What's your typical implementation process?

Every Call Stream AI implementation is customized, meaning no two setups are exactly alike. However, most implementations follow a standard process outlined below:

Week 1-2: Discovery & Planning

1. Kick-off Meeting: Introduction of project teams, confirmation of goals, timelines, and communication protocols
2. Requirements Gathering: Detailed collection of call flows, business rules, FAQs, and knowledge base requirements
3. Technical Assessment: Evaluation of existing systems, integrations needed, and data connections
4. Data Collection: Aggregation of historical call data, customer interactions, and common scenarios
5. Implementation Plan: Development of detailed project plan with milestones, dependencies, and responsibilities

Week 3-4: Development & Initial Configuration

1. Knowledge Base Creation: Building and populating the AI knowledge foundation
2. Voice Persona Development: Selection and customization of voice characteristics and conversational style
3. Call Flow Mapping: Designing conversation trees and decision points
4. Integration Setup: Establishing connections with required systems (CRM, POS, etc.)
5. Initial Configuration: Base setup of the Call Stream AI platform for your specific business needs

Week 5-6: Training & Testing

1. AI Training: Teaching the system through your historical data and specific scenarios
2. Test Script Development: Creation of comprehensive test cases covering common and edge scenarios
3. Internal QA Testing: Rigorous testing by Call Stream AI quality assurance specialists
4. Client User Acceptance Testing (UAT): Supervised testing by client team members
5. Performance Benchmarking: Establishing baseline metrics for system performance
6. Refinement: Iterative improvements based on testing feedback

Week 7-8: Deployment & Launch

1. Final Adjustments: Implementation of changes from UAT feedback
2. User Authorization: Formal client sign-off on the AI bot capabilities and performance
3. Training: Client team training on management interface and reporting tools
4. Soft Launch: Limited deployment to a controlled subset of calls/interactions
5. Performance Monitoring: Close observation of initial live interactions
6. Full Deployment: Complete system rollout
7. Handover: Transition to ongoing support and management structure

Post-Launch Support

1. 30-Day Hypercare: Intensive support period with rapid response to any issues
2. Performance Reviews: Weekly reviews of system performance against KPIs
3. Continuous Learning: Ongoing AI model improvement and knowledge base updates
4. Quarterly Business Reviews: Strategic assessment of performance and future enhancements
5. Scaling Support: Assistance with expanding to additional departments or use cases