Zackary M.

I was testing how LLMs rank products in our space by asking vague buyer questions like "what's the best tool for X." We showed up on Gemini, but ChatGPT kept listing older competitors that haven't updated in years.

It made me realize how inconsistent these AI recommendations are compared to traditional Google SEO.

If you added comparison pages, structured data, or an llms.txt file recently, did you notice any real difference in how models cite you?

Ours was simple. We priced for the Fortune 500 budget. Leads were coming in, interest was real, but deals kept dying. Took us months to understand why.

CFOs treat new marketing experiments like a rounding error on their total spend. If your price tag is bigger than what they mentally allow for "let's try this," the deal dies in finance, not in the sales call.

We launched a second brand at credit card swipe pricing instead. Same service, different lane.

What was your pricing mistake?

I upvote products on Product Hunt that I will probably never use.
If a launch post has a clear problem statement, an honest tone, a solid demo video, and a maker comment that sounds like a real human instead of a corporate press release, it gets my vote.
On the other hand, I often scroll past genuinely useful tools because the pitch is full of buzzwords and bad screenshots.
Upvotes are technically meant to signal product value, but for me, they often mean "thank you for explaining this clearly."
Does anyone else vote for the quality of the presentation, or do you strictly judge the product itself?