Companies are completely invisible or unrecognized when users query AI models for solutions in their category, and slight variations in how queries are phrased cause unpredictable, massive shifts in recommendations with zero model-specific tracking.
Companies and product creators are completely invisible in AI model search recommendations despite being relevant, and they lack clear visibility into how specific prompts and different models influence their visibility.
I built a tool that shows which companies AI actually recommends in your category. It went live on Product Hunt this morning.
"We asked few models about our service and got nothing, not even wrong info just completely blank. Like we don't exist at all"
commentThis is super interesting, I had similar experience with my company but on smaller scale. We asked few models about our service and got nothing, not even wrong info just completely blank. Like we don't exist at all The question shaping results part is wild, 4 to 29 mentions just by changing how you ask? Makes me think lot of companies are optimizing for wrong keywords in AI context Per-model breakdown definitely more useful than single score, you cant fix what you cant see. If ChatGPT gives zero and Claude gives 10 that tells very different story than "average 5" I run few tests after reading this and already found one competitor mentioned 8 times on Gemini but never on others, they probably trained on some specific dataset
"The question shaping results part is wild, 4 to 29 mentions just by changing how you ask?"
commentThis is super interesting, I had similar experience with my company but on smaller scale. We asked few models about our service and got nothing, not even wrong info just completely blank. Like we don't exist at all The question shaping results part is wild, 4 to 29 mentions just by changing how you ask? Makes me think lot of companies are optimizing for wrong keywords in AI context Per-model breakdown definitely more useful than single score, you cant fix what you cant see. If ChatGPT gives zero and Claude gives 10 that tells very different story than "average 5" I run few tests after reading this and already found one competitor mentioned 8 times on Gemini but never on others, they probably trained on some specific dataset
"Per-model breakdown definitely more useful than single score, you cant fix what you cant see."
commentThis is super interesting, I had similar experience with my company but on smaller scale. We asked few models about our service and got nothing, not even wrong info just completely blank. Like we don't exist at all The question shaping results part is wild, 4 to 29 mentions just by changing how you ask? Makes me think lot of companies are optimizing for wrong keywords in AI context Per-model breakdown definitely more useful than single score, you cant fix what you cant see. If ChatGPT gives zero and Claude gives 10 that tells very different story than "average 5" I run few tests after reading this and already found one competitor mentioned 8 times on Gemini but never on others, they probably trained on some specific dataset
Founders and marketing leads of SaaS companies running product categories who want to ensure their brand is actively recommended when users search within LLM tools.
Repeated complaints focus heavily on complete invisibility during baseline categorization queries, alongside radical unpredictability driven by tiny query alterations.
Unlike generic SEO tools or single blended AI optimization scores, this tool explicitly isolates how minor prompt-shaping and query variations alter visibility per model, letting users see exactly what they can fix.
An LLM monitoring dashboard that automates querying across all major AI models (ChatGPT, Claude, Perplexity, Gemini) with query-variation matrix testing, providing per-model visibility scores and alerting teams when prompt variations drop their product from recommendations.
Users express extreme frustration over losing potential customers to competitors recommended by AI engines, stating 'you can't fix what you can't see.' They are currently wasting high-value founder/marketer hours manually checking outputs.
“Track your product's visibility across major AI models and fix prompt-shaping drops instantly.”
An LLM monitoring dashboard that automates querying across all major AI models (ChatGPT, Claude, Perplexity, Gemini) with query-variation matrix testing, providing per-model visibility scores and alerting teams when prompt variations drop their product from recommendations.
Target tech product launch platforms (Product Hunt, IndieHackers) and relevant communities on Reddit (r/saas, r/marketing, Hacker News) where users openly share data on AI search optimizing or generative engine optimizations (GEO).
Running dozens of matrix variations per customer across multi-token expensive models could diminish product margins.
Hitting operational caps on upstream model providers when refreshing batch keyword lists across hundreds of accounts.
AI providers changing formatting or system instructions could break structured data extractors evaluating brand positioning.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis opportunity scores well above the median for ideas surfaced by MonetScope, with a validation sub-score of 9/10 against 4 independently sourced evidence signals. A "strong" rating in this band typically means the pain signal is consistent and recurring across multiple discussions, but one of the three pillars (severity, willingness to pay, or competitor weakness) is somewhat softer than top-tier opportunities. Founders evaluating this should focus customer discovery on the softest pillar first — confirming the gap before committing engineering time to a build.
It sits at the intersection of "ai-powered", "analytics", "marketing", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
