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Product
Refunds in chatVerify the customer, find the payment, resolve it.Disputes & fraud warningsPrevent chargebacks, respond to disputes on time.Refund policy & simulatorClear rules, tested before you publish.Safety & auditWhy the AI can never move money.Payment analyticsApprovals, declines and alerts at a glance.TicketsYour team’s queue after the automation.CasesEvery request with its decision, step by step.
Solutions
Subscription softwareCancellations and forgotten renewals.Online storesDouble charges, deliveries, chargebacks.Digital goods & gamesFraud warnings and refund abuse.Online educationGuarantee refunds, by chat or email.Travel & bookingsLarge amounts, decided by people.Several countriesSeveral accounts, several currencies.Payment providersStripe, PayPal, Paddle, Adyen and more.Your own payment systemOne open contract over REST or MCP.Helpdesk teamsZendesk, webhook intake and email.
Developers
The Chargeless APIWebhooksAPI referenceYour own billing systemEvidence serviceprovidercheck
Resources
How it worksFrom customer request to resolution.IntegrationsProviders, channels and helpdesks.SecurityHow money and data are protected.FAQAnswers to the common questions.
DocsPricing
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Tickets

How your tickets arrive, move and end.

What automation closed, what reached your team, how fast it was solved, and why tickets were handed over.

Request early accessTickets: Overview

From arrival to outcome

Every ticket of the period by where it stands now, per channel. Tiles show created, resolved, reopened and backlog, with median reply and solve times.

  • Every path, per channel
  • Created, resolved, backlog
  • Median reply and solve time
Ticket analytics in the merchant panel.
Illustrative example, not real data

Targets, team and reasons

Targets met, each member’s workload and response times, and why tickets came to your team: your best hint for a policy change.

  • Targets met per priority
  • Per-member figures
  • Handoff reasons
Ticket analytics, SLA targets: first reply and resolution targets met, next reply breaches, targets by priority and tickets due soon or overdue.
Illustrative example, not real data

Filters and the fine print

Filter by channel and priority, group by day or week. Helpdesk tickets are counted, but kept out of the team’s figures.

  • Channel and priority filters
  • Day or week grouping
  • Helpdesk tickets apart

Last 30 days

Executed automatically71%
Handed to your team20%
Median first reply1 h 40 m
Illustrative example, not real data

More in Tickets

Start with low limits. Raise them when the record shows it works.

Start in test mode, run your policy against your own history, and go live when the results meet your standards.

Request early accessHow it works
  1. 1A test workspace on your provider’s test mode
  2. 2Your past requests replayed against your policy
  3. 3Live with low limits, raised as the record allows