Show IH: I Built an AI “Co-Founder” That Already Knows Your Product Before You Type Anything
I’ve been building IndieAIs, an AI tools directory, and noticed a frustrating pattern.
Most builders don’t struggle with building.
They struggle with explaining why their product matters.
So I built something different.
Compass AI, an AI co-founder built directly into the directory.
The key idea:
it already knows your product before you ask anything.
It has your listing, your category, your positioning, your traction, so instead of generic advice, you get feedback that actually applies to your tool.
You can ask it things like:
“why isn’t this converting?”
“how should I reposition this?”
“who are my real competitors?”
“what should I focus on this week?”
And it responds with specific, contextual answers, not templates.
This came from reading hundreds of indie builder posts where the issue wasn’t the product… it was the gap between what was built and how it was understood.
Just opened early access.
If you’re building something and want feedback that already has context:
👉 https://indieais.com/IndieAIsCompass
Would love to test it on real projects from here.
The “gap between what’s built and how it’s understood” is real. We’ve seen similar where the product itself is fine, but the way it’s explained doesn’t line up with how users actually think about the problem.
Once that clicks, things tend to move a lot faster.
The context-first approach resonates. I've been building something similar but for autonomous trading agents.
RayoBot, the strategy generator in my lab, now consults an episodic memory log before generating any new strategy. It knows which bugs were caused by which design decisions, which market regimes killed which agent types, and what architectural principles emerged from 27 days of real trading.
The result: instead of generating strategies from scratch every cycle, it generates strategies informed by what actually happened — not just what the backtest said.
The hardest part wasn't building the memory. It was deciding what deserved to be remembered. We settled on three types: bugs (with root cause and fix), architecture decisions (with reasoning and outcome), and principles (design rules that emerged from repeated failures).
Your point about the gap between what's built and how it's understood is real. In our case it was the gap between what agents were designed to do and what they actually did in live conditions. Context closes that gap faster than any additional feature.
the context-aware angle is smart. generic AI advice is useless because it doesn't know your specific situation. i built something similar for outreach — my scanner already knows the prospect's SEO issues before i write the email, so the pitch is specific instead of templated. that one change took my reply rate from 0% to 2%. context is everything. how are you handling the data freshness problem? listings change, positioning shifts — how often does Compass re-index?
"Having an AI 'co-founder' that already has the context of my positioning and traction saves so much time over generic advice—it’s like skipping the 20-minute intro. 🧭 Using it to fix conversion gaps is a smart use case.Nice idea, this could be a good way to test it. There’s a competition where you can submit Compass AI — entry is $19 and winner gets a Tokyo trip.Prize pool just opened at $0 so your odds are the best right now."
Interesting angle. One thing that gets missed with "already knows your product" is trust, users bail fast when the context is 80 percent right but stale. In stuff like this, a simple way to inspect, correct, and refresh what it thinks it knows matters as much as the chat itself.