Use case · Customer support

Close the ticket. On the first reply.

The agent reads the help center, the policy, and the ticket in front of it. The reply cites the article. A person takes the ones that need a human. The data stays on your network.

80% fewer repeat ticketsSource on every reply24/7, in the customer's language
EazyAI · Customer Support AISample
Where is the refund for order 4412? It has been nine days.
Sentiment · frustrated · order found in the ticket
Refund policy, section 4: returns received are refunded in 5–7 business days after warehouse scan.
Warehouse scan on this order is day 6. The window is still open.
Where is the refund for order 4412?
Your return was scanned six days ago. Refunds go out 5–7 business days after that scan, so this one is still inside the window. Refund policy §4Scan logDraft · ready to send
This ticket also asks to change the payout bank. That step is not in the refund policy.
Draft saved. A specialist has the thread and the citations. Waiting on a person

What changes on this desk.

The screens above are a sample of the workspace. They play on their own. Select a step to hold it.

The queue grows. The answer already exists.

Agents retype the same refund, shipping, and setup answers. The article is in the help center. The ticket still waits. A wrong reply becomes a second ticket.

The first reply is the one in your docs.

EazyAI drafts from the sources you connect and names the article. High-confidence replies can go out. Low confidence, anger, or a missing policy lands with a person, with the draft already written.

How EazyAI is used here.

Four moves. The sources are yours. The answer names them. A person keeps the decision that matters.

01Connect the desk

Help center, policies, and past tickets. They stay on your network.

02Answer from those files

Dual RAG pulls the passage. The reply names it. Empty policy does not invent a refund date.

03Send or hand off

A clear match can go out. Sentiment, a missing rule, or a money change waits for a person.

04Put it where customers are

The same agent sits in the widget, the help desk, or on a voice line.

What you can show a buyer.

The working shape of this use case. Live in two to three days on the sources you already have.

Grounded in your articles

The reply is built from the help center and the policy you uploaded, with the passage attached.

The customer's language

The question can arrive in one language and the answer can return in that language.

A person on the exception

Anger, a missing policy, or an account change keeps the draft and routes the ticket.

80%Fewer tickets that need a second reply
24/7The same policy, including overnight
2–3 daysA first support agent on your sources

The file stays.

The project runs on your premises or your private cloud. Dual RAG cites the passage. The model you serve is the one you register. A demo is the way onto a first workflow.

Operating proof on the support desk.

First-reply resolution from your help center and policies. Exceptions escalate with the draft and citations attached.

80%Fewer tickets that need a second reply*
CitedSource on every reply
HumanOwns anger, money, missing policy

Policy-grounded. Escalation with context.

Dual RAG pulls the article. Empty policy does not invent a refund date. A person takes the exception with the draft already written.

First-reply resolution

High-confidence matches can go out. The article is named in the reply.

Policy-grounded

Help center, refund rules, and past resolutions stay on your network.

Escalation with context

Sentiment, payout changes, and missing rules route to a specialist queue.

Before you book.

What support leadership asks before a first agent goes live.

Does the agent invent policy?

No. If the source is silent, the reply does not invent a date or a refund. The ticket waits for a person.

Can it sit in the widget and on voice?

Yes. The same project grounds the widget, the help desk, and a voice line.

Who owns account changes?

A person. Money moves and bank changes keep the draft and escalate.

Other desks.

Same platform. A different set of sources and a different person in the loop.

See this use case on your files.

Book a demo. A first workflow in two to three days. Your data stays where it is.

Book a demo