Voice AI assistants have become ubiquitous in modern customer service, but testing them effectively remains a significant challenge for developers and operations teams. Every voice bot developer knows the frustration of manually placing dozens of test calls to verify their bot’s behavior, responses, and call flow logic. This manual testing approach is not only time-consuming but also prone to human error and inconsistency.
Today, with OpenAI’s Realtime API becoming generally available, at Sipfront we are setting out to solve this challenge by launching our own AI-powered voice bots that can