Knowledge-Based Resolution
Uses internal documentation and support knowledge to answer accurately.
Use case ยท AI for Customer Support
AI for customer support: resolve the repetitive 80% and escalate the rest cleanly.
The problem
Most support teams answer the same handful of questions hundreds of times a day. Hiring more agents doesn't scale, and traditional chatbots make customers angrier — they read the FAQ back at you and route you to a human anyway.
The solution
We build assistants trained on your real product documentation and your historical resolved tickets. They give a useful answer the first time. When they can't, they escalate with the full context attached so the human agent isn't starting from zero.
The platform combines product documentation, historical ticket data and organisational knowledge to provide accurate and contextual responses. When a query exceeds predefined confidence thresholds or requires human judgement, the system escalates seamlessly with all relevant context attached.
The objective is not to replace support teams, but to remove repetitive work and improve customer experience.
Workflow
From Customer Query to Resolution
How an incoming question flows through the platform — resolved automatically when confidence is high, or escalated cleanly to a human with context attached.
Customer question
Captured from chat, email, portal or ticket form
Knowledge search
Docs, policies and past tickets searched in real time
AI resolution engine
Generates a contextual answer using verified sources
Response generated
Draft prepared with citations and recommended actions
Confidence check
Scored against thresholds and risk policy
Resolve or escalate
Auto-resolved if confident — routed to a human if not
Customer resolution
Outcome recorded, CSAT captured, knowledge updated
Typical platform capabilities
What a production-grade AI customer support platform looks like once it is deployed and running alongside your support team.
Historical Ticket Intelligence
Learns from previous successful resolutions to improve over time.
Smart Escalation
Automatically routes complex cases to the right team based on topic and priority.
Context Preservation
Transfers the full customer history and AI summary at the moment of escalation.
Agent Assist
Provides suggested responses, macros and source links to support agents in real time.
Analytics & Monitoring
Tracks resolution rates, response times and customer satisfaction across channels.
Before vs after
Traditional support
AI-augmented support
What changes day-to-day
Same volume of incoming tickets. A very different shape of the support team's day.
Queue, review, repeat
- Customer query arrives
- ↓ Joins the queue
- ↓ Agent review
- ↓ Manual documentation search
- ↓ Response drafted
- ↓ Follow-up exchange
- ↓ Resolution — eventually
Instant resolution or clean escalation
- Customer query arrives
- ↓ AI analysis against knowledge and ticket history
- ↓ Instant resolution — or —
- ↓ Intelligent escalation to the right team
- ↓ Full context attached automatically
- ↓ Faster, more consistent resolution
The outcome
The vast majority of repetitive queries get resolved without a human. Response times drop from hours to seconds. Human agents focus on the genuinely complex tickets — and customer satisfaction usually goes up, not down.
Faster response times
Routine issues resolved in seconds rather than hours.
Reduced ticket volume
Repetitive queries handled automatically, freeing the team for complex work.
Better agent productivity
Human agents focus on the genuinely complex tickets that need their judgement.
Improved customer experience
Faster and more consistent support across every channel.
Production AI
From Support Bot to Production Support Platform
Many organisations deploy simple chatbots that answer a handful of questions. The real challenge is creating a support system that integrates with documentation, ticketing platforms, escalation processes and operational workflows.
We help organisations design, deploy, host and support AI-powered customer support solutions that operate successfully in production — not just in demos.
Azure Hosting
AWS Hosting
Ticketing Integrations
Monitoring
Security
Managed Support
Architecture
Typical deployment environment
A layered architecture — from the knowledge base and ticketing platform through to monitored, managed AI in production.
Knowledge base
Policies, guides and product documentation kept up to date and searchable.
Ticketing platform
Zendesk, Freshdesk, ServiceNow or a similar system of record.
AI resolution engine
Query analysis, retrieval and response generation with confidence scoring.
Escalation layer
Routing rules, ownership and structured context transfer to the right human team.
Support team
Human agents handling complex issues, supported by AI suggestions and history.
Monitoring & optimisation
Performance tracking, CSAT analysis and ongoing tuning of the resolution engine.
Related service
This use case is delivered through our /chatbot development work. See also our full services list and other use cases.
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