Serge Punchev

The multi-agent approach is what makes this interesting to me. Having different AI agents analyze the same portfolio from different angles -risk, growth, sector trends -is fundamentally better than one model trying to do everything. We're seeing the same thing in business strategy: the best insights come when you let multiple perspectives clash, not when you ask one AI to be an expert in...

The "idea to exit" scope is ambitious -in a good way. As someone who's been through 4 startups, the gap I always felt wasn't at the idea stage. It was in the messy middle: should I pivot, should I double down, should I fire that client. Does the AI push back on your assumptions or mostly validate what you feed it? Because a cofounder who just agrees with you isn't really a cofounder.

This is really interesting to me. We're building something in the multi-agent space too (for business strategy) and the one thing I've learned is that the magic isn't in the individual agents -it's in the disagreement between them. That's where the real insights come from. How are you handling cases where both agents just converge on the same answer instead of actually challenging each other?

I like that you're automating the GTM stuff specifically. Most founders I know (myself included) spend way too much time on market research and SEO when they should be talking to users. One question-how does it handle niche B2B markets where the data is thin? That's where I've always struggled with AI-driven research.

The investor matching part is what caught my eye. I'm raising an Angel round right now, and honestly, the hardest part isn't the pitch- it's figuring out which investors actually care about your space vs. who's just taking meetings. If this can cut through that noise, it solves a real pain point. Curious how the matching algorithm weights thesis fit vs. check size vs. stage preference?