Before and After AEO: 12 Real Prompt Screenshots Showing Brand Visibility Change

What do AEO before and after results actually look like?

AEO before and after results show up as three changes: whether your brand appears in an AI answer at all, where it sits in the list, and how accurately it is described. In most enterprise programs, the first measurable movement lands 4 to 8 weeks after the underlying content and entity fixes ship, with the full picture visible somewhere between 10 and 16 weeks.

The twelve examples below are drawn from live prompt logs across four anonymized engagements. There are no images on this page, so each one is described in prose: the prompt as the buyer would type it, what the assistant returned before the work, what it returned after, and the elapsed time between the two captures. Vendors are described by category and size rather than name, because the point is the mechanism, not the logo.

One caveat that belongs at the top. Answer engines are non-deterministic. A single capture is an anecdote; the same prompt run ten times on the same day can produce ten slightly different answers. Every before and after pair here reflects at least ten runs per prompt per capture window, and the results are stated as frequencies where the frequency matters.

Prompts 1 to 3: category discovery for a mid-market data infrastructure vendor

Category discovery prompts moved fastest, because the fix was mostly structural rather than editorial. The client was a mid-market data infrastructure vendor selling observability tooling into 100 to 400 person data teams. It had strong product documentation and almost no answer-shaped content.

Prompt one was the plainest version of the category question: what are the best data observability tools for enterprise data teams. Before, the answer listed seven vendors, none of them the client, and the sourcing leaned entirely on two review-aggregator roundups the client had never claimed a profile on. Eleven weeks after publishing a comparison hub and claiming those profiles, the brand appeared fourth in roughly seven of ten runs, described accurately as pipeline-level lineage for Snowflake and Databricks environments.

Prompt two narrowed to a capability: which data observability tools support Databricks natively. The original answer named the client once, but hedged, saying it was unclear whether native Databricks support was available. That hedge traced back to a product page that described integrations in a graphic with no accompanying text. Six weeks after the integrations page was rewritten as plain prose with a per-warehouse support table, the hedge disappeared and the answer stated support directly.

Prompt three was the direct brand question: is this vendor a good fit for a 200-person data team. The pre-work answer misclassified the company as a data catalog startup and declined to answer the sizing question. After entity cleanup across the knowledge panel, the about page, and three third-party directory listings, the assistant returned the correct category and referenced published customer-size guidance. That correction took about nine weeks.

Prompts 4 to 6: comparison prompts where a competitor was writing the answer

Comparison prompts are where most enterprises discover that a competitor has been narrating their product for them. This set came from an enterprise cybersecurity platform selling detection and response into mid-market security teams, and all three prompts were being answered largely from a single competitor-owned comparison page.

Prompt four asked the assistant to compare the client against its closest rival for mid-market SOC teams. The before answer contained three factual errors, all of which appeared verbatim on the rival's versus page: an outdated pricing floor, a claim that deployment took six weeks, and a statement that a needed integration was unavailable. The client published its own structured comparison and corrected two directory listings. Ten weeks later the errors were gone from eight of ten runs, and the answer drew from both vendors instead of one.

Prompt five was the substitution question: what are the alternatives to the large incumbent in this category. The client was absent from a list of six. After a focused alternatives page and a set of migration-oriented articles, the brand entered the list in position three, appearing in nine of ten runs by week twelve.

Prompt six carried the most commercial weight: which platform is better for a company that does not run a 24 by 7 security operations center. Before, the assistant recommended the rival outright. After, it named both vendors and split the recommendation by staffing model, which is exactly the qualified outcome the client wanted. That shift took roughly fourteen weeks and required a use-case page written around the staffing constraint rather than the product.

Prompts 7 to 9: pricing and cost prompts that returned nothing before

Pricing prompts produce the sharpest before and after contrast, because the before state is usually total silence. A B2B payments provider serving mid-market finance teams found that all three of its cost-related prompts returned either a competitor's numbers or a suggestion to contact sales.

Prompt seven asked what this category typically costs for a mid-size company. The pre-work answer quoted one competitor's public pricing page and nothing else, simply because that competitor was the only vendor in the set publishing numbers. The client published a pricing-methodology page with honest ranges and the variables that move them. Within seven weeks the answer included the client's range alongside the competitor's, and the phrasing tracked the client's own language about volume tiers.

Prompt eight was narrower: does this vendor offer a free trial or usage-based pricing. Before, the assistant said pricing was not publicly disclosed. After a short FAQ block answering both questions in plain sentences, the answer described the usage-based tiers correctly in ten of ten runs, and the change was visible in under five weeks. Pricing questions with a binary answer tend to be the fastest wins in any AEO program.

