Volume outpaces capacity
Contact volumes grow faster than the team. Queues lengthen and service levels are missed at peak times.
AI FOR SERVICE
Service leaders are asked to lower cost and raise satisfaction at the same time. We help you decide which contacts AI should resolve and which need a person. Then we build the agents, assistants and integrations, and show that the change is working.
The problem
Demand keeps rising. Adding headcount is rarely an option, and customers notice when quality slips.
Simple questions fill the queue
Routine requests take the same route as complex ones. Skilled advisers spend their day on work that needs no judgement.
Knowledge is scattered
Answers live in documents, inboxes and the heads of experienced staff. Customers get different answers from different people.
Risk holds back automation
Leaders worry about wrong answers, tone and compliance. Without clear guardrails, pilots stay small.
Blueprint
A shared picture for service, technology and risk leaders. It makes scope and accountability clear before we build.
05
Experience
Where customers and advisers meet the AI.
- Web and app chat
- Messaging
- Voice
- Adviser console
04
AI agents and assistants
The agents and assistants that take on defined service work.
- Agentforce agents
- Adviser assistants
- Custom MCP servers
- Case automations
03
Trust and guardrails
The rules that keep answers accurate and safe.
- Approved knowledge only
- Escalation rules
- Data masking
- Audit trail
02
Data
One governed view of the customer and their history.
- Data 360
- Customer profile
- Case history
- Entitlements
01
Systems of record
The sources the AI reads from and writes to.
- Agentforce Service
- Knowledge base
- Order and billing
- Field service records
Use cases
We rank candidates by contact volume, risk and readiness of knowledge, then build the strongest first. These six come up most often.
Case triage
An AI agent classifies each new case, sets priority and routes it to the right queue. Team leads review the routing rules and any exceptions.
Self-service resolution
An agent resolves routine requests from approved knowledge and account data. It hands over to an adviser when the request falls outside policy.
Adviser assistant
An AI assistant works beside the adviser. Through a custom MCP server it can look up a case and update it under permission rules, with the adviser confirming each change.
Knowledge drafting
An agent drafts articles from resolved cases. A knowledge owner reviews and publishes each one.
Escalation summaries
An agent prepares a short summary of the case so far when it is escalated. The receiving adviser starts informed and decides the next step.
Proactive outreach
An agent spots known issues affecting a customer and drafts a notice before they call. A service manager approves the message and the audience.
Workflow
One example of how a routine request is resolved, with a person in control of the exception.
- 01
Customer contact
A customer reports a faulty product in chat.
- 02
Entitlement check
The order, warranty and case history are confirmed.
- 03
Service agent
Diagnoses the fault and proposes a replacement within policy.
- 04
Team lead
Approves the exception that sits outside standard policy.
- 05
Case resolved
Replacement is confirmed and the case is closed.
Illustrative views
Sample screens with sample values. They show the kind of evidence a service leader should expect each week.
Service AI performance
Sample quarter
Cases resolved by AI agent
46%
Average time to resolution
4.2 hrs
Customer satisfaction
4.5 / 5
Escalations to advisers
18%
Cases resolved without adviser effort
- Order and billing queries82
- Product troubleshooting61
- Returns and replacements57
- Appointment changes44
Self-service resolution
Web chat
Customer
My heating controller stopped responding this morning. It is only a few months old.
Service agent
Sorry about that. I can see the order and it is under warranty. I have run the remote check and the unit is not responding, so a replacement is the right step.
Customer
Thank you. Could it be sent to my work address? I am away from home this week.
System. Address differs from the account record. Approval requested from the team lead.
Team lead
Approved after an identity check. The replacement will go to the work address and you will get tracking details by email.
- Verified customer identity
- Checked entitlement
- Ran remote diagnostic
- Requested address approval
Both views are illustrative. Values are samples, not client results.
Outcomes
Cost and quality are measured together. We agree the baselines with you first and report against them openly.
Targets are set with each client against their own baseline.
Case deflection rate
The share of contacts resolved by AI without adviser effort.
Time to resolution
How long a customer waits from first contact to a resolved case.
First contact resolution
The proportion of cases closed in a single interaction.
Customer satisfaction
How customers rate the service they receive, across AI and adviser contacts.
Cost to serve
The average cost of handling a contact across all channels.
Time to production
How long it takes to move from decision to a governed AI agent handling real cases.
Approach
We advise and we build. Strategy and the business case come first. Design, build, launch and measurement follow.
01
Discover
We analyse contact reasons, knowledge quality and channel mix to find where AI can safely take on work.
Outputs
- Contact demand analysis
- Knowledge readiness review
- Opportunity longlist
02
Define
We prioritise use cases by volume and risk, and set out the business case with cost and quality measures side by side.
Outputs
- Prioritised use cases
- Business case
- Success measures
03
Design
We design and build the agents and assistants, the escalation paths and the limits on what AI may resolve alone.
Outputs
- Solution design
- Working agents and assistants
- Escalation and guardrail rules
04
Direct
We launch in stages with your service teams, monitor answer quality and measure value against the business case.
Outputs
- Staged launch plan
- Quality monitoring
- Value tracking
Start with a conversation. We will tell you plainly whether there is a case.