Model repetitive ticket load first
This calculator is most accurate when the input volume is repetitive support traffic, not complex enterprise onboarding work.
Free Tool
Estimate how much support load an AI assistant can actually remove.
Model your monthly ticket pressure, likely deflection, and captured escalation volume. This is built for business owners who need a sober payback view before they roll out an AI support layer.
ROI Snapshot
Estimated monthly outcome
$10,867
After a $149 Growth plan, this model suggests the assistant could remove 612 repetitive tickets per month and still route the right customers to a human.
Support cost saved
$11,016
$132,192 annualized at the same ticket volume.
Tickets deflected
612
Per month at the current deflection assumption.
Escalations captured
108
Qualified handoffs or leads the bot can route cleanly.
Recommendation
This looks like an immediate rollout candidate.
Use the calculator as a planning model, then validate against real widget traffic, grounded answer quality, and lead routing once the bot is live.
How to use this
Use this as a planning model, then prove it inside live support.
The calculator estimates operational upside. The product closes the loop with grounded answers, escalation capture, conversation review, and analytics that let owners see whether deflection is actually happening.
Business-owner planning lens
Validate citations before scaling
Deflection only holds if the bot answers with grounded product knowledge and operators can inspect misses quickly.
Pair savings with lead routing
A healthy support assistant should remove repetitive work while still capturing high-intent escalations and buyer signals.
Trial Handoff
Take the estimate into a live support workspace.
Index your docs, launch the widget, inspect unresolved conversations, and measure whether the deflection and escalation assumptions above are actually happening.