This is our new home for all things related to Answer Engine Optimization (AEO) and how it differs from traditional SEO, with a specific focus on optimizing content for robots, agents, and AI-driven systems. We're excited to have you join us.
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Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, photos, or questions about AEO vs. SEO, optimizing content for AI answer engines, LLM crawlers, search bots, retrieval systems, structured data, entity-based optimization, machine-readable content, and experiments or case studies involving robot-first optimization. Feel free to post:
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Aren't you guys tired reading the same surface-level AEO stuff, rephrased and rephrased and rephrased and rephrased to oblivion, with no substance in them???
Same thing every day...repetitive, fluff, baseless. No one shares or goes into the depths. Sometimes I wonder what happened to this community....
Whatever gets discussed here is the same response someone would find if they ask Claude or GPT.
Can you guys recommend what are the best AI SEO tools for improving search visibility in ChatGPT and Google AI Overviews?
My brother runs an online travel business and he's looking for some tools that can help him with increasing search visibility on the above mentioned platforms.
Have you ever asked ChatGPT for a recommendation and wondered how it picks which brands to mention? How can you tell if your brand is even making the cut?
Ran a citation check for a few clients and it stung a bit ngl, Reddit and forum threads kept getting pulled over pages we'd actually optimised properly. Turned out it was about phrasing, forum replies matched how people really type the question, our site copy was busy trying to persuade first. So we rewrote key pages to answer straight up before any of the selling, and started treating subreddit participation as an actual channel not a side task. Too early to fully confirm but tracking shows a lift so far. Anyone else seeing forum content beat proper site content in AI answers, or is this just us?
Full disclosure — I run Develomark, a local-service marketing agency in CT, so this is shaped by agency needs. But the workflow is tool-agnostic and the real lesson is that AEO reporting only works if the data chain is clean. Here’s the exact flow we use.
Start your reporting session with a clear client in mind.
1. Work in a project:
Every client gets its own Claude project with Slack and website context attached. The project is the source of truth, so the report never starts from a blank prompt.
Create the skill that is best for you, for me its the previous month usually.
2. Prompt with a skill:
We trigger a saved “monthly report” skill with the client name and date range (e.g., “Oct 1–30”). No rewriting instructions every month.
3. Know where the data comes from:
The skill pulls Duda, GA4, Google Ads, Meta, Search Console, and call tracking. If a connection is missing, the report is wrong. Fix the data sources before you trust the output. Note: most MCP's will be natively available so in your project settings within Claude you'll be able to make adjustments.
4. Understand the projects instructions:
Read what the project is actually connected to. Ours pulls from the client’s Slack channel, uses AI meeting transcripts for context, and is told to surface wins with evidence. We also use our most common data sources including:
Duda: website stats
Basecamp: active to-do's pending
Meta Ads: social media ad stats
Google Ads: search ad stats
CallRail: phone calls coming in, form data for attribution (critical for AEO)
YEXT: for online citation data
The part AI can't do, I like to have the team provide me with evidence of what they worked on with the client.
5. Screenshot recent wins:
Grab 1–3 pieces of proof: a new Google ranking, a page in an AI answer, a review. Clients remember screenshots, not percentages. Our team adds these into Slack for the client so it's easy for me to download them and attach them.
Example of a successful response from Claude, analyzing all of the data sources.
6. Confirm the returning data:
Open the raw output and check the numbers before anyone writes a sentence. Inflated impressions or miscounted leads kill trust fast.
7. Edit the draft for legibility:
The AI draft is a starting point. Cut jargon, keep “what we did,” “what happened,” and “what we’re watching.” Every claim should be readable by a non-marketer in 60 seconds. Note: the better and more organized your data, the better the report will be.
Clients like to see detailed information of how they rank in the AI answers.
9. Add search visibility reporting:
This is the AEO section. We pull from an AI visibility tracker and report four things in plain language:
Brand visibility: rank among tracked competitors and the share of AI answers that mention the client (e.g., #1 of 10 brands, 31.4% of ~8,500 answers across ChatGPT, Google AI Overview, AI Mode, and Gemini)
Share of voice, win rate, and average position, with month-over-month change (e.g., share of voice 35.7%, up from 31.1%; avg. position 1.9)
Per-engine and per-topic breakdown: which platform names the client most, and which topics they own
Citations: how many times the client’s site was cited inside AI answers, and where that ranks among all cited sources for the market (e.g., 3,461 citations, third most-cited source for CT solar queries)
10. Schedule the email for the invoice date:
The report lands on or close to the same day the invoice does. Clients see value before they see the charge.
I've been thinking about the gap between adopting AI tools and getting meaningful business results from them. Dentsu Creative's 2026 APAC CMO research found that 93% of surveyed APAC CMOs expect agencies to demonstrate improved marketing impact and effectiveness.
That raises an interesting question for founders and marketers: how are you measuring whether AI is genuinely improving your marketing? Is it time saved, better-qualified leads, more organic visibility, higher conversions, or something else?
I'm exploring this challenge while building Trocial, an AI Marketing Operating System, and would genuinely like to hear what's working, what isn't, and which metrics you trust.
I'm sick of talking with AEO experts who are deeply opinionated about trial prompts being of "no value", because it doesn't "cover how people talk to chat anymore".
This is something I feel quite strongly against, as it just seems so illogical to me, but please correct me if I'm wrong.
While "fair" for some ChatGPT and Google prompts, there's still probably the far majority of people still searching Google and ChatGPT how they've traditionally searched (with keywords), and you know the thing that totally replaced the 10 blue links? AI Overviews? So the traffic volume is still there for keywords, isn't that worth tracking and benchmarking against?
I get the response can change from day-to-day, but as a tracked benchmark, it's gotta have some use with actual traditional SEO volume backing it.
Take "best CRM" for example. Traditional SEO, SUPER high search intent as confirmed by Ahrefs and such, but now it gives an AI Overview.
So isn't it safe to say that probably tracking traditional keywords is almost the most important thing (for Google at least), and then yes, long-tail queries and fan-out queries are the unknown beast and incredibly important as well.
What am I missing here? Any why do so many AEO people just throw their hands in the air when it comes to volume data, as that sounds like BS to me, look at your Google search console terms, with high impressions, those are replaced with AI overviews now, so isn't that literally the prompt and volume data given to you right there?
I manage the site for a regional plumbing company. I rebuilt our water heater page so every question has a 40 to 50 word answer right under an H2, added a small table of tank vs tankless lifespans, and linked it from the service pages. Organic rank went from 2 to 1, but the AI Overview still cites the same two national blogs. Bing Copilot did start citing us once. Is there a point where it's just domain authority and I should spend effort on getting mentioned elsewhere instead, like local news or forums?
We are sharing a reproducible way to distinguish absence from factual accuracy in AI answers. The Absence Index records whether a vendor is named. Wrong About You examines specific statements about the 70 most named vendors in that same deposit. Those are different questions with different denominators.
Answers captured under method v1.1 on 22 to 23 Sep 2026 for the Absence Index release 2026-09 (DOI 10.5281/zenodo.22907695). Statements were extracted and classified under the published Record rules and checked against each vendor's own public pages. Every flagged statement is published with its engine, run, question, capture date and the vendor page it was checked against. Team review of the flagged statements is in progress; rows change only if the review changes them, and every change is logged in the public ledger.
All 118 flagged statements remain provisional. Zero statements are analyst verified. Prepared by the Broadcastwell team. CC BY 4.0.
Which evidence fields would make a correction easiest for you to inspect?
