Orbator
Get recommended by ChatGPT, Claude, Grok & Gemini
I've been building Orbator for SaaS founders, product teams, and local business owners who want visibility in AI assistant responses, plus agencies managing multiple products across clients. Anyone optimizing for discovery through ChatGPT, Claude, and similar tools rather than traditional search.. Product teams submit to directories hoping to get recommended, but they never know if AI assistants actually see or cite them. Orbator shows you whether ChatGPT, Claude, Gemini, Perplexity, and Grok recommend your product right now, identifies the exact sources they read to decide, and places your listings on those sources with weekly verification so you stay in the answer.
Still early and looking for honest feedback.
19 Comments
The invisible search problem. Every founder knows Google's distribution rules, but AI recommendations work the same way - except nobody knows what ChatGPT is actually reading or recommending them for. You've built the transparency layer that was completely missing. The real power here isn't just knowing if you're recommended - it's understanding which sources matter. That's the signal that lets founders make decisions instead of guessing. Traditional directories let you submit and hope. Orbator flips it - you submit, verify, and iterate based on actual recommendation data. That's the feedback loop that makes distribution work.
Thanks Omri, that's a better summary of the thesis than most of my own landing page copy. The part that surprised me building it is that the source layer isn't even one layer. Each engine reads a noticeably different set for the same category, so "which sources matter" has five answers, and they drift. That's what pushed us from a one time audit to weekly verification, the map you act on this month is stale by next month. If you ever want to see the source map for your own category, the free check spits it out in about a minute.
The monitoring half and the listings half of this age very differently. Tracking whether models cite you gets more valuable every quarter, but paying to get placed on the sources models read is link building with a new name, and the labs will discount those sources the same way Google eventually discounted paid directories. I would also lead with variance instead of a yes or no, because the answer shifts across phrasings and sessions, and a founder who sees 'you appear in 3 of 20 phrasings' learns something actionable where 'not recommended' just makes them close the tab.
This is great critique! thank you! Mostly I agree. If we were selling paid placements I'd agree completely, that playbook dies the same death paid directories died with Google. But most of what the engines read for a given category is stuff you can be present in for free, directories, review profiles, communities, and our report now explicitly separates those from editorial coverage you'd have to earn. When a lab starts discounting a source, the weekly measurement shows it, the citations move, and the placement targets move with them. So I'd put it this way, the listings half is perishable by design and the monitoring half is what tells you when it perished. That asymmetry you called out is real and it's why measurement leads the product.
On variance, you're right and I'm taking the framing. The free check is deliberately one phrasing per engine to keep it cheap. The paid tracking runs a set of buyer questions per product, and alongside the questions a founder picks we also run our own standardized prompts for each category, independent of any customer. Partly because founders instinctively choose phrasings that flatter them, partly because that neutral battery is what builds our cross-category index over time. That second stream is where the variance you're describing shows up clearest, the same product appears in some phrasings and not others, and "you appear in 3 of 20 phrasings" is a better way to surface it than any binary verdict. I just reworked the report to stop overclaiming in the other direction, mentioned is not recommended. Appearing in N of M is the natural next step on top. Genuinely useful comment, thanks for taking the time.
Good sign that you shipped the framing change instead of just agreeing with it in the comments. Most founders nod along to feedback like this and never touch the product copy. The N of M approach also ages better than a binary verdict, it stays true even as citations shift week to week instead of breaking the first time one drops.
This is a really interesting concept. As someone running an ecommerce brand, I've been wondering how AI assistants decide which products or websites to recommend. Traditional SEO only tells part of the story, so having visibility into what AI actually sees could be genuinely useful. Looking forward to seeing how Orbator evolves!
Thanks, and ecommerce is honestly where this gets most interesting. When someone asks ChatGPT for the best product in your niche, the engine forms that answer by reading a specific set of sources, review sites, listicles, communities, and they're different for every category. The report shows you exactly which sources your engines read and whether you're in them, which is the part traditional SEO never surfaces. If you want, run the free check with your store's domain, it takes a minute and you'll see the source list for your own category. Would love to hear if it matches where you thought your visibility came from.
That makes sense. I’m particularly curious to see what comes up for a niche like modest fashion (alabayae) i've build ecom store, since I’d expect the sources to be quite different from the usual ecommerce SEO sites. I’ll run the check for my store and compare the results with the sites I’m currently focusing on.
"This is a smart angle — getting found where AI is already looking. I'm building Rallynex and hadn't thought about that channel.
What's been the most surprising thing you've learned since launching Orbator?"
