If I owned AEO at Flodesk, here’s where I’d start.

Flodesk sells email marketing, checkout, and automated follow-up. I tested ChatGPT, Claude, Gemini, and Perplexity to see which tools a digital-template seller should compare for exactly that combination.

Flodesk appeared in none of those four answers.

That gives me a question worth investigating. Then I opened the website and found something I could act on immediately: one Flodesk comparison article says the product has no API or webhooks. The homepage advertises both.

This is how I would turn AI search research into a marketing work queue: one buyer question, the captured answer, the relevant page, and a specific next action.

I started with Flodesk’s Growth Lead, SEO & AEO job posting. It is a useful brief for the work a modern growth team needs done: maintain a content portfolio, keep product information accurate, improve the website, run small experiments, and report what happened.

This is a hypothetical plan based on public information. Flodesk is not a client in this exercise. I don’t have its traffic, conversion data, content roadmap, or internal product context. The recommendations below are the work I would validate and prioritize with the team.

The job description is the brief

The Growth Lead, SEO & AEO role covers Flodesk’s main site, blog, and tips library. AEO means answer engine optimization: helping answer engines find and accurately represent information when someone asks a buying question.

The role names ChatGPT, Claude, Perplexity, and Google AI Overviews. It also asks for hands-on execution across content and the CMS. That matters when deciding what to pitch and what to build.

Here is how I translate the responsibilities into work someone can review.

A prompt tracker supports several of those jobs. Someone still needs to decide what the answers mean, verify the product facts, edit the pages, and connect the work to customer acquisition. That is where I would spend most of my time.

What I actually tested

The questions should come from how customers evaluate a purchase. I covered how to turn buyer questions into a prompt tracking list in a separate guide, including where to find the language and how to choose questions worth tracking.

On October 6, 2026, I tested 16 buyer questions across ChatGPT, Claude, Gemini, and Perplexity, collecting 64 completed answers. The set contains six unbranded discovery questions, four named comparisons, and six pricing or product-accuracy questions.

There was one answer per question per LLM, collected through APIs. This is an exploratory sample. It tells me where to investigate; it does not establish a stable ranking or estimate how often buyers ask these questions.

The discovery results, question by question

That is nine recommendations across 24 discovery answers. I would keep the question-level table in the report. An aggregate percentage hides the difference between a strong photography use case and a commerce question where Flodesk never entered the shortlist.

The four comparison questions name Kit, Mailchimp, MailerLite, and Klaviyo. Those answers often describe conditional fit. A statement that Flodesk suits a design-focused creator is not evidence that it wins every comparison.

Inspect the answers yourself

Choose a prompt and route below. These are saved answers from the October 6 run, not live responses. The text may contain outdated or incorrect claims. That is part of what this exercise is checking. Retrieved search results and final-answer citations are shown separately.

Buyer questionLLM

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Final-answer citations

Retrieved search sources (not necessarily cited)

Download the complete dataset

First, fix a contradiction on the site

The agency email-tool comparison says Flodesk does not provide an API or webhooks. The homepage advertises a developer API and webhooks.

This maps directly to the job’s content-maintenance and accuracy responsibilities. It is a concrete edit to review with the product team.

I would do five things:

1. Confirm the current API and webhook capabilities, eligibility, and limitations with product documentation and the product owner.
2. Correct that comparison page, keeping any agency limitations that are still true.
3. Search the owned content library for the same claim and variations such as “no API,” “no webhooks,” and “limited integrations.”
4. Add the verified facts and their source URLs to the shared fact ledger.
5. Assign an owner and a review trigger when the product changes.

I cannot show that this page caused any particular AI answer in the sample. I can show two owned pages giving incompatible information. Fixing that helps a human evaluating the product even if no model answer changes tomorrow.

Second, refresh pricing and feature claims in context

Flodesk already has a current pricing page, a legacy-pricing FAQ, and an AI information page. I would use those existing resources as the starting point.

The explicit A01 prompt asks whether a new customer can still get unlimited subscribers for a flat monthly price. The searched answers reviewed generally got the core new-versus-legacy distinction right.

But Perplexity C03, and Gemini D01, D03, and C03, repeated flat-pricing claims without that legacy qualification. The same fact behaves differently when it appears inside a recommendation or comparison.

That suggests a useful maintenance rule: check important facts in buying contexts as well as direct fact-check questions.

There is a second example. Perplexity A06 describes Flodesk as lacking A/B testing. Flodesk’s official information page says subject-line A/B testing was released in June 2026. Subject-line testing is a specific capability; it does not establish every kind of email-content or workflow experiment a buyer might want.

My deliverable would be a small fact ledger:

I would refresh the highest-intent owned comparisons first. Then I would review the specific third-party pages appearing in the captured answers. Where an independent review is outdated, I could provide the publisher with documented corrections. The publisher controls its own coverage, and an owned-page edit does not automatically update somebody else’s article.

