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You’re describing a very real moment. What I usually see right after that: companies show up in AI answers inconsistently -> which means they’re losing a chunk of high-intent demand they don’t even track yet. In similar cases, that’s easily 10–25% of potential inbound, especially if AI is already replacing search in their niche. Curious, do your customers actually measure lost visibility vs captured demand, or just track mentions?

Speed is great, but the real metric is conversion -> won jobs, not proposals sent. In many cases, founders overestimate performance and leave $2–5k mrr per month on the table. Have you tested positioning Outbid around deal success instead of proposal velocity?

Appreciate the context, that actually confirms what i was seeing. points that stood out: 1. Category framing is limiting perceived value Right now it still reads close to “prospecting tool,” while what you described is much closer to pipeline generation infrastructure. That usually impacts both pricing expectations and conversion quality. 2. The outcome isn’t explicit enough For this type of product, the strongest trigger is: “how many qualified opportunities does this generate per week/month?” Without that, it’s harder for buyers to map it to revenue. 3. Positioning vs alternatives You’re not really competing with other prospecting tools, you’re replacing manual outbound + fragmented tooling + time cost. That shift usually opens a completely different pricing ceiling. I can map this into a more concrete repositioning + where it likely impacts conversion on the page if that’s useful.

Right now it feels positioned as an “ai intern,” which sounds interesting but also a bit vague in terms of business impact. The product actually looks closer to ai ops automation for b2b teams, which is a much higher-value category. That difference usually affects both conversion and pricing. I can share a quick breakdown of where this might be leaking revenue. want me to go deeper?