Solution / Business use case
Resolve the routine. Give people the hard cases with context.
Faster, more consistent service: AI triage, agent assist and self-service that hands over cleanly.
Customer Support & CX
/ 01Challenge
What gets in the way today
Support teams face rising volumes across more channels while customers expect instant, accurate answers. Agents spend much of their time searching for information, re-reading history and writing similar responses.
Self-service bots have often made things worse: they deflect without resolving, and customers arrive at a human more frustrated than before.
/ 02AI workflow
How the work flows with AI in it
Steps marked human are decision points owned by your people.
- 01
Understand
Incoming messages are classified by intent, urgency, language and sentiment, with account context attached.
- 02
Ground
Relevant policies, product documentation and similar resolved tickets are retrieved.
- 03
Resolve or draft
Routine requests are resolved through scoped actions; other cases get a drafted reply with sources.
- 04human
Review
Agents edit and approve drafts; uncertain or sensitive cases are escalated automatically.
- 05
Learn
Edits, transfer reasons and quality reviews feed the evaluation set and content fixes.
/ 03Example architecture
Signals in, governed outcomes out
A typical reference design. Your version depends on your systems, data and risk appetite.
- Email, chat and messaging
- Ticket history
- Knowledge base and policies
- Order and account systems
- Intent classification
- Hybrid retrieval
- Response drafting
- Scoped action calls
- Confidence thresholds
- Sensitive-topic escalation
- PII redaction in logs
- Agent approval of drafts
- Resolved requests
- Approved replies
- Routed tickets with summaries
- Quality analytics
/ 04Benefits
What changes, and how you would measure it
Shorter handling time
Agents start from a grounded draft and summary instead of a blank screen.
Consistent answers
Responses draw on the same approved sources, reducing variation between agents.
Better self-service
Customers get real resolution for routine requests and a clean hand-off when needed.
Measures we would agree with you
- Average handling time
- First-contact resolution
- Containment with satisfaction, not deflection alone
- Draft acceptance and edit rate
- Escalation accuracy
We do not quote generic improvement figures. Targets are set from your measured baseline.
Limitations to plan for
- Results depend on knowledge base quality; gaps in documentation show up quickly as poor drafts.
- Complaints, vulnerable customers and regulated advice should remain with trained people.
- Containment targets must be balanced against satisfaction so the assistant does not trap customers.
/ 05Product and service match
What we would use
Product concepts
- Product conceptConversational & Voice AIChat and voice assistants that resolve routine requests and hand over cleanly to people.
- Product conceptEnterprise Knowledge CopilotGrounded answers from your own documents and systems, with sources, permissions and clear uncertainty.
- Product conceptAI Agent WorkspaceA governed workspace where AI agents take on multi-step work with people approving what matters.
Services
- Service / buildEnterprise RAG & Knowledge AIRetrieval-augmented systems that answer from your content with citations, permissions and measured quality.
- Service / buildAgentic AI & Multi-Agent SystemsAgents that use tools to complete multi-step work, engineered with limits, approvals and evaluation.
- Service / operateEvaluation, Guardrails & ObservabilityKnow how your AI systems behave: test suites, runtime guardrails, tracing and quality monitoring.
/ 07FAQ
Questions buyers ask
Will AI replace our support agents?
In our designs AI handles routine resolution and preparation, while agents focus on complex, sensitive and high-value conversations. Staffing decisions remain yours and should follow measured results.
How quickly can we pilot this?
An agent-assist pilot on one queue is usually the fastest start because it keeps a person in control of every reply while quality is measured.
Explore AI for customer support & cx
Bring a sample of the work: tickets, documents, requests. We will show you what is realistic and how we would measure it.