to refine your agent prompts using the sectional structure [(3) ]() 4. Implement continuous testing to validate improvements [(1) ]() This is definitely buildable on your own with the right technical resources, but expect 3-4 weeks for a solid v1 rather than one week for production quality at your scale.

### Answer Building a comprehensive QA and call monitoring system for 100k+ monthly calls is absolutely achievable, though completing it in one week would be ambitious for a production-ready solution. Here's what Retell AI provides and how you can approach this: ## What Retell AI Provides Out of the Box Retell AI offers real-time call transcription and post-call summaries & analytics [(1) ]() . You can access: - Full transcripts via the Get Call API [(2) ]() - Call recordings - Latency metrics - Function call logs - Call duration and status Additionally, Retell AI provides sentiment & success-rate dashboard capabilities [(1) ]() , which can help identify coaching opportunities and successful conversation patterns. ## Building Your QA System ### Data You Can Extract From Retell AI's webhook events, you can capture: - **Call Started**: When the call connects [(2) ]() - **Call Ended**: Final status and duration - **Call Analyzed**: Transcript and analysis ready - **Call Failed**: Error details and reasons Webhooks provide real-time updates without polling, making them ideal for production systems . ### Recommended Approach Week 1-2: Foundation 1. Set up webhook integrations to capture all call data in real-time 2. Store transcripts, call outcomes, and metadata in your database 3. Use the Get Call API to backfill historical data for your 2000+ hours of calls Week 2-4: Analysis Layer For analyzing 100k+ calls monthly, you'll need cloud-based LLMs rather than local models due to scale. Modern LLMs excel at: - Identifying failure patterns in conversations - Extracting frequently asked questions - Detecting sentiment and customer satisfacti

on issues - Highlighting where agents deviated from desired behavior Prompt Engineering Integration Retell AI uses sectional prompts for better organization and LLM comprehension [(3) ]() . When you identify issues, you can update specific sections without affecting others: - Identity section: Define agent role and expertise - **Style Guardrails**: Control response length and tone - **Response Guidelines**: Handle formatting and confirmation patterns - **Task Instructions**: Specific steps the agent should follow - **Objection Handling**: Scripts for common scenarios ### Technical Stack Recommendation Cloud-based approach (recommended for your scale) : - Use GPT-4 or Claude for transcript analysis - Build a pipeline that processes call transcripts through batch API calls - Create dashboards showing failure patterns, common questions, and performance metrics **Why not local/NLP**: - Traditional NLP lacks the contextual understanding needed for nuanced conversation analysis - Local models would struggle with 100k+ monthly calls - Cloud LLMs provide better accuracy for identifying subtle agent failures and extracting actionable insights ### Realistic Timeline - **Week 1**: Basic data collection and webhook setup - **Week 2-3**: Analysis pipeline and initial insights - **Week 4+**: Iterative prompt improvements and monitoring dashboards For immediate improvements, use Retell AI's agent testing capabilities to test refined prompts before deployment [(1) ]() . This helps eliminate issues and optimize agents for your exact operational needs. ## Next Steps 1. Start collecting comprehensive call data via webhooks [(2) ]() 2. Build a simple analysis pipeline using cloud LLMs 3. Use insights

​ To learn more, see the sources I considered: [[1] AI Voice Agents in 2026: Everything Businesses Need to Know | Retell AI](https://www.retellai.com/blog/ai-voice-agents-in-2026) [[2] Outbound Calls (Make Calls)](https://docs.retellai.com/deploy/outbound-call) [[3] Prompt Engineering Guide](https://docs.retellai.com/build/prompt-engineering-guide) ​

Feel free to tag @AI Evy with additional questions. I also appreciate feedback, it helps me improve.