How to Automate Your L1 Support with GoZupees

Identify which call types consume the most agent time and are suitable for AI automation.

• Analyze call recordings and categorize by intent (billing, troubleshooting, outage, etc.)
• Calculate Average Handle Time (AHT) for each call type
• Identify calls with high volume but predictable resolution paths
• Document current first-contact resolution (FCR) rates by category
• Estimate monthly call volume for each automation candidate

Document the systems your AI agent needs to access to resolve customer issues.

• Inventory your OSS/BSS systems (billing, provisioning, ticketing)
• Document API availability for each system (REST, SOAP, or database)
• Identify authentication methods and access requirements
• Map data flows: what info does an agent need to resolve each call type?
• List required actions: payments, ticket creation, status lookups, scheduling

Start with one high-impact, low-complexity use case to prove ROI quickly.

• Best first candidates: after-hours support, billing inquiries, outage notifications
• Avoid complex troubleshooting for first deployment
• Choose use case with clear success metrics (calls deflected, time saved)
• Ensure backend integrations are available or can be built quickly
• Define escalation criteria: when should AI hand off to human?

Create the dialog trees and response templates your AI agent will use.

• Define greeting, authentication, and intent recognition prompts
• Script responses for common questions using actual agent language
• Map decision points: if customer says X, agent does Y
• Design error handling: what happens when info isn't available?
• Create handoff scripts for escalation to human agents

Connect your AI agent to the backend systems it needs to resolve calls.

• Configure API connections to billing, CRM, and ticketing systems
• Set up secure authentication (OAuth, API keys, certificates)
• Build data retrieval functions: customer lookup, account status, etc.
• Create action functions: update tickets, send SMS, schedule callbacks
• Test integrations in staging environment with sample data

Connect your AI agent to your phone system to receive and make calls.

• Choose integration method: SIP trunk, call forwarding, or API
• Configure phone numbers: new DID or route from existing IVR
• Set up call recording and transcription
• Configure business hours and after-hours routing
• Test call quality, latency, and failover scenarios

Feed the AI with examples and test it extensively before going live.

• Provide 50-100 example conversations for each intent
• Test with variations: accents, phrasing, edge cases
• Run load tests to ensure system handles peak volume
• Conduct UAT with internal team members posing as customers
• Document issues and iterate on conversation flows

Go live with a controlled rollout to measure real-world performance.

• Start with 10-20% of call volume for the selected use case
• Monitor calls in real-time for first 48 hours
• Track key metrics: resolution rate, customer satisfaction, escalation rate
• Collect caller feedback via post-call surveys
• Make rapid adjustments based on observed issues

Measure pilot performance against baseline and identify improvements.

• Compare AI resolution rate vs. human agent baseline
• Calculate cost savings: (calls handled × agent hourly rate × AHT)
• Review escalated calls to identify gaps in AI capability
• Analyze customer satisfaction scores for AI vs. human interactions
• Document lessons learned for next use case rollout

Expand AI coverage based on pilot success and add new use cases.

• Increase AI call volume to 100% for proven use case
• Add next use case using lessons from first deployment
• Implement VerSight analytics for ongoing quality monitoring
• Consider VerSense Co-Pilot for calls that still require human agents
• Establish continuous improvement process for AI agent updates