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.
Exactly. That "click" is the sound of Product-Message Fit.
Most builders treat their landing page like a technical manual, but users read it like a survival guide. If they have to "work" to understand how you save them, they're gone. Compass is designed to catch those alignment errors before you waste months of traffic on a message that doesn't land.
What was the one specific word or phrase you changed that finally made your last project "click" for users?
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.
That is a perfect parallel. You've essentially built "Episodic Memory" for trading, while Compass is building "Institutional Memory" for a startup's positioning.
The "three types" of memory you settled on, bugs, decisions, and principles, is a brilliant framework. It moves the agent from "guessing based on data" to "acting based on wisdom." In my world, those "principles" are the Listing Quality Framework, the rules that emerged from seeing 1,000+ builders make the same mistakes.
Context doesn't just close the gap; it prevents the "Groundhog Day" loop of making the same architectural (or marketing) errors twice.
How are you weighting those three memory types? Does a "Principle" carry more weight than a recent "Bug" when RayoBot generates a new strategy?
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?
Smart move. That 0% to 2% jump is the perfect "Context Premium" case study.
On the data side: Compass doesn’t "index" on a schedule; it’s an active listener. Because it’s baked into the IndieAIs directory, the "Co-Founder" has a direct line to your listing's database. The second you update your headline, category, or pricing in your dashboard, that new data is the "foundation" for the next prompt. It’s real-time context, not a stale cache.
Quick question for your outreach scanner: Are you feeding that SEO data back into a CRM, or just using it for the initial hit? I’m looking at how Compass can "track" positioning shifts over time similarly.