A few weeks into building this, I ran my own tool on a tester's site — a plumber — and proudly showed him the content plan it spat out. He read it ...
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This hits the nail on the head. AI made generation effortless, but targeting the right intent and finding a genuine niche is the real bottleneck. Kudos for building a tool to tackle the actual problem instead of just adding to the noise!
Not a developer, this just showed up in my feed, but the "what do I even write about" thing is my whole life so I clicked. Ran the free report on my pilates studio site expecting a shrug and it kind of stung — asked the way a client would and I wasn't in a single answer, three studios I've never heard of were. The gaps it listed actually made sense, which I wasn't expecting from an AI thing. One bit of honest feedback: it gave me the diagnosis but I stared at it not knowing which fix to do first. A "start here, do this one" would go a long way for people like me who don't speak SEO.
This is the most useful comment I could've gotten, thank you — and a little painful because you've named the exact thing I keep under-building. You're completely right: a list of gaps isn't help if you don't know which one to touch first. Right now the plan does prioritize under the hood, but I'm clearly not surfacing it as a plain "do this one first" the way a non-SEO person needs. That's going straight to the top of my list. Quick question if you're up for it, since you're precisely who I'm building for: would you rather it just picked the single highest-impact thing and told you to start there, or show you the ranked list but make #1 obvious? I've been guessing at that and I'd rather hear it from you than guess.
Really interesting approach! I think the hardest part of content creation is often not the writing itself, but figuring out what’s actually worth writing about and how to stay consistent.
I especially like the idea of turning content strategy into something more structured instead of relying on random inspiration. It feels like a practical solution to a problem a lot of developers and creators face.
Great work, and congrats on building and sharing it!
Thanks — and you said it better than I did. "Structured instead of random inspiration" is exactly the reframe I was reaching for. The consistency angle you mention is the sneaky-hard half too: most people don't stop publishing because they run out of skill, they stop because deciding what's next every single time is exhausting, and a plan quietly removes that decision. Curious whether that matches your own experience — is it the deciding or the doing that usually breaks your consistency? Appreciate you taking the time to read it.
Great insight. I really like the shift from "AI can write" to "AI needs the right strategy." The plumber example made the problem instantly clear. Looking forward to seeing how this evolves—especially the AI visibility side. Best of luck with the beta!
Thanks, that means a lot. And you picked the exact part I'm most obsessed with — the AI-visibility side is moving fastest and it's where the surprises keep coming (watching a business rank fine on Google but be completely invisible in ChatGPT never stops being weird). I'll probably write up what I learn there as it develops. Appreciate you reading it properly.
The cannibalization piece is the part I'd poke at. Semantic similarity and search-intent overlap aren't the same animal — two pages can sit almost on top of each other in embedding space and still rank for completely different queries, and you'll also miss pairs that are worded differently but chase the identical intent. Did you validate the clusters against actual Search Console data (which of your URLs surface for the same queries)? I think I'd start from GSC query overlap as the ground truth and use embeddings as a secondary signal, not the primary one. What did your false-positive rate look like in practice — how often did it flag a "merge" that a human immediately overruled?
This is the sharpest version of the critique and you're right that I conflated two things early on. Semantic similarity was my v1 primary signal and it burned me exactly how you'd predict — pages that looked near-identical in embedding space but ranked for genuinely different queries kept getting flagged as merge candidates. Where I've landed matches your instinct: GSC query overlap is the stronger ground truth, so it's now the primary signal and embeddings are the secondary "these might be related, go check" pass. The honest catch is that new or tiny sites often have thin-to-no Search Console data, and that's exactly the audience I'm building for — so for them I'm still leaning on embeddings more than I'd like, which is where most of my remaining false positives live. I don't have a clean false-positive number I'd stand behind publicly yet; it was low enough on the GSC-backed sites to be useful and clearly worse on the data-poor ones. If you've dealt with the cold-start version of this problem I'd take any pointer you've got.
On your second question, since you asked for it straight: I've watched a lot of "new channels" get oversold — everyone tuning for featured snippets a few years back, and how much of that survived? "Get cited by the LLM" has a similar smell. I don't think it's nothing, but my worry is durability. The models retrain, the retrieval layer shifts, and whatever earns a citation this quarter can quietly stop next quarter, and you don't own any of those levers. Building a product whose headline value is a moving target someone else controls makes me nervous. How are you thinking about that risk? And when you measure visibility — is it a fixed prompt set tracked over time, or more of a spot-check? Because those two tell very different stories.
The featured-snippet comparison is fair and it's the risk that keeps me up, so I won't hand-wave it. My honest position: I'm not betting the product on "gaming the citation." The moving target you're describing is real and I don't own any of those levers, you're right. What I'm betting on is that the inputs the models seem to reward — clear structure, content that actually answers the question, consistent business data, real authority signals — are the same fundamentals that survive a retrain, because they're roughly what good SEO already wanted. So if the citation channel evaporates tomorrow, the work still produced better content and better Google visibility; the AI-visibility part is upside, not the whole thesis. That framing might be cope, and I'm genuinely open to it being cope. On measurement: it's a fixed prompt set tracked over time, not a spot-check — same queries, same cadence, so the trend is comparable — but I'll be honest that the prompt set is my best guess at how a real customer would ask, which is its own soft spot. If you think there's a more defensible way to define that set, I'm all ears, because you clearly know this terrain better than I do.
ngl the part about the plumber site is so real. tool works great until you actually see how the target audience reads it lol
exactly. the demo always looks great because you're grading your own homework. did the "any plumber" thing ever bite you on something you shipped? feels like everyone has one of these stories.
This was a great read, and I really appreciate how honest you are about the rough parts, that kind of builds trust which you don't see often. The bit about whether ChatGPT actually recommends you is something I'd genuinely never thought about, and now I can't stop wondering how many businesses are sitting there invisible without even realizing it. The idea that a small site can quietly end up with its own pages competing against each other was also new to me, I'd never have thought to look for that. Rooting for you on the beta, I'll give it a go!
This really resonated with me because it highlights a problem most AI writing tools don't solve. Writing the content is the easy part now—figuring out what's actually worth writing is where people get stuck. I like that you focused on the strategy layer instead of just generating more text. Building something to remove that decision fatigue feels much more valuable in the long run. Looking forward to seeing how the platform evolves. Great work!
The plumber test is the best diagnostic I have seen for this problem, and it applies way beyond content strategy. A professional profile that says experienced full-stack developer is the exact same generic mush. It could be for literally any developer. The fix you found, that grounded inputs beat clever prompts every time, is the same principle I built Opportunity Skill's impression management around. The agent does not ask you to write a bio. It observes what you actually reject, what you consistently insist on, and how you collaborate, then distils that into structured impressions. The result is specific enough that another agent searching for a backend engineer who refuses daily stand-ups and only works with technical founders can actually match on those dimensions. Generic signal produces generic matches. Grounded signal produces precision.
The plumber example was actually my favorite part because it's such an obvious thing in hindsight 😅 If the output could apply to literally any plumber, then it's not really helping that business make decisions. The AI visibility part was interesting too. I keep wondering whether being cited by chatgpt and similar tools is going to become its own form of SEO or if we're all overestimating how stable these systems will be...
ngl the plumber example is a reality check, most people just ignore the actual niche needs when building ai tools
that plumber example is a mood lol, honestly the hardest part is always the actual planning not the drafting