Solkendo is a free tool I built. You paste your store URL and in ~60 seconds you get a 0 to 100 AI visibility score plus your 3 biggest gaps. No signup. It checks whether AI shopping assistants (ChatGPT, Perplexity, Google AI) can actually read and recommend your store: product schema, machine-readable prices, AI crawler access, and more. I run my own store and built this after realizing I had no idea whether AI could even see it, now that shoppers search for products there. Looking for honest feedback from fellow merchants: Is the landing page clear? Try the scan on your own store and tell me if the score and the gaps it finds actually match your reality. Brutally honest feedback very welcome.
What feedback do you want?
Two things: (1) Is the landing page clear about what the tool does within the first few seconds? (2) After you scan your own store, does the AI visibility score and the 3 gaps it reports actually match your store’s reality, or does anything look wrong?
You’ve made a nice tool for AI visibility reports. Thank you for keeping it free for now. It’s interesting how the tool is specifically oriented on Shopify.
Will it evolve into something like Peec, Profound, and Beamtrace that can also track your mentions in LLMs dynamically and make content suggestions? Maybe an ability to see prompts your competitors rank with?
Or are you planning to add other models like Gemini, Claude, and Perplexity to check visibiity there? I’ve read they scrape differently, so results shall vary.
Whatever you choose, good luck and have fun developing the tool!
Thank you — and great questions, you clearly know the space. Short answer: yes to dynamic tracking and more models (Perplexity, Gemini, and others) — that’s the next phase. But I’m deliberately coming at it from a different angle than Peec/Profound: instead of enterprise brand monitoring, I’m starting with the Shopify SMB long-tail — free scan first, then monitoring, then actually fixing the gaps, not just reporting them. You’re right that each model scrapes and answers differently, so monitoring will query several and average with a confidence range rather than treating any single answer as truth. Seeing which prompts surface competitors is exactly the kind of thing I want to show. Really appreciate the thoughtful comment!
Thanks for your replies. The ability to fix the gaps will make it wholesome, and deep focus on Shopify stores can be quite a unique thing at the current market of these tools. Good luck further - would be excited to see updates in this or new threads to keep up with your progress (of course, if you feel like sharing it again).
@Solkendo ,
I tried the site and the concept is genuinely useful especially with AI driven search becoming more important. The landing page communicates the purpose fairly quickly but I think you could make the value proposition even stronger by showing what merchants can gain more AI visibility, recommendations, and organic traffic rather than focusing mainly on the score itself.
Add a sample report or screenshot above the fold so visitors immediately know what they’ll receive.
Briefly explain how the score is calculated to build confidence in the results.
Include a couple of real-world examples or testimonials showing how merchants improved their score and what impact it had.
After the scan, prioritize the recommendations by impact and provide links to resources explaining how to fix each issue.
Overall its a promising tool. The clearer you make the connection between the score and tangible business outcomes the more valuable it will feel to merchants.
Thank you — this is incredibly useful, some of the clearest feedback I’ve gotten. You’re right that the score is a means, not the end; I’ll sharpen the page toward the actual outcome (getting recommended by AI, and the traffic that follows). A sample report above the fold and a short “how the score works” explainer are going straight on the list, and I love the idea of prioritizing fixes by impact with a link to a how-to for each. Testimonials are coming — I’m collecting real before/after results now. Really appreciate you taking the time.
One thing I’d change is separating technical accessibility from recommendation likelihood. Valid schema and crawler access mean a model can read the store, not that it will recommend it.
A few practical improvements:
Show two subscores, crawlability and recommendation readiness, instead of relying on one 0 to 100 number.
List every check behind the score, its weight, the page tested, and the scan timestamp. That makes false positives easier to spot.
Scan at least one product page and one collection page, not only the homepage. Price, availability, variants, shipping, and returns often differ there.
Add a retest button beside each fix so merchants can confirm the change today.
I’d also avoid tying score gains directly to traffic until you have measured before-and-after cases. Position the score as a technical diagnostic, not a promise of AI mentions.
This is the sharpest feedback I’ve had — thank you. You’re exactly right: crawlability and schema mean a model can read the store, not that it will recommend it, and I shouldn’t blur those. Splitting into two subscores (crawlability vs recommendation-readiness) is a great way to make that honest, and I’m going to do it. Same for scanning product + collection pages, not just the homepage, and showing every check with its weight, the page tested, and a timestamp — transparency should make false positives easy to catch. And I take the positioning point: I’ll frame the score as a technical diagnostic, not a promise of AI mentions, until I have real before/after data. Would you be open to me following up as I build this out? Your input would be genuinely valuable.
Thank you — this is really helpful. You’ve confirmed something a few people have now said: for non-technical merchants, “what to fix next” matters more than the number itself, so I’m moving the fix guidance to the front and adding a sample report to the hero so you see what you’ll get before scanning. The consistency-across-store-types point is well taken too — I’m expanding scans beyond the homepage to product and collection pages so the reported gaps hold up on large and heavily customized stores. Appreciate you taking the time.
I think those changes will make a noticeable difference, especially showing a sample report before the scan. Setting clear expectations upfront helps merchants understand the value immediately.
Expanding the analysis beyond the homepage also sounds like the right direction. Many Shopify stores rely heavily on their product and collection pages for organic traffic, so checking those should make the results much more representative of a store’s overall AI visibility.
Looking forward to seeing how the tool evolves. Wishing you the best with the project!
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I like the concept because it’s solving a question a lot of merchants are starting to ask: “Can AI actually find and understand my store?” That’s becoming more relevant as shoppers use tools like ChatGPT and Perplexity for product discovery.
Looking at your post, I think landing page should answer one question almost immediately: “What does this score actually mean for my business?” A72 vs. 90 is only meaning full if merchants understand how that affects their visibility and what improvements are likely to have the biggest impact.
I’d also make sure reported gap is accompanied by a short explanation of Why it matters and how to fix it. For merchants who aren’t familiar with structured data or AI crawlers, actionable guidance is often more valuable than the score itself.
Overall, I think the idea is useful. As more merchants start thinking about AI-driven discovery, having a simple way to identify technical issues could be helpful. I’d be interested to see how the scoring evolves as AI shopping experiences continue to mature.
Thanks — this lines up exactly with what a few others have said, and it’s shaping the next update: the score is becoming secondary to “here’s what to fix, why it matters, and how,” right up front. I’m also making the hero answer “what does this mean for my store” before you even scan. Appreciate you taking the time.