FAQ
Frequently asked questions
Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.
Will my sales team actually trust an AI forecast over their own judgment?
Trust builds over 1-2 quarters as the AI proves it's more accurate. The right framing is augmentation, not replacement: the AI surfaces signal the rep might miss ('this prospect hasn't replied in 2 weeks — are you sure it's 90%?'), and the rep either updates the assessment or provides context the AI can't see ('we talked on her cell, not email'). The AI gets smarter from the feedback. Most teams start skeptical and convert by month 3 when the AI catches 2-3 deals the rep was overly optimistic about. The AI doesn't replace rep judgment; it pressure-tests it with data.
How is this different from Clari, Gong, or BoostUp?
Those are revenue intelligence platforms — they surface signals and dashboards. Tycoon's AI Head of Sales acts on the signals: it not only flags the at-risk deal, it drafts the re-engagement email, suggests the meeting agenda, and schedules the internal deal review. Clari will tell you a deal is at risk; Tycoon will tell you, explain why, and execute the save play. The difference is the same Tycoon pattern: those tools are software you operate; Tycoon operates the software as your AI employee. For teams that already love Clari/Gong, Tycoon layers on top — it reads Clari data and takes action, it doesn't replace the signal source.
What if our sales process is consultative and relationship-driven — not transactional SaaS?
The AI adapts to your sales motion. For consultative sales (enterprise, 6-12 month cycles), the signals shift from 'email reply rate' to 'steering committee formed,' 'budget line item confirmed,' 'security review scheduled.' AI Head of Sales tracks these milestone signals instead of velocity signals. The forecast horizon extends to 2-4 quarters with appropriate confidence decay. For relationship-driven sales, the AI tracks relationship strength indicators: executive sponsor meetings, reference calls completed, champion's internal selling activity. The model learns what predicts a close in your specific motion — it doesn't force-fit a PLG SaaS model onto an enterprise consulting sale.
How does it handle new reps with no historical data?
New reps start with team-average close rates and ramp curves, adjusted for their experience level and territory. By month 2, the AI builds a rep-specific model: 'Jordan's commits close at 85% vs team average of 72%,' or 'Casey's early-stage deals 3× more likely to stall at legal review.' This rep-level calibration is one of the highest-ROI features — it surfaces that two reps saying '90% confident' mean very different things. Sales leaders use this to coach, not to punish.
Can it do capacity planning — how many reps we need to hit next year's target?
Yes. AI Forecasting Analyst can run team capacity models: given historical rep ramp time, quota attainment distribution, and territory coverage, it projects how many reps you need to hire and when to hit a revenue target. It models different scenarios: 'to hit $5M ARR by Q4 2027, you need 6 reps by Q2 at current ramp rates, or 4 reps if you improve ramp time by 30%.' This is the kind of analysis that normally requires a RevOps hire — the AI does it from your live CRM data.