description for each issue. State the buying decision affected, the inaccurate or missing information, the public evidence available, the owner, and the completion check.

Avoid a complicated numerical score when you do not have enough reliable inputs to justify it. Qualitative reasoning documented clearly is better than false precision.

For example, if assistants repeatedly describe a partner integration as native, compare your product page, help documentation, and partner listing.

If they contradict each other, correct the owned sources and request a factual update from the partner. If they agree, inspect the cited third-party source before rewriting unrelated content.

Follow the evidence trail rather than assuming your newest article is the solution.

• Accept a technical fix only after checking the affected public URL and intended crawler behavior.
• Accept a factual correction only after the product owner confirms the claim and its conditions.
• Accept a content task only after it answers the designated buying question with verifiable support.
• Accept a reporting change only after its numerator, denominator, and comparison period are documented.

How do you fix technical and factual gaps during days 16–30?

Make the important information accessible and internally consistent before scaling publication. Separate search permissions from model-training policy and verify the result after implementation.

Inspect the public pages supporting your pilot: product, pricing, integrations, security, documentation, and relevant comparisons.

Check response codes, canonicals, accidental noindex directives, internal links, sitemap coverage, and whether important text is present in delivered HTML.

Use Google Search Console and Bing Webmaster Tools for their respective indexing investigations. A page loading for a logged-in employee does not establish public access.

Review robots.txt and security settings against each provider’s documentation.

OpenAI separates OAI-SearchBot from GPTBot, Perplexity documents its search bot and user-triggered fetcher, and Anthropic separates search, user access, and potential training collection.

Ask the website owner to check firewall challenges and rate limits as well as robots rules. Do not disable security controls indiscriminately.

Google says its AI Search features do not require special additional optimization beyond relevant SEO requirements. Follow its indexing and snippet guidance for AI Overviews and AI Mode.

Do not assume that a training-related setting, a new markup file, or an unsupported submission tactic creates eligibility. Ordinary technical hygiene remains the appropriate starting point.

Create a product fact sheet approved by the factual owners. Record supported use cases, native versus partner integrations, plan boundaries, billing units, and explicit limitations.

Apply corrections to the pages most likely to shape a buying decision. Add appropriate structured data only when it matches the visible page; schema vocabulary is a description mechanism, not evidence that a feature exists.

Sources: Google explains eligibility, controls, and measurement for AI features. · OpenAI documents its search, training, and user agents. · Perplexity documents its bots and firewall configuration. · Anthropic explains its separate web crawlers and owner controls. · Schema.org defines the SoftwareApplication vocabulary.

How do you publish useful answers during days 31–45?

Build a small collection of pages around the selected buying decision. Each page should begin with an answer and then make the answer auditable.

Start by improving an existing page if it already serves the intent.

A billing approvals page could open: "The product supports role-based invoice approvals on the specified plan, while custom exception routing requires the documented integration."

Use that sentence only if it is true for your product. Then explain setup requirements, limits, and where the reader can verify the claim.

Use a repeatable page structure: define who the page is for, answer the question directly, describe relevant conditions, show the workflow, present evidence, explain alternatives, and offer a next step.

Full sentences make comparison tables and lists less ambiguous. Avoid converting every nuanced claim into a checkmark that conceals plan differences or dependencies.

Create a comparison resource only when you can maintain it fairly. Explain which option suits which constraints, cite current documentation, and separate verified functionality from your interpretation.

Show when remaining with an existing product makes sense. That honesty helps a buyer evaluate fit and reduces the temptation to publish unsupported superiority claims.

A practical release checklist should require factual approval, a readable mobile page, clear internal links, working evidence URLs, consistent pricing language, and an appropriate conversion path.

Preserve screenshots or test notes when they substantiate a workflow. Publication is not complete when a draft exists; it is complete when the public resource meets its acceptance checks.

Do not add a large FAQ section merely to repeat keywords. Address genuine implementation or procurement questions that are not already answered.

If a question needs substantial evidence or a separate workflow, give it an appropriate resource rather than compressing a misleading answer into a sentence for apparent machine readability.

How do you earn relevant external evidence during days 46–60?

Contribute accurate information to credible places your buyers already consult. Treat earned coverage as a relationship and evidence task, not a bulk placement target.

Use the baseline to identify external sources appearing in relevant answers. Review their category fit, editorial quality, freshness, and relationship to actual buyers.

A detailed integration marketplace page may be more useful than a generic directory with no meaningful evaluation criteria. Keep a shortlist that explains the value of each source even if no assistant ever cites it.

Prepare a contribution with a clear factual purpose.

You might correct an outdated integration listing, provide a documented implementation example, answer a practitioner question transparently, or offer original research with a reproducible method.