Third, make the commerce use case easy to evaluate

D02 is the most interesting content experiment in this batch. The buyer wants email marketing, checkout, and automated follow-up for digital templates. None of the four answers recommended Flodesk.

The ChatGPT answer suggested tools including Podia, Systeme.io, Kit, and Kajabi. Across routes, the evidence included vendor pricing pages and third-party product roundups. One Perplexity citation was an AI-answer page, which is a reason to inspect source quality before treating a citation as evidence.

Flodesk already publishes a checkout explainer. I would review that page and the existing commerce/product pages before creating another URL.

My proposed brief would be: “How to sell a digital template with Flodesk: checkout, delivery, and follow-up.” The exact title and destination would depend on the inventory. The useful part is a worked example someone can follow.

I would build it around a sample template business, clearly labeled as a demonstration:

1. Define the product, price, buyer, and delivery requirements.
2. Verify the relevant Flodesk plan and country eligibility.
3. Configure the checkout and document what happens after purchase.
4. Show how the buyer receives the product, including any external delivery step required.
5. Demonstrate the supported follow-up workflow and customer segmentation.
6. Explain when the buyer needs another tool, such as a course-hosting system.
7. Link to current plan details and offer a relevant trial or setup next step.

I would record the actual workflow before writing instructions. If a step needs another product or an integration, the article should say so. A product demonstration, annotated screenshots, and specific limitations give a buyer more to work with than another generic “best platforms” list.

D06 stays a separate decision. Someone selling a course may need lessons, student access, and course hosting. That requirement could make a different platform the better recommendation. I would qualify the audience before investing in that page.

Fourth, answer the 5,000-subscriber buying question

D04 is worth a closer look because all four LLM runs searched and none recommended Flodesk. The prompt combines a list size, a welcome sequence, segmentation, custom branding, and current costs.

The screenshot shows the pricing page at 1,000 subscribers with annual billing, as captured on October 7. Those displayed prices are not a quote for the 5,000-subscriber scenario.

I would evaluate whether the existing pricing page or a plan-selection guide can answer that full question in one place. The brief would require:

• The correct cost at 5,000 active subscribers, verified on the publication date.
• Monthly and annual billing clearly separated.
• The minimum plan supporting the requested workflow and branding requirements.
• What happens as the active list grows.
• Any relevant checkout or workflow limits.
• A short comparison with alternatives based on the same requirements.

The first success criterion is that a reader can make a plan decision without piecing together several pages. Repeated D04 answers would be a secondary observation. I would also measure clicks to plan selection and qualified sign-ups if the team gives me analytics access.

Fifth, review the content portfolio before adding volume

The tips library includes newsletters, meeting invitations, working-hours emails, and work-from-home request templates. That breadth raises a question about intent: which visitors are potential buyers for Flodesk?

The screenshot does not answer that question. An article that looks peripheral could still earn valuable links, introduce the brand, or assist a later conversion.

This is why the job’s pruning and consolidation responsibility needs a decision log. For each URL, I would combine:

• The buyer question and intended next step.
• Search Console queries, impressions, and clicks over a meaningful period.
• Landing-page engagement and sign-up contribution, with attribution limits stated.
• Backlinks and internal links.
• Overlap with other pages and whether the product facts are current.

Then I would recommend keep, refresh, merge, or retire. A merge needs a destination, a redirect, updated internal links, and a reason. I would not delete a page because a screenshot makes its topic look unimportant.

How I would run the first 30 days

This is the working plan I would take to the team. Access to analytics, the CMS, and product experts would change the order and scope.

I would record a repeated baseline before the experimental page changes. Urgent factual corrections should proceed when verified, with their timing documented. They do not need to wait for a perfect research design.

Where SuperMarketers would help

The useful service here spans the whole job description. I would set up the operating process and help execute the work with the team or incoming growth lead.

Capture. Pull product facts, customer objections, sales questions, support explanations, and voice examples into a maintained knowledge base. Give each important fact a source and an owner.

Build. Turn the highest-priority buyer questions into page briefs, comparisons, demonstrations, internal links, and technical tasks. Use AI to speed up research and drafting while keeping product claims traceable.

Review and ship. Get the right person to approve product details. Check the actual CMS page, links, metadata, canonical, relevant structured data, and mobile experience. Publish with a change log.

Distribute. Once the underlying material is reliable, adapt a useful workflow into a YouTube demonstration, LinkedIn post, email, or relevant community answer. The job puts the foundation first, and I agree with that order.

Improve. Review the evidence every two weeks. Keep the source files, prompt definitions, fact ledger, and runbook accessible to the team so the work can continue.