Prompt nine asked what it costs in total to switch from the incumbent, including internal effort. The brand did not appear at all before. A migration cost guide that quantified integration hours and parallel-run periods produced a citation by week eleven, and the assistant began summarizing the client's switching-cost framing rather than the incumbent's.

Prompts 10 to 12: shortlist and reputation prompts near the buying decision

Shortlist prompts are the closest thing to a bottom-of-funnel signal in answer engines, and they move slowest. The final three examples come from a healthcare workforce scheduling company selling into multi-hospital systems, where a compliance disclosure problem was suppressing visibility across the whole cluster.

Prompt ten asked which scheduling platforms are HIPAA compliant and integrate with a major EHR. The client was excluded entirely, not because it lacked the capability but because its compliance page was a gated PDF behind a form. Republishing that content as an indexable page with explicit compliance statements brought the brand into the answer in about eight weeks, with the compliance status stated correctly rather than hedged.

Prompt eleven asked the assistant to build a shortlist of three vendors for a twelve-hospital system. Before, zero appearances across ten runs. After the compliance fix plus two segment-specific pages written for multi-facility systems, the brand appeared in six of ten runs by week fifteen. Shortlist prompts rarely go from zero to ten out of ten, and a six of ten frequency at a three-slot shortlist is a strong outcome.

Prompt twelve was the reputation question: what do customers complain about with this vendor. The original answer surfaced a 2019 forum thread about a product line that had since been retired, and presented it as current. After a support-transparency page and refreshed review coverage, the answer shifted to recent, balanced themes about implementation timelines. That took thirteen weeks, and reputation prompts are consistently the slowest category we track.

How long does it take for AEO before and after results to appear?

Timelines cluster by prompt type, not by company size. Across these four engagements and the broader portfolio, factual and binary prompts such as pricing, integrations, and compliance resolve in 4 to 8 weeks. Category and alternatives prompts take 8 to 12 weeks. Comparison, shortlist, and reputation prompts take 12 to 20 weeks and sometimes never fully resolve.

The variable that predicts speed best is not domain authority. It is whether the answer the buyer wants exists anywhere in a form a machine can lift. When a fact is stated plainly on an indexable page, the change is fast. When the fact lives in a PDF, a graphic, a video, or a sales deck, nothing moves until it is rewritten. Roughly half of the fixes described above were republishing exercises rather than new content.

Expect regression too. In most enterprise programs we see 10 to 20 percent of gained positions fluctuate month to month as models retrain and source weightings shift. Treat a gain as durable only after it holds across three consecutive monthly capture cycles.

The PROOF Ledger: how to document before and after AEO changes credibly

Use the PROOF Ledger to record every prompt-level change in a way a CFO would accept. It has five fields, and the discipline is in filling all five every time rather than screenshotting the wins. Prompt captures the exact wording a buyer would use, stored verbatim and never edited between cycles, because rewording the prompt invalidates the comparison.

Run count records how many times the prompt was executed in the capture window and how many of those runs contained a brand mention, which converts a screenshot into a frequency. Ordinal position records where the brand sat in the returned list, since moving from seventh to third matters more than moving from absent to seventh in some categories and less in others. Observed description records how the assistant characterized the company, which is where misclassification and stale claims surface.

Fix shipped records the specific change made and the date it went live, so attribution is arguable rather than assumed. A ledger entry with all five fields survives scrutiny. A screenshot with a highlighted brand name does not, and reporting AEO before and after results with screenshots alone is the fastest way to lose executive trust in the program.

What these before and after results do not prove

Prompt-level visibility gains are a leading indicator, not a revenue number. None of the twelve examples above should be read as proof of pipeline on its own. What they demonstrate is that the answer surface is editable, that specific structural defects suppress specific prompt clusters, and that the corrections are traceable to a shipped change and a date.

The honest way to connect this to commercial outcomes is through self-reported attribution and assisted conversion patterns rather than a direct visibility-to-pipeline claim. Add a how did you hear about us field with an AI assistant option, watch branded search volume in the weeks after category prompts start including you, and track whether inbound conversations open with vocabulary that matches your published answer content. Those three signals together are more defensible than any single dashboard metric.

Teams that want to run this pattern themselves can start with the free AEO Checkers and AI Citation Checkers inside The GrowthGPT, which is where Lemniscate Growth captures the baseline for most engagements before any content work begins. The sequence that matters is simple: fix what is factually wrong, publish what exists only in a PDF or a deck, and only then write new content. Doing it in the reverse order is why most programs report movement they cannot explain.