Thanks. A couple things surprised me. The first is how little the engines agree with each other. We measured recommendation overlap across ChatGPT, Claude, Gemini, Perplexity and Grok, and they agree on what to recommend only about 16 percent of the time. Everyone assumes there's one AI answer out there. There isn't, there are five different ones, and being in one says nothing about the other four. The second is who writes the sources they read. We analyzed 143000 citations and close to half point to content the vendors themselves own. The input to AI recommendations is much more shapeable than people assume, which is both the problem and the opportunity. Happy to run Rallynex through the free check if you're curious what the engines say about your category, takes a minute.
"16% overlap is wild. I assumed there'd be more consensus, but it makes sense — each engine has its own bias and data sources.
The fact that nearly half the citations come from content vendors themselves is interesting. Means there's a real opportunity to shape what gets recommended.
I might take you up on that free check. Curious what the engines say about Rallynex. What do you need from me?"
Nothing needed, I ran it this morning. Rallynex turns out to be a live example of the 16 percent problem. Three of the five engines recommend you. ChatGPT calls you best for founders building in public, Gemini gives an explicit yes, and Grok includes you. But Claude declines, its stated reason was that there's no independent coverage to judge you on yet, and Perplexity recommends Contentful and others instead. Same product, same morning, a 3 to 2 split.
The source side explains the split. The engines read almost nothing third party about you, a couple cited rallynex.com itself and the rest was scattered editorial. The two holdouts happen to be the engines that lean hardest on independent sources, which matches Claude saying so in its own answer. So the fix list is short, get a few independent listings and pieces of coverage the engines can read, and the holdouts finally have something to judge you on.
If you want the hosted report with the full source map, run the check at orbator.io with your email and it generates it in about a minute. And honestly, three engines recommending you this early is ahead of most products I scan. Nice position to defend.
Which part of Orbator has been hardest to personalize for each founder? I’m curious whether users spend more time editing the AI output or choosing which marketing task to run next.
The core output is a measurement, not copy. We ask the engines the questions your buyers ask and show who gets recommended, so there is nothing to edit there.
The hardest part to personalize is exactly those questions. If a tracked prompt contains your product name it measures nothing, a forced mention is not a recommendation. So we have to generate what a stranger in your niche would actually ask, like "best scheduling tool for gyms", and getting those specific-but-neutral per founder took more iteration than anything else in the product. We even had to block users from adding their own name to a prompt, because everyone instinctively does it.
So to your either/or: time goes to a third thing, the fix list. Once you see which sources each engine reads before answering, the work is getting listed on those. The submission part we've made mostly one-click, but deciding which surfaces are worth it for your niche is still the founder's judgment call. Which part were you expecting to be the bottleneck?
The measurement-to-action loop is the interesting part here.
With the early users you've had so far, are they coming primarily because they want to understand where their AI visibility is coming from, or because they already want Orbator to improve it for them?
Almost everyone comes for the diagnosis. Most founders have never seen their product through the engines' eyes, so "am I in the answers" is the hook. What keeps them around is that answers don't sit still. Watching the same buyer questions over months, we see engines swap who they recommend as the sources they read change. A clean result today says nothing about next month. Once people see that drift they stop treating AI visibility as a one time audit and treat it like uptime, something you watch weekly and act on. So measurement is the door. The recheck plus the action loop is the product.
That’s helpful context. The distinction between the initial diagnosis and what keeps users engaged afterward is interesting.
I’d like to continue the conversation outside the thread. What’s the best email to reach you on?
I've tried it with Firmgrove and we are in the answers. At first glance if I'm in the answers doesn't look like that Orbator can give me value.
I've asked for the full report too and it's showing me agencies or blog sites, so it's not really useful for me because we can't list our company on those sites.
What about if Orbator can offer improvements so Firmgrove can appear in more recommendations?
Thanks for actually running it and for the honest pushback. This is exactly the feedback I needed, and I spent today shipping fixes because of it.
First, on being in the answers. I pulled up your scan. The engines named Firmgrove because the check asks about firmgrove . com directly, but when they were asked if they would recommend it, none of them said yes at the time. They pointed buyers to Linear and Notion instead. Being named when someone asks about you is not the same as being the answer when a buyer asks what the best startup management software is. The old report made that far too easy to misread, so that one is on me. The report now leads with the real verdict instead of a surfaced count.
Second, the sources. You were right, a list of blogs and agencies that all carry the same submit link is useless. The report now separates directories you can actually get listed on from editorial coverage you would have to pitch. Appvizer was in your list, it takes vendor listings, and it is now in the submittable set.
And your case got more interesting. Your first scan ran while our ChatGPT engine was down. I reran your report on all five engines and ChatGPT now recommends Firmgrove, while Perplexity and Grok still point buyers elsewhere and Claude and Gemini name you without recommending you. Engines disagree and answers shift week to week, which is exactly why the product rechecks weekly and alerts you when you drop out. Your original report link now shows the fresh version. Would genuinely value a second look, and I would like to hear if it lands differently now.