FAQ
Frequently asked questions
Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.
How is this different from just using Canny or Discord for feedback?
Canny and Discord are feedback surfaces — they capture input but don't run the beta program. They don't select cohorts, onboard testers, ensure activation, triage bugs, track usage, or convert beta testers at graduation. Tycoon orchestrates across Canny + Discord + Linear + Mixpanel + your email tool to run the end-to-end program. Most teams use Canny for public feature requests post-launch and Discord for community; beta needs more structure than either provides alone.
My beta is 5-10 people — is this overkill?
Structure scales down well. For 5-10 testers: skip the segmented cohorts, use one Slack channel instead of Discord, do weekly 1:1 calls instead of async forms. The AI still handles: tracking who's activated, aggregating feedback themes, converting bugs to Linear issues, sending the Friday pulse. The overhead is near-zero for small betas because most of the work is orchestration you'd do anyway. For larger betas (50+), the structure pays off more dramatically.
What about closed betas with NDAs — handling confidentiality?
Supported. NDA signed during beta invite (via DocuSign, auto-routed by /workflows/legal-document-generation). Access gated by NDA completion. Feedback channels marked 'confidential' — AI Customer Support doesn't leak beta-specific info to public channels or to testers not in the cohort. Graduation email includes reminder that NDA lifts on public launch (or continues, depending on terms). For highly sensitive betas (regulated industry, pre-announcement products), the workflow has a 'privileged' mode that keeps everything in isolated memory scope.
How do I know when beta is 'ready to launch' vs 'needs more baking'?
AI COO tracks a readiness scorecard: (1) NPS trending — rising or stable (launch-ready) vs declining (hold). (2) Critical bug rate — <1 per week (ready) vs 3+ per week (hold). (3) Activation rate — >60% (ready) vs <40% (hold). (4) Feature completeness — beta testers report <3 missing features as critical (ready) vs 5+ (hold). (5) Qualitative signal — testers asking 'when can my team use this?' vs 'I'm not sure this solves my problem'. All five green = launch. Any red = specific gap to close. Shifts the decision from gut feel to evidence.
Can we run multiple overlapping betas for different features or cohorts?
Yes, and this is common for bigger products. AI COO supports multiple beta programs in parallel with separate cohorts, feedback channels, and Linear tags. Example: 'mobile app beta' + 'enterprise dashboard beta' + 'API v3 beta' running concurrently with separate testers, separate feedback themes, separate graduation ceremonies. Cross-beta insights (a bug affects multiple surfaces) get flagged by AI COO for coordinated handling. Keeps each beta focused while capturing systemic issues.