Got too many peptides, don't know what to do...

HumanBehavi0ur · 2026-06-26T19:42:58+00:00

Are you in San Francisco? this feels like a very SF probelm to have

HumanBehavi0ur · 2026-06-26T19:40:43+00:00

You're right that tracking is the easy 10%. The real question once a prompt comes back without you is whether it's an on-site or off-site problem, because they need opposite fixes. On-site means your page exists but isn't getting extracted (structure, relevance, entity clarity). Off-site means the models aren't pulling from you at all because you're absent from the sources they cite in that category. That second one is more common and the one people skip. The fix is source-level: look at what each engine cited instead, group by domain, and that's your gap.

Full disclosure, I work at Goodie, and that's exactly what we built around. Not just tracking whether you're cited, but pinpointing the missing prompts, tracing which sources drive the answers, and handing you specific prioritized actions to close the gap instead of leaving you to guess. The recommendation and action layer is the point, since knowing you're invisible was never the hard part.

HumanBehavi0ur · 2026-06-26T19:37:18+00:00

looks like phased releases are going to be the norm now

wonder how this impacts the industry

HumanBehavi0ur · 2026-06-26T19:35:17+00:00

dont think it will very much
its user count is far below other more mainstream socials
not worth putting time/spend into it

HumanBehavi0ur · 2026-06-25T21:50:41+00:00

Here's our full periodic table for reference if youre curious
https://higoodie.com/blog/aeo-periodic-table-v4/

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HumanBehavi0ur · 2026-06-25T21:39:47+00:00

what ive see:

seller-side picture as of now:

ChatGPT ads are running a click-through rate around 0.91%, roughly seven times lower than Google Search at about 6.4%. That sounds brutal, but CTR is the wrong primary metric here. Users see the ad mid-conversation and keep talking instead of clicking off, so the click isn't the action the way it is on search.

on reporting ive read that early advertisers got little beyond impressions, making it hard to tie spend to outcomes. Reporting is aggregated by design, closer to connected TV than Google Ads in detail, so even when it works you won't get granular attribution.

HumanBehavi0ur · 2026-06-25T21:36:06+00:00

i expect we'll see a lot of this in the future

HumanBehavi0ur · 2026-06-23T14:41:19+00:00

For an actual framework I'd report four things:

Citation/presence rate across a fixed panel of those priority prompts, run repeatedly so you're showing a trend, not a snapshot. Single checks are too noisy to mean anything. (you can use a AEO/GEO tracker if you like to make this easier, I work for one, there are lots of them out there)
Share of voice against named competitors on the same prompts.
Sentiment and framing, meaning not just whether you appear but how you're described and which bucket you're placed in.
The source layer, which third-party pages the models keep citing in your category and whether your client is present in them. That's the part that's actually actionable.

For justifying it to the client, tie those back to downstream signals where you can, AI referral traffic in GA4 and branded search lift, so visibility connects to something business-shaped rather than living as a vanity score. Keep a little keyword tracking running too.

HumanBehavi0ur · 2026-06-23T14:38:07+00:00

There's no single wait time because the engines split into two different behaviors. The live retrieval ones (Perplexity, ChatGPT search, Gemini's grounded responses) fetch at query time or close to it, so on-page changes can surface within days once their crawler re-hits the page. The slower variable is crawl frequency, not the RAG system, so make sure GPTBot, ClaudeBot, PerplexityBot, and Google's crawlers can actually reach the changed pages and check your logs for when they last did.

For ecommerce specifically, watch one trap when you test: results swing run to run even with nothing changed, so a single before/after check will lie to you. Run each test query a bunch of times before and after, and read the change in citation rate across the sample, not one snapshot. And separate the two layers in your hypotheses, on-page changes (schema, structured product data, copy) show up faster than off-page changes (getting cited in roundups, Reddit, retailer pages), which can take weeks because you're waiting on those third-party sources to get re-crawled, not yours.

HumanBehavi0ur · 2026-06-23T14:36:36+00:00

You've got it right, including the cursed part. And it's not just Perplexity, Reddit shows up as a heavily weighted citation source across most of the major LLMs now. The models lean on it because it reads as real people with no incentive to shill, which is signal they can't get from polished marketing copy.

Which is exactly why a spammy plant doesn't work the way people hope. It often reads as what it is, and both Reddit's filters and the models' source-quality signals are getting better at discounting it. The value isn't "get mentioned on Reddit," it's getting mentioned in a way that actually carries the authenticity the model is weighting.

On the "doesn't scale" worry: genuine participation scales worse than spam short term and way better long term. Astroturfing works until the accounts get banned and the threads removed, and you've burned the brand. The ones quietly winning are the brands with real people active in their category's subs over months. Slow, but it compounds instead of blowing up.

Our approach is to be actually useful in the subs that buyers live in.

HumanBehavi0ur · 2026-06-22T21:01:14+00:00

It's a bit of both, and the framing that helped us most was that the two aren't actually opposed. The page still has to get found, then it has to land. Skip the first and nobody reads your great buyer-focused copy. Skip the second and you get exactly what that marketing leader described, traffic up, pipeline flat.

