How I’m Building a Regulated AI Product Faster & Leaner — Need Advice on Feature Scope
Hey everyone — I’m building CompliAssistant™, an AI assistant to help healthcare teams handle tough compliance questions quickly.
To move fast and keep costs down, I’m focusing on a very narrow feature set and strict compliance controls from day one. This helps reduce risk but limits what the product can do out of the box.
My question:
How have you balanced building fast and lean while not sacrificing essential features — especially in regulated or high-trust industries? Any tips for deciding what features to prioritize or delay?
Would love to hear your experiences or frameworks that worked for you!
— Kai
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Respect bro, I’m building my own AI tool too (DM Genius — helps rewrite cold DMs to get more replies
Keeping it super lean has helped me actually make progress instead of drowning in features.
Curious how you’re deciding what to cut vs keep? Always feels like a balancing act.
Respect right back 🙌 Love what you're doing with DM Genius — real value in helping people get replies that convert.
Totally feel you on the balancing act. With CompliAssistant™ (HIPAA compliance assistant), I’ve been ruthless about cutting anything that doesn’t serve one goal: helping users get a trustworthy answer to a HIPAA question fast.
If a feature doesn’t directly improve speed, accuracy, or trust, it’s on the chopping block. I keep a “maybe later” list, but launch with the bare minimum that delivers real value.
Curious — what was the hardest feature you had to cut from DM Genius?
Appreciate the kind words. Means a lot coming from someone building something so high-stakes and focused like CompliAssistant™ — super inspiring how you’ve defined such a clear success filter (speed, accuracy, trust).
Totally with you on the "maybe later" list. For DM Genius, I actually had to cut a pretty cool feature idea early on: auto-personalization based on LinkedIn profiles. Would’ve been slick, but it added way too much complexity and API overhead. Right now, I’m just focused on doing one thing ridiculously well — rewriting cold DMs so they actually get replies.
Once that’s locked in and working smoothly, I’ll revisit the rest.
How did you come up with your trust criteria — trial and error or was it clear from the start?
Totally makes sense — sounds like you made the right call. Auto-personalization is a strong differentiator, but yeah, the complexity adds up quick when you’re trying to stay lean and move fast.
On the trust side, it came from early feedback more than anything. People are quick to disengage if something feels even slightly off. That shaped our thinking pretty fast.
We keep things simple: if a decision doesn’t clearly improve user confidence, we push it to the “later” pile.
Would love to hear — what signals are you using right now to know DM Genius is working? Replies, conversions, anything else?
Right now my main “is it working?” signals are:
Reply rate to rewritten DMs (main metric I care about)
User return rate — are they coming back to rewrite more DMs?
A bit of qualitative feedback from early users (“this got me a meeting” is gold)
I’m trying not to overcomplicate it this early, so I’m focusing on getting a small group of people genuinely hooked before I chase bigger numbers.
If you’re curious, here’s the tool: https://www.dmgenius.io/ — still early days but already seeing some interesting results.
Do you have an email to talk further ?
Yeh it’s [email protected]
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