DATA BOUNDARY
Read-scoped by default
The agent gets read access to the specific objects it needs, not a service account with the run of your CRM.
An agent reads the ticket, gathers the account history a human would have gathered, and drafts the reply they would have written. Your team approves, corrects, or takes over. Nothing sends itself on day one.
Watch an agent handle a ticket and the writing takes ninety seconds. The other six minutes are spent finding out who the customer is, what they bought, what went wrong last time, and what the policy says. That work is mechanical, and it is where the queue backs up.
This is also why deflection bots disappoint. A bot bolted to the front of the queue answers the easy third and infuriates everyone else. An agent placed behind the queue does the research on every ticket, including the hard ones, and hands your team a head start instead of a wall.
Every one of these is configured in the build, not promised in a contract.
Swipe →
The agent gets read access to the specific objects it needs, not a service account with the run of your CRM.
Drafts cite the record or policy clause behind each statement, so a reviewer checks in seconds rather than re-researching.
Below a confidence floor the agent escalates with its notes attached instead of producing a plausible answer.
Refunds, legal threats, cancellations, safety complaints — routed to a named human, never drafted.
What it read, what it drafted, who approved it, what they changed — queryable, exportable, retained on your terms.
Disable the agent and the queue works exactly as it did before. No process depends on it being there.
Four stages. You decide at each one whether the agent has earned the next.
The agent drafts on live tickets. Nobody sees the drafts but you. We measure how often it would have been right.
Drafts appear in the agent's console. Your team edits and sends. Their corrections become training signal.
On ticket types with a proven track record, review collapses to one click. Everything else stays in assist.
A small set of unambiguous categories sends without review, sampled and audited. Many clients stop at Stage 3 — that's a legitimate end state.
We won't quote you an industry-average saving. Here's the arithmetic we'd run with your numbers during the Sprint — run it yourself first.
Line 3 is where most business cases quietly cheat. Measure it before you believe anyone's projection, including ours.
Only what a given task requires, and only under the terms you choose. We deploy against providers with zero-retention agreements, on your cloud account or ours, and we can run open-weight models entirely inside your network where policy demands it. This is decided in the Sprint, not after the contract.
No. The agent works through your helpdesk's own API and appears as drafts and internal notes inside the tool your team already uses. Replacing the helpdesk is the fastest way to lose a support team's goodwill, so we don't.
In shadow and assist stages, a human catches it and their correction is logged as a signal. In approve and autonomous stages, we sample continuously and pull categories back a stage when accuracy slips. The design assumption is that it will be wrong sometimes — the containment is the product.
That's your decision, not ours, and we'd rather you made it with real numbers. What we consistently see first is the same team clearing backlog, answering faster, and spending their time on the tickets that actually need judgment. Teams that frame it as a layoff mechanism get poor adoption from the very people whose corrections train the system.
You do. Source in your repository, deployed to your infrastructure, documented so your engineers can maintain it without us. We'd like you to keep us on retainer because the work is good, not because you're locked in.
We'll classify it, tell you which categories an agent could safely handle, and show you a working draft against your real tickets. Five days, no cost, findings are yours.