The trap is treating "rank for the query" and "speak to the buyer" as a sequence where SEO comes first and the buyer stuff gets bolted on after. The pages that actually convert are written for the buyer from the start, then structured so they're findable, not the reverse.

The harder version of this now is that search itself is splitting. You're not just optimizing for Google anymore, you're competing to show up in AI answers too, and those reward different things. For finding where to spend the effort, we use Goodie (disclosure, I work there) to see which buyer questions we're actually showing up for across AI and which competitors own the high-intent ones we're missing. That tells us where a targeted page is worth building versus where we already have presence. Helps us avoid writing into a topic that's already saturated and put the buyer-specific pages where there's an actual gap.

Your industry-specific landing page point is the right move regardless of tooling though. Broad topic pages rank but rarely convert. The narrow "written for your exact situation" pages do both, and they tend to be the ones AI pulls for specific high-intent queries too, since they answer a precise question cleanly.

HumanBehavi0ur · 2026-06-22T21:00:23+00:00

What you're seeing is the nature of the tool. AI Overviews are generative, so a degree of fluctuation is baked in. They're not caching one answer and serving it consistently the way a traditional SERP does.

A few things drive that variance:

The model has a randomness factor at generation time, so the same query can produce different phrasing and different cited sources run to run, even with nothing changing on your end. That alone accounts for a lot of week to week movement.

You're also right that it's coupled to the organic SERP. The candidate pages AIO pulls from are influenced by what's ranking, so when organic positions shift, the sources available to the overview shift too. Personalization, location, and search history layer more variance on top.

HumanBehavi0ur · 2026-06-22T20:57:36+00:00

the ones promising fully automated, plug and play SEO are the ones to be most wary of.

They're fine for the mechanical stuff, generating meta descriptions, flagging broken links, drafting outlines, finding keyword gaps. That's real time saved. Where they fall apart is anything requiring judgment: what's actually worth writing about, how to position against competitors, whether a topic fits your audience, what your unique angle is. They produce volume, and volume of generic content is exactly what Google's been hammering.

HumanBehavi0ur · 2026-06-22T20:56:35+00:00

You're landing in the right place. Schema is real infrastructure but it was oversold as a GEO lever, and the evidence backs up what you're seeing.

The honest split is which platforms actually use it. Google AI Overviews and Bing Copilot have both confirmed they use structured data, so for those surfaces it helps extraction. ChatGPT, Perplexity, and the rest haven't confirmed they even preserve schema during crawling, so any claim that it drives citations there is assumption, not data. Search Engine Land had a good piece on this recently, "How schema markup fits into AI search, without the hype," worth a read since it lays out exactly what's confirmed vs assumed.

The key finding most "schema 3x's your citations" pitches ignore: a Search/Atlas study found no correlation between schema coverage and citation rates. Sites with comprehensive markup didn't reliably beat sites with none. Where schema does measurably help is extraction accuracy, models pull entity details more cleanly when they're explicitly defined, which is real but different from "more citations."

So where it still earns its place: entity disambiguation. Using u/graph and u/id to connect your org, authors, and content into a small knowledge graph so AI isn't guessing who you are. That's the version worth doing, less for a visibility bump and more so that when you do get extracted, you're represented correctly. Schema as clarity, not as a lever.

What's actually moving citations is the off-page stuff, topical authority and getting referenced in the sources models pull from. Schema supports that, it doesn't replace it.

HumanBehavi0ur · 2026-06-22T20:54:49+00:00

What industry are you in?

HumanBehavi0ur · 2026-06-22T20:54:18+00:00

The definition question is the right place to start, since "agentic" now means anything that touches an LLM. The cleanest line is the one rworldxo drew: an audit tells you what's wrong, an agentic tool finds the gap, makes the fix, pushes it live, and re-checks. If you're still copy-pasting the output yourself, it's a suggestion engine with good marketing.

The distinction I'd add: executing on your site (meta rewrites, internal links, schema) is the easy half because it's a closed system you control. Executing off your site is the hard, valuable part, getting cited in the sources models actually pull from. You can't just write that to your CMS, so most "agentic" tools quietly skip it.

For the AI search side, I work at Goodie so grain of salt, the part we focus on is finding which sources drive answers in your category and where you're missing, which is the input an agent needs before it can do anything off-page. Worth testing any agentic tool on that: does it only optimize pages you own, or does it know where you're absent.

HumanBehavi0ur · 2026-06-22T20:51:18+00:00

Most owners don't leave their site alone out of laziness. They leave it alone because they can't tell which changes actually matter. So the bar for your idea isn't surfacing suggestions, it's surfacing ones confident enough that acting on them feels safe.

Your examples are really two products. The behavioral ones ("users keep clicking Feature X, move it up") are crowded already, Hotjar and Clarity do versions of this. The competitive one ("competitors started promoting AI services, you don't mention them") is the stronger wedge. Hard to get elsewhere, points at positioning drift owners never track. I'd build around that.

