Agent assistance
Put policy search, customer history and suggested next steps alongside the existing helpdesk workflow. Keep supporting sources available for inspection.
Build / Better support starts behind the reply
AI customer service & support.
Connect AI to the knowledge and systems your support team uses. We build agent assistance, triage, customer summaries and response workflows that help people handle enquiries with the right context.
Discuss this with usThe opportunity
Help your team get to the right answer.
Support teams lose time searching for policy details, reading long conversations and switching between customer records. A faster draft is only useful if it reflects the right customer, the current policy and what the team is allowed to do.
Start with one queue or task and a baseline for handling time, quality and escalations. We connect approved knowledge and relevant customer context, then agree which actions remain with the agent. Evaluation includes difficult questions and cases where the system should ask for help.
What we can do.
Triage & conversation summaries
Classify enquiries, identify missing information and prepare concise case summaries. Route work using agreed categories and escalation rules.
Grounded responses & handoff
Draft responses from approved content and context. For customer-facing automation, define the allowed topics, authentication needs and clear routes back to a person.
Tangible outputs
What you receive.
The exact scope is agreed around your workflow. A typical engagement can include:
- One clearly scoped support workflow
- Knowledge and helpdesk integrations
- Response, handoff and escalation rules
- Quality evaluations using representative enquiries
- A dashboard specification for workload and service quality
Illustrative workflow
A customer asks about an unresolved request
Retrieve the case → summarise previous contact → find the relevant policy → suggest a response → the agent reviews and replies.
An example of how this capability can fit a business process.Define what better looks like.
We agree useful measures before implementation and compare them with the current workflow.
- ↗Handling time alongside answer quality
- ↗Rework, repeat contact and escalation rates
- ↗Agent adoption and customer experience
Your questions, answered.
Will this replace our helpdesk?
It can work with your existing helpdesk if suitable integration options are available. We begin by understanding where your team works today.
Can we start with staff-only assistance?
Yes. An internal assistant or summary tool can be a useful first scope before deciding whether to automate any customer-facing responses.
What if an answer is uncertain?
Define a fallback: request clarification, show that no supporting source was found, or hand the enquiry to a person. Test that behaviour before release.
Useful before you start
Questions worth exploring.
AI customer service starts before the answer
The most useful first improvement may be finding context, summarising a case or helping an agent decide what to do next.
How to choose your first AI project
Start with work you can observe, data you can access and an outcome you can measure.
From AI pilot to production: what changes?
A working demonstration is a starting point. A released service also needs integration, ownership and a dependable response to failure.
A useful next step
Bring us the work
that should work better.
One process, one product idea, or a question about where to start.