FAQ
Frequently asked questions
Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.
Won't the AI misclassify requests — turn bug reports into feature asks or miss the real problem behind a vague complaint?
Classification isn't 100%, which is why the AI Customer Support attaches the raw message to every triaged row and flags low-confidence calls for your review. In practice, ~85% of messages classify correctly on the first pass (bug vs. feature vs. question vs. praise). The other 15% land in a 'needs-review' bucket that you or your AI COO resolves in a few minutes daily. The bigger failure mode isn't misclassification — it's missing context. A user complaint that sounds like a feature request might actually be a pricing objection. The AI reads the full conversation history (Intercom thread, prior tweets, past calls) before classifying, which cuts the context-blind errors dramatically. You can also retrain its classifier on your product vocabulary in a few sessions.
My power users will notice if the AI replies to their tweet acknowledging a feature request. How do you handle AI-sounding outreach?
The AI doesn't post public replies to tweets — too easy to go wrong, too public when it does. It DMs instead, or leaves a like/ack on the public tweet and follows up privately. Tone is calibrated to your past DMs (the AI learns your voice from your prior founder replies) and messages are short: 'Saw your ask about X — logged it as issue #142, you're voter 3 of 5. Will update when it ships.' Direct, specific, traceable. The anti-pattern is generic 'thanks for the feedback, we'll consider it!' which everyone reads as AI immediately. Specificity is the tell of real engagement, and the AI leans hard into it.
How does this handle requests from prospects vs. paying customers? A prospect's ask and a paying customer's ask aren't equal.
They're weighted differently in the score. A paying customer's ask gets their MRR as the base weight. A prospect's ask gets weighted by the AI Head of Growth's deal-stage confidence: a prospect in active trial with a decision date next week gets near-customer weight; a cold inbound asking 'would you ever build X' gets a fraction. The scoring formula is visible — you can see why row A ranks above row B — and you can override the formula weights for your business. Some founders care more about expansion (weight existing customers heavily), others care more about new-segment expansion (weight prospects higher). The default balances both, you tune from there.
What if I reject a request and the customer complains publicly? Does the AI route that to me or handle it?
Anything that's trending publicly — a request denial going viral on Twitter, a Reddit thread picking up steam, a community revolt in Discord — escalates to you within 10 minutes of trend detection. The AI Head of Growth and AI Customer Support don't try to handle reputation events alone. What they do prepare: a timeline of the request, full context of why it was rejected, who the complainers are, similar cases from the past, and two or three suggested response paths (ship it anyway, double down on the rejection with clearer reasoning, offer a different solution). You decide and respond; the AI drafts in your voice and sends once you approve.
Won't consolidating voices from free users, paying customers, and prospects just make my roadmap bland — chasing the average?
It would, if you let the score be the decider. But the AI flags high-variance requests explicitly — 'this feature has 1 enterprise voter willing to commit $40K ARR vs 23 free-tier voters who'd use it casually' — so you can make strategic bets instead of popularity plays. The feature request system is meant to surface signal that would otherwise stay scattered, not to auto-rule the roadmap. Founders who use it well read the weekly digest and then make a decision; founders who use it badly let the score rank everything. The AI CTO's monthly synthesis deliberately highlights contrarian requests and asks from specific customer types to counteract the average-chasing tendency.