AI employee · Support
An AI support employee that resolves the routine and routes the rest.
Reads incoming requests, retrieves customer context, classifies issues, drafts or sends responses within policy, updates tickets, and escalates sensitive cases to your team.
AI Support Employee
Customer support
- Task
- Answering a delivery-status request
- Tools
- Help deskOrder DBKnowledge base
- Approval
- Required for refunds and account changes
- Last action
- Tagged ticket and drafted reply
The role
What this AI employee is for
Front-line support: understanding what a customer needs, finding the relevant information, and either resolving it or getting it to the right person quickly. Much of this work is repetitive, but it still requires context.
Repetitive work it can handle
- Read incoming emails, chats, and tickets
- Retrieve customer account, order, and history context
- Classify issue type, urgency, and sentiment
- Draft responses using your knowledge base and policies
- Send replies for approved, low-risk request types
- Update ticket fields, tags, and status
- Escalate sensitive, complex, or upset-customer cases
- Identify recurring issues for the product or operations team
Systems
Tools it can connect to
Connected through scoped, revocable access. Examples below; your actual stack is confirmed during scoping.
- Help desk (e.g. Zendesk, Freshdesk, Intercom)
- Email and chat
- CRM
- Order or account database
- Knowledge base
- Slack or Teams
Approval model
Clear lines between doing, asking, and never
allow
Handles on its own
- Ticket classification and tagging
- Answers to documented questions
- Status updates
- Routing
require approval
Asks for approval
- Refunds and credits
- Account changes
- Replies to complaints
- Anything not covered by documentation
deny
Never does
- Makes policy exceptions
- Shares data with unverified requesters
- Closes tickets marked sensitive
Example workflow
Ticket to resolution
- 1
Ticket in
Email, chat, or form.
- 2
Context
Account and history retrieved.
- 3
Classify
Type, urgency, and sentiment.
- 4
Respond
Draft or send within policy.
- 5Human
Escalate
Sensitive cases to an agent.
- 6
Update
Ticket fields and notes saved.
Implementation
How we put it to work
Ticket analysis
01Review historical tickets to find the most common, well-documented request types.
Knowledge preparation
02Organise help articles and policies for retrieval.
Draft-only launch
03All replies drafted for agents to approve.
Selective auto-send
04Specific request types sent automatically once accuracy is proven.
Expected benefit
What changes for the team
Shorter first-response time
Routine requests get an accurate answer quickly.
Better routing
Tickets reach the right person with context attached.
Consistent answers
Responses follow current policy and documentation.
Insight into recurring issues
Patterns in requests are surfaced for the wider team.
Limitations
What it does not do
- Quality is limited by the accuracy of your knowledge base.
- Emotionally sensitive conversations should stay with people.
- New products or policies need documentation before the AI can handle them.
FAQ
Frequently asked questions
01Will it replace our support team?
It is designed to handle routine volume so your team can focus on complex and sensitive conversations. Most teams use it to keep response times steady as volume grows.
02Can it work in our existing help desk?
Yes. It works inside your help desk through its API, so agents see AI drafts and actions where they already work.
Related reading
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Next step
Design your AI support employee.
A strategy call is a working conversation about one workflow: what it involves today, what could be automated, and what a first build would look like.
What happens next
- 1Tell us about one workflow
- 2We review it before replying
- 3A working session, then a first-build proposal