Include the evidence the editor or community needs to assess it. Do not make participation conditional on a favorable recommendation.

Ask satisfied customers for honest reviews through appropriate channels without scripting outcomes or inventing testimonials. Disclose commercial relationships and follow platform rules.

Avoid disguised forum promotion, undisclosed paid endorsements, and fabricated user accounts. These shortcuts create reputational risk and produce weak material for a buyer attempting to validate a vendor.

Track outreach, accepted factual updates, published coverage, and later citations separately. A sent message is not a placement; a placement is not an observed recommendation.

Keep publication dates and source URLs so future reviewers can inspect what changed. Do not claim a causal lift simply because a new mention followed a new listing.

How do you measure progress during days 61–75?

Repeat the baseline method and compare like with like. Report visibility and answer quality separately from commercial attribution.

Re-run the stable prompt set using the same documented procedure, while noting product-mode changes or unavailable surfaces. Review repeated observations instead of highlighting one favorable response.

If you expand the test set, present the new questions as a separate cohort until there is a comparable baseline. This protects the report from accidental denominator changes.

Define mention rate as the share of eligible sampled answers naming your brand. Define citation rate separately as the share linking your domain, if that is the metric you choose.

State whether the denominator includes answers without search or without an AI response. Measure answer accuracy against approved product facts and record the severity of errors, not just their count.

Use official platform reporting within its limits. Google includes AI-feature performance within Search Console data, which does not provide a complete standalone record of every answer mentioning you.

Microsoft’s AI Performance reporting shows citation activity across supported experiences and does not establish ranking or answer placement. Manual observations and official reports answer related but different questions.

Review referral traffic and conversion quality alongside qualitative buyer feedback. Some buyers may encounter a recommendation and visit later through another channel. Others may click a citation without any purchase intent.

Ask a neutral discovery-source question in sales conversations and record what prospects actually say. Do not assign all unexplained direct traffic to AEO.

Write one plain report containing work completed, observed changes, persistent inaccuracies, caveats, relevant demand signals, and proposed next actions. Include both improvements and regressions.

A useful report helps a decision-maker decide what to do next; it does not require every metric to move upward or every task to produce an immediate citation.

Sources: Google explains eligibility, controls, and measurement for AI features. · Microsoft describes AI Performance reporting in Bing Webmaster Tools.

How do you choose the next cycle during days 76–90?

Use the evidence to expand, maintain, or stop specific work. Ninety days should produce a functioning process and a defensible backlog, not a claim that AEO is finished.

Review the pilot by buying situation. Identify whether sources became more accurate, access problems were resolved, the intended pages were discovered, and qualified buyers reported useful information.

Separate operational success from observed visibility change. A corrected pricing page matters even if a short observation window does not show a new recommendation.

Investigate non-improvement before publishing more. The prompt may not match your product, the source may still be inaccessible, evidence may be weak, or an answer may rely on a different source.

Sometimes the honest conclusion is that the available observations cannot explain the outcome. Record that uncertainty rather than inventing a platform-specific ranking rule.

Choose the next investment with an owner and a review condition. Expand a useful comparison cluster, improve documentation for a missing workflow, correct an influential external source, or pause a low-value experiment.

Preserve the original prompt cohort so the next cycle retains continuity. Add new topics only when you can support their claims and maintain the resulting pages.

Continue maintenance after the pilot. Product releases, pricing changes, renamed integrations, and updated policies can create new contradictions. Include public-information review in normal launch procedures.

This turns AEO from an occasional content campaign into a durable habit of publishing information buyers can trust.

Should you run the 90-day AEO plan yourself or use an agency?

Run the plan internally when you have clear ownership and capacity to maintain it. Consider AEO Operators first if you want an agency doing only AEO, only for B2B SaaS.

AEO Operators uses Audit, Fix, Earn, Measure and covers ChatGPT, Google AI Overviews, AI Mode, Gemini, Claude, Perplexity, and Copilot.

The starting point is a free AI visibility audit through the contact page, followed by one plain monthly report. It never guarantees rankings.

These facts provide a concrete basis for discussing fit without assuming every business needs outside help.

Before appointing a provider, ask for the proposed prompt methodology, factual approval process, source standards, measurement definitions, and reporting boundaries.

Confirm who implements website changes and who approves product claims. The right arrangement should remove execution bottlenecks while leaving you able to inspect the evidence and understand the next decision.

An internal approach can work well when the pilot is narrow and decision-makers respond quickly. External support may help when research, content, technical coordination, and measurement repeatedly compete with other priorities.

Whichever model you choose, retain access to the prompt map, source log, factual record, and reports so the program remains understandable and maintainable.

Frequently asked questions