An AI customer service agent handles the repetitive majority of tickets — order status, account changes, policy questions, password issues — by reading the request, checking your systems and acting. Complex or sensitive cases are escalated to a human with the history and diagnosis already attached.
Resolution, not deflection
The previous generation of support bots optimised for deflection: keep the customer away from a human at any cost. Customers learned to type 'agent' immediately, and satisfaction fell. An agent that can actually read the order, apply the refund policy and process the change is a different product — it resolves rather than stalls.
The design principle we hold to is that escalation must be fast, obvious and lossless. If a customer asks for a human, they get one, and that human sees everything already said.
Process
How we deliver it
Process flow for AI agents for customer service
01
Analyse the ticket history
Cluster past tickets to find what is genuinely repetitive.
02
Ground the knowledge
Connect policies, product data and account systems with citations.
03
Build resolution paths
Actions the agent can take, and hard limits on what it cannot.
04
Set escalation rules
Confidence thresholds, sensitive topics and instant human handover.
05
Launch and measure
Track resolution rate, escalation rate and customer satisfaction.
Deliverables
What you receive
A live support agent across your chosen channels
Grounded knowledge base with source citations
Escalation rules, handover context packaging and audit logging
Resolution and satisfaction reporting
Team training on reviewing and improving agent responses
Engagement shape
Six to ten weeks to first production launch, scoped by channel and ticket volume.
Stack decisions follow the problem. This is where we usually start, not a fixed menu.
Frequently asked
Questions we get about this
What resolution rate is realistic?
It depends entirely on how repetitive your ticket mix is. Businesses with a concentrated set of common questions see a large share handled end to end; those with highly bespoke enquiries see much less. We analyse your actual ticket history before quoting a number, rather than repeating an industry average that may not apply to you.
How do we stop it giving wrong policy answers?
The agent answers only from your grounded documentation, cites the source, and refuses when the answer is not there. Refusal routes to a human. An agent that says 'I do not know' is far cheaper than one that invents a refund policy.
Talk it through before you commit
A discovery call is a working session on your constraint, not a sales pitch.
