Looking for blunt feedback on a small AI brand-visibility tool
Solo founder building under HanzDevCraft. Right now I’m focused on AI Visibility Monitor — repeatable checks of how AI assistants talk about a brand. Also shipping QATestFlow for simple site/QA checks. Looking for blunt product feedback, not a pitch.
Built a small tool and want honest opinions.
One check across 5 AI assistants: what they say about a brand — score, sentiment, competitors, saved answers. Free: 1 brand + 5 checks, no card. Then monthly plans: Starter €39 (30 checks / 2 brands), Pro €99 (100 / 5), Business €199 (300 checks). Point isn’t “ask ChatGPT once” — it’s repeatable checks with a report so you don’t re-guess every week.
Useful for SMB / agency work, or noise? What would you add first, and what should I drop? Roast welcome.
I ran it through the grader i'm building: 68, Cooked.
the thing costing you most: there's no screenshot of the product anywhere on the page. someone deciding on €39 has to leave to find out if the dashboard is real or a text list. one image of the score + sentiment view in "Read the signal" fixes that.
second one: your best line, "AI assistants answer buyer questions without a classic SERP", sits below the solution. it's the problem, put it first.
Full breakdown, every point tied to a public test: launchgrill.com/g/n2ja7nux?src=ih
it's a public page, say the word and i'll take it down. you can claim it and re-grill after you change things 👌
Thanks, both points are fair. A real dashboard screenshot and the problem line first are going on the list. Fine to keep the page up.
Interesting idea. The biggest thing I’d want to understand is how you make the results trustworthy. AI answers can change depending on the model, prompt, or even repeated runs, so showing variance, exact prompts, and evidence behind the scores would add a lot of value.
I’d also focus less on a general “brand visibility” score and more on what users can actually do with the data like seeing where competitors are gaining visibility, what changed over time, and which topics they should create content around. The before/after changes could be especially useful.
Thanks, this is really useful. You're right that a single run isn't enough. We show the exact prompts and raw answers, and we're adding repeated runs with variance, competitor comparison, week-over-week history with before/after deltas, and concrete topic suggestions. Trust and actionability are exactly where we're heading.
Love this angle. Building Xstream4K right now so this hits close to home — what made you look into it in the first place?
Thanks! It started with a simple worry: people now ask ChatGPT or other AI assistants who to hire, and a small business has no idea whether it gets named or its competitor does. Google still matters, but it isn't the only place people get recommendations anymore. So we built a way to check that yourself, with the raw answers from 5 models instead of just a score. Good luck with Xstream4K.
Blunt feedback: the core idea is solid — brand visibility is genuinely hard to track and most tools give vanity metrics. The challenge will be proving causation vs. correlation (did visibility actually lead to pipeline?). Show before/after examples with real data from beta users. If you can demonstrate a clear link between AI mentions and actual inbound leads, that's your whole pitch. What does the output look like right now?
Fair point — causality vs correlation is the hard part. Right now we ship the visibility layer (score, sentiment, competitors, saved answers), not a proven AI-mention→leads link. No beta before/after cases yet; I won’t invent them. A live check shows the current report shape — happy to walk through it.
The score-and-competitor view is useful, but the harder problem for a niche product is figuring out why an assistant recommends someone else instead of you. A score alone says you're behind, not why: maybe nothing about your use case is indexed anywhere an assistant reads, maybe there's no review or comparison for it to point to, or maybe it just defaults to whichever name shows up most regardless of fit. For something like a time-tracking app built around billable client calls specifically, rather than time tracking in general, that distinction matters more than the raw number. Does the tool break down the cause, or is it purely comparative right now?
Fair roast. Right now it’s mostly comparative: score, sentiment, competitors, saved answers. It does not yet break down why an assistant picks someone else — e.g. “no review/comparison indexed,” “generic category default,” vs “your niche use-case isn’t represented.”
That’s the next layer I want: cause labels on top of the score, not just “you’re behind.” Curious which cause you’d pay for first if you had to pick one: missing source coverage, weak differentiation language, or competitor density?
Repeat checks are more interesting than one-off prompts. Have any users actually changed content, positioning, or spend because of what the reports showed?
Honest answer: too early for that proof. I’m still collecting blunt feedback and early checks; I don’t have a clean case yet of “report → they changed positioning/spend → measured lift.”
What I do have is the workflow hypothesis: repeatable checks so you’re not guessing weekly. If you’ve seen teams act on AI-visibility reports, what actually moved first for them — page copy, comparison pages, or paid?
The missing report → decision → outcome case is the key gap I’d be interested in discussing. If you’re open to it, what’s the best email to reach you on?
Sorry for the slow reply — I was tied up and only just catching up on the thread.
Happy to dig into that over email — info@hanzdevcraft.net. Today we give the data layer (score, sentiment, competitors, history) so you can decide; we don’t have a published report→spend/outcome case yet, and I won’t invent one.
(If you email, subject “IH — report → decision → outcome” helps me spot it.)
Thanks! I’ve just sent it over.
Looking forward to hearing your thoughts whenever you have a chance.