AI customer support agents
Faster answers for customers. More time for complex cases.
We build support agents that work from your own knowledge and systems, resolve routine requests end to end, and hand over to your team whenever a case needs human judgement.
Customer
My order hasn’t arrived. Can I change the delivery address?
AI agent
Your order shipped yesterday and is due Thursday. Address changes after dispatch need our team, so I’ve passed this on with your details.
Common support challenges
Most support load is predictable. Most support tooling isn't built for it.
- 01
Repetitive volume
A large share of requests ask the same questions about orders, accounts and policies, crowding out the cases that need real attention.
- 02
Fragmented knowledge
Answers live across help centres, internal documents, ticket history and business systems - rarely in one place an agent can search.
- 03
Inconsistent answers
Different people give different answers to the same question, especially as policies change.
- 04
Risky automation
Scripted chatbots frustrate customers, and unconstrained generative replies can state things your business never agreed to.
Agent capabilities
What a support agent can do
Each agent is designed around your channels, your policies and the actions it is allowed to take.
Understand intent
Interpret free-text requests, follow multi-turn conversations and ask clarifying questions when information is missing.
Answer from your knowledge
Retrieve relevant passages from help content, policies and past resolutions, and ground replies in those sources.
Act in your systems
Look up orders, update records or create tickets through permissioned integrations rather than free-form access.
Escalate with context
Hand conversations to your team with a summary, the sources used and the reason for escalation.
Illustrative conversation flow
From question to resolution - or to the right person
A typical flow for a delivery question. The agent answers what policy allows and escalates what it doesn't.
Conceptual flow. Steps, systems and rules are defined per deployment.
Flow
Illustrative01Customer asks a question
“My order hasn’t arrived - can I change the delivery address?”
chat · email
02Retrieve knowledge and order status
The agent searches the shipping policy and looks up the order through a read-only integration.
knowledge base · order system
03Draft a grounded reply
It answers the delivery question using the retrieved policy and live order status.
When: Policy requires human review
04Escalate the address change
Address changes after dispatch need a person, so the case is handed over with a summary.
helpdesk
Escalation and human oversight
Clear limits, visible reasoning, easy handover
Good support automation knows when to stop. We design those boundaries with you before anything goes live.
Defined scope
Agents only handle request types and actions you approve. Everything else is routed to people.
Escalation rules
Hand over on low confidence, sensitive topics, policy exceptions or a customer simply asking for a person.
Reviewable conversations
Transcripts, retrieved sources and actions are logged so your team can audit and improve behaviour.
Measured quality
We evaluate answers against realistic test conversations before launch and keep monitoring after it.
Implementation approach
How we would approach it
Every engagement is shaped by your systems and constraints. A typical path looks like this.
- 01
Map the request mix
Review real conversations to find which requests are routine, which need people, and why.
- 02
Prepare knowledge
Organise and connect the content and systems the agent needs, with access scoped appropriately.
- 03
Test realistic conversations
Evaluate replies and escalations against scenarios drawn from your own support history.
- 04
Launch gradually
Start with a narrow scope or agent-assist mode, monitor closely, then widen coverage.
Have a business problem AI could solve?
Let’s understand the challenge and explore what a useful solution could look like.
Or email info@skarp.global