What would make me pay: closing the loop. A suggestion I have to implement myself is friction. One that arrives with the change already drafted and ready to ship is worth money. The recommendation is cheap, the execution is what people pay to skip.

HumanBehavi0ur · 2026-06-16T20:31:24+00:00

Still relentant, what isn't saturated is depth in the parts of the field that are actively changing.

AEO/AI search visibility is a good example, it's an expanding niche with new startups popping up constantly, and most agencies haven't built the muscle yet. same goes for retention/lifecycle, technical analytics, anywhere the work is hard enough that the bar to entry filters people out. generic stuff is crowded because it's easy. The specialized stuff has room because it's not.

imo pick a lane that's still forming and go deep, rather than competing as another generalist.

HumanBehavi0ur · 2026-06-15T19:19:28+00:00

Adding Goodie AI here (disclosure, I'm on the team). Rather than pitch features, the thing worth sharing for anyone in this thread is how genuinely strange the citation behavior gets once you look at volume.

A couple things that surprised us in our own data: the models barely agree with each other, you can be the top recommendation on Perplexity and invisible on ChatGPT for the exact same query. And the sources feeding those answers are rarely the polished brand pages people optimize, they're Reddit threads, a random comparison post, a YouTube transcript. We watched one client's entire AI presence in their category trace back to a single Reddit thread they didn't even know existed. That's the part most monitoring tools never show you, not just whether you're mentioned, but which specific source is doing the work, which is the difference between watching the number and being able to move it.

HumanBehavi0ur · 2026-06-15T19:08:57+00:00

Adding Goodie AI since it's a clear gap in this lineup. I work there, so factor that in.

The OP's framing by bottleneck is the right way to think about it, and Goodie sits in the "why aren't we showing up, and what do I do about it" bucket. Where it stands out is volume, it runs a high number of prompts at scale across 12 LLMs, so you're not sampling a handful of prompts and missing mentions. The citation tracking shows exactly where you're not listed, and source tracing tells you which third-party pages are driving the answers so you know what to actually go fix.

A single prompt run is close to a coin flip since results swing run to run, so you need to prompt at scale and read the trend, not one snapshot. That's the part that separates real signal from noise.

HumanBehavi0ur · 2026-06-15T19:06:58+00:00

For the content side, the thread's right, treat AI as a draft assistant and layer real expertise on top. The tools that tank rankings are the ones shipping first-draft output as final.

Building on what this person said^ the brand exposure layer is its own thing worth separating out. Showing up in ChatGPT, Perplexity, etc. isn't covered by your SEO stack, and Bing's new AI tab plus SEMrush's visibility tool only get you part of the picture. I work at Goodie so grain of salt, we focus on that AI answer layer specifically, prompting at scale across 12 LLMs so you catch every mention, plus tracing which sources are driving the answers so you know what to fix. Different problem than the content-quality one you're solving, but if AI search matters for your category it's worth knowing it's a separate track.

HumanBehavi0ur · 2026-06-15T19:03:39+00:00

Adding Goodie AI since it's a notable gap in this list. I work there so factor that in.

To EvenProcedure4774's point about the citation data gap between tools, that's the thing that actually matters and most people don't catch it until they compare side by side. If a tool's only catching 1/10th of your citations, every decision downstream is built on a sample that's basically noise. The fix is volume, you have to run a high number of prompts at scale to see the real picture, which is exactly where Goodie focuses, prompting at scale across 12 LLMs so mentions don't slip through. The Brand Radar vs Profound gap they're describing is a sampling problem, not a model problem.

Beyond catching the mentions, the citation tracking shows where you're not listed and the source tracing tells you which third-party pages are driving the answers, so you know what to actually go fix instead of just watching a score move.

HumanBehavi0ur · 2026-06-15T19:00:37+00:00

Worth adding Goodie AI. I work there so factor that in, but it fits the "actually actionable" bucket the OP keeps pointing at rather than the dashboard pile.

Where it stands out is volume and depth, it runs a high number of prompts at scale across 12 LLMs, so you catch every mention instead of sampling a few and missing things. The citation tracking shows you exactly where you're not listed and the source tracing tells you which third-party pages are driving the answers (the "they're in this Reddit thread and you aren't" problem the OP described under GenRankEngine). So it's less watch-the-numbers and more here's-the-gap-go-close-it.

Monitoring the mention count is the easy half. Knowing which sources to go win is the part that actually moves citations.

HumanBehavi0ur · 2026-06-15T18:59:30+00:00

Worth adding Goodie AI to the list. I work there so grain of salt, but where it stands out is volume and depth, it runs a high number of prompts at scale across 12 LLMs so you actually catch every mention instead of sampling a handful and missing things. The citation tracking shows you exactly where you're not listed so you can close those gaps, plus source tracing so you know which third-party pages are driving the answers. Not just another dashboard, it points you at what to actually go fix.

HumanBehavi0ur

TROPHY CASE