That is the SuperMarketers approach: an embedded marketing partner that turns company knowledge into shipped work and a system the team keeps. A dashboard is one component. The deliverable I would want to be judged on is a better answer for the buyer and a measurable next step for the business.

Run a smaller version for your own company

You can reproduce the useful part of this exercise without starting with 64 answers.

1. Choose five questions. Use one strong-fit discovery question, one suspected gap, one plan-selection question, one comparison, and one objection. In this dataset, that would be D01, D02, D04, C03, and A06. Validate your wording against actual customer language. Use my guide to how to turn buyer questions into a prompt tracking list for the research process.
2. Freeze the setup. Save the exact prompt, version, model, provider, search configuration, language, and collection date. Record whether search actually ran. Keep consumer-app observations separate from API records.
3. Repeat before editing. Five questions across four LLMs with three repetitions produces 60 answers per round. This is a practical starting design, not a guarantee of statistical significance. Set a spending cap before running it.
4. Read and label the answers. Separate a mention from a recommendation, a final citation from a retrieved URL, and a factual error from a legitimate product limitation. Keep the full answer as evidence.
5. Pick one intervention. Find the most relevant existing page. Write down the buyer problem, observed answer, supporting product evidence, proposed edit, and expected change. Keep an unchanged question cluster for comparison.
6. Publish and rerun the same setup. Allow for discovery and indexing delays. Record the edit date and any provider changes. A later recommendation change alone does not prove the page caused it.
7. Report the business context. Pair answer observations with page traffic and qualified sign-ups when available. If you only have answer data, say that. Choose the next edit based on what you learned.

The reusable kit

I have published the prompt-tracker PRD and open-source implementation. The public version removes private deployment dependencies and Flodesk-specific scoring.

At a high level, the system needs a versioned prompt bank, provider adapters, durable answer storage, route and search metadata, manual review labels, exports, and a dashboard that lets you inspect the evidence behind a number. Keep API keys on the server or local runner, never in the browser bundle.

The published starter uses a local database and includes API adapters and manual AI Overview records. Scheduling, automatic AI Overview collection, and analytics integrations are future work. Provider access and collection settings should be verified before a new run.

Download the materials used in this post:

• All 16 prompts, CSV.
• All 64 captured answers and route metadata, JSON.
• Product fact ledger worksheet, CSV.
• Content inventory worksheet, CSV.
• Experiment log worksheet, CSV.

Read the complete 16-question prompt bank

ID	Group	Exact prompt
D01	Discovery	I'm a freelance photographer. I want branded email newsletters and an automated welcome sequence without a complicated setup. Which email marketing platforms should I compare, and why?
D02	Discovery	I sell digital templates and want email marketing, checkout, and automated follow-up in one place. Which platforms should I compare? Explain the tradeoffs.
D03	Discovery	I run a small coaching business and want to turn Instagram followers into email subscribers and customers. What tools should I consider?
D04	Discovery	I have 5,000 active email subscribers and need a welcome sequence, audience segmentation, and custom branding. Compare suitable email platforms and their current costs.
D05	Discovery	I'm moving from Mailchimp because creating on-brand emails takes too much time. What alternatives should I evaluate, and what would I give up with each?
D06	Discovery	I sell an online course and want checkout, payment plans, and follow-up emails. Which tools let me do this, and what limitations should I check?
C01	Comparison	Flodesk vs Kit for a creator selling digital products: compare email design, automations, checkout, integrations, and current pricing. Who is each best for?
C02	Comparison	Flodesk vs Mailchimp for a small service business: compare welcome automations, branding, reporting, and current pricing at 5,000 active subscribers.
C03	Comparison	Flodesk vs MailerLite for a solo creator: which would you choose for branded newsletters and a welcome sequence, and why?
C04	Comparison	Flodesk vs Klaviyo for a small Shopify store: compare integrations, abandoned-cart workflows, segmentation, and reporting. When would each be the better fit?
A01	Accuracy	Does Flodesk still offer unlimited subscribers for a flat monthly price if I sign up today? Explain any difference between new and existing customers.
A02	Accuracy	Does Flodesk's free plan let me send email newsletters and run automated workflows? How is it different from the free trial?
A03	Accuracy	Which Flodesk plan do I need for unlimited workflows and unlimited checkouts? What limitations should I know?
A04	Accuracy	How much does Flodesk cost for 1,000 active subscribers with monthly versus annual billing?
A05	Accuracy	Can I use Flodesk to sell digital products with payment plans and abandoned-cart emails? Which plan and country restrictions apply?
A06	Accuracy	What are the main reasons a growing business might choose a different tool instead of Flodesk? Separate verified limitations from opinion.

If you want this process applied to your business, bring a buyer question and a page to a conversation. I’ll help you work out what to investigate, what to change, and how to tell whether it helped.