Deflection that does not damage the relationship
Routine questions get a specific, correct answer drawn from your own articles rather than a search page or a bot that loops the customer back to the start.
AI Customer Service Agents
AI Agents for Customer Service Teams
Resolve the repetitive half of your ticket volume across email, chat, and portal, route the rest with the context already gathered, and keep every answer traceable to an approved article.
2Agents covering resolution and feedback analysis
4Channels served from one knowledge base
CitedEvery answer traced to an approved article
On-premCustomer conversations stay in your estate
Enterprise controls
Answers constrained to approved knowledge contentConfidence threshold below which it routes to a humanRefunds and account changes require explicit approvalCustomer conversation data stays on your infrastructure
Category overview
Customer service agents for queues that grow faster than headcount
VDF customer service agents close the loop between answering customers and learning from them. One works the live queue, resolving the repeating share from your documented knowledge and escalating the rest already diagnosed. The other reads the whole accumulated record — tickets, surveys, reviews, calls — and ranks what to fix so the queue gets structurally smaller.
Escalations that arrive ready to work
When a case needs a person, it lands with the history summarised, the account context pulled, the category set, and the likely cause proposed.
A ranked case for fixing the cause
The whole feedback corpus is grouped into themes and weighted by reach, revenue, and churn, so product work is argued from evidence rather than from the loudest complaint.
Customer Service Agents
Explore the customer service agents catalog
Each agent has its own SEO page with use cases, governance notes, expected outputs, FAQs, and related tools.
Operating model
From resolving a ticket to removing the reason for it
The two agents run on different clocks against the same material: one in seconds against a live contact, the other across months of accumulated history. Together they turn a support queue from a cost centre into a source of product direction.
Ingest across channels
Messages from email, chat, the portal, and connected messaging platforms are normalised into one queue with the customer record attached.
Resolve or route on confidence
High-confidence cases are answered and closed with the source article cited; the rest go to a person with a diagnosis attached.
Read the accumulated record
Tickets, survey verbatims, reviews, and transcribed calls are analysed together as one corpus and clustered into themes by underlying issue.
Rank the fixes and re-measure
Themes are weighted by reach, revenue, and churn signal, handed to product with the quotes attached, then re-measured after each release.
Governance & deployment
Support AI that will not invent a policy
A support agent speaks to customers in your name, which makes a confident wrong answer more expensive than no answer. VDF constrains responses to approved content, records the article behind each reply, and keeps refunds, credits, and account changes behind a person.
Answers constrained to approved knowledge contentConfidence threshold below which it routes to a humanRefunds and account changes require explicit approvalCustomer conversation data stays on your infrastructure
FAQ
Questions about customer service agents
What are AI customer service agents?
AI customer service agents cover live resolution and feedback analysis: reading inbound messages and answering what your documentation covers, then mining the accumulated record for the themes worth fixing.
Why are resolution and feedback analysis separate agents?
They work on different clocks and carry different risk. Answering happens in seconds under a confidence threshold with a customer waiting; analysis runs across months of history with no customer in the loop and no authority to reply.
What stops it giving customers a wrong answer?
Responses are generated only from retrieved approved content with the source recorded, and any case scoring below the configured confidence threshold is handed to a person instead of answered.
Keep exploring
Related agent categories and resources
Put customer service agents to work on your own infrastructure
See these agents applied to your operations — governed, on-premise, and orchestrated together.