All case studies

Service provider, 50,000+ customer accounts

A High-Volume Service Provider: A 12-Agent AI System for 5,000+ Monthly Support Inquiries

A service provider with 50,000+ customer accounts replaced manual ticket triage with a 12-agent AI support system. Most cases now resolve in under four hours instead of days.

• 5-7 days to under 4 hours Resolution time (80% of cases)
• 96-98% Accuracy across AI decisions
• 400-600 to 2,000+ Inquiries handled per day
• 52-58% Reduction in cost per interaction
• 40-45% Reduction in FTE effort
• 58% to 82-85% Customer satisfaction (CSAT)
• +25-30 points Net Promoter Score
• 22-28% Increase in agent satisfaction
• 100% Audit compliance, with 87% fewer violations

The problem

A mid-to-large service provider with more than 50,000 customer accounts and over 5,000 inquiries a month was triaging tickets by hand. Limited access to knowledge slowed resolution, context was lost across channels, escalation was inefficient, and there was no real-time monitoring. The workload was burning out agents, and gaps in compliance and audit created regulatory risk.

• Manual ticket triage consumed excessive resources
• Limited knowledge access increased resolution time
• Context lost across channels, with no awareness of customer sentiment
• No real-time monitoring, leading to high workload and agent burnout
• Compliance and audit gaps created regulatory risk

What we built

We built a 12-agent AI support system. A chatbot agent receives each inquiry and an orchestrator directs it through specialised agents: an analysing agent assesses complexity and context, a decision agent chooses the workflow, and knowledge base retrieval and real-time search supply answers. Warranty, pricing and validation agents apply policy, generate quotes and check them, while an audit agent monitors compliance across every workflow. Quality assurance dashboards track interactions and customer feedback is collected for continuous improvement.

Tools: Multi-agent AI system (12 agents), Chatbot agent, Orchestrator, Knowledge base database, Natural language processing, Intelligent routing

How we ran it

Every engagement follows The Claro Build Framework.

1. Assess: We examined how inquiries were triaged, escalated and audited, and identified manual triage, context loss across channels, missing real-time monitoring and audit gaps as the core problems.
2. Design: We designed a 12-agent architecture with a chatbot entry point, an orchestrator, a shared knowledge base and dedicated agents for analysis, decisions, warranty policy, pricing, validation and audit.
3. Build: We built the agents and routing logic, connected knowledge retrieval and real-time search, and set up quality assurance dashboards and customer feedback collection.
4. Sustain: As with every build, handover followed The Adoption Standard: a structured 45-minute handover call and documentation written as the system was built.

The result

Resolution time moved from five to seven days to under four hours for 80 per cent of cases, with 96 to 98 per cent accuracy across AI decisions. Daily capacity rose from 400 to 600 inquiries to more than 2,000, cost per interaction fell by 52 to 58 per cent, and the provider reports 100 per cent audit compliance with an 87 per cent reduction in violations. Customer satisfaction rose from 58 per cent to between 82 and 85 per cent.

Client name withheld for confidentiality.