Most customer support AI agents read your help center back to the customer. Kal authenticates them, calls Stripe or your CRM, runs the action, and closes the ticket. No human in the loop. An execution layer for the work your team should never have to touch.
The difference
An AI chatbot that quotes the manual never cut your headcount.
The market is flooded with AI chatbots that summarize help articles. They deflect a question and call it a day. Your queue, and your payroll, never noticed. Resolution is a different job.
A customer writes
"I want my money back."
Summarizing chatbot
"Here's how to request a refund: open Settings, go to Billing, and submit a request."
Resolution layer
"Done. I've refunded $49.00 to your card ending 4242."
The loop
Ticket in. Ticket closed.
Four steps run between the customer's message and a resolved conversation. Here is one $49 refund moving through every one of them.
Kal picks it up
Thinking…
Authenticate
Confirms it is really them.
Verifies the customer against your own records before it touches anything. Email match, order ID, last-four, magic link. Your rules. No identity, no action.
Decide
Checks if it is allowed.
Reads the request against your policy. Inside the refund window? Plan eligible to downgrade? It only proceeds when the answer is yes. Everything else routes to a human.
Execute
Makes the API call.
Fires the real action in Stripe, your billing system, or your CRM. A refund is a refund. A downgrade is a downgrade. Not a draft for someone to copy-paste.
Close
Replies and closes the ticket.
Tells the customer it is done, logs every step, and marks the conversation resolved. Your team never opens it. Most finish in under a minute.
No human ever
sees the easy ones.
The refund. The downgrade. The address fix. They come in, get done, and close. Your team only works on the conversations that actually need a person.
Actions, not answers
The tickets that should never reach a person.
Hooked straight into the systems where the work happens. Each one authenticated, executed, and closed.
Refunds
"I want my money back." Inside the window and within policy, Kal issues the refund and confirms the amount and card.
Plan changes
Upgrades, downgrades, seat changes. Kal updates the subscription, prorates it, and shows the new amount before the next invoice.
Account updates
Email, billing address, company name. Kal verifies the request and writes it straight to the record. No internal ticket.
Shipping fixes
Wrong address caught before it ships? Kal updates the order and tells the customer the corrected destination.
Cancellations
Cancels the subscription, sets the end date, and confirms what they keep until then. Logs the reason for your retention team.
Invoices & receipts
Resends the invoice, generates a receipt, updates the VAT number. The errands that eat an L1 queue, gone.
Automate 30% of your actionable tickets,
and it pays for two of your L1 reps.
A mid-market support org spends most of its L1 hours on refunds, plan changes, and account edits, the exact work Kal can finish end to end. Take those off the queue and you stop backfilling two full-time seats. Auto-Resolve costs a fraction of one of them.
Actionable tickets resolved
L1 seats you stop backfilling
Humans in the loop
Ticket to closed
Figures are illustrative of a typical mid-market support org and depend on your ticket mix and policies. You pay only for resolutions Kal actually closes. See plans and per-resolution pricing.
Trust
Autonomous. Not unsupervised.
Letting an agent touch billing is only a no-brainer if it stays inside the lines. Kal does. By design, not by hope.
Authenticates first, always
No action runs on an unverified person. You set the proof you require, and Kal will not move without it.
Scoped to what you allow
Refund up to a ceiling. Downgrade but never delete. Kal acts inside the exact limits you grant. Nothing past them.
Every step is logged
A full audit trail of who asked, what ran, and which API responded. Export it for finance or compliance any time.
Hands off when unsure
Edge case, angry customer, anything outside policy. Kal stops and passes the full thread to a teammate. It never guesses.
FAQ
Questions. Honest answers.
What teams ask before they let an AI agent touch billing. Written by the people who built it.
Yes. Auto-Resolve is an execution layer, not a chatbot. Kal authenticates the customer, checks the request against your policy, runs the action in Stripe or your CRM, and closes the ticket. No human touches a resolved conversation. Anything outside policy is handed to a teammate with the full thread.
Refunds, plan changes, cancellations, account and address updates, invoices and receipts. The repetitive Level-1 tickets that fill a support queue. Each one is verified, executed in the real system, and logged. This is resolution, not ticket deflection that leaves the work for a rep to finish.
You pay per resolution Kal actually closes, on top of a per-seat plan. Free to start, no card required. See plans and per-resolution pricing for the full breakdown.
Both. A small business gets an AI helpdesk that closes routine tickets from day one, without hiring an L1 rep. A mid-market team stops backfilling seats. You set the scope and the limits; Kal stays inside them and hands off anything it is unsure about.
Updated June 2026
Stop staffing the easy tickets.
Wire Kal into Stripe and your CRM, set the limits, and let the resolution layer take the work your team never wanted. Free to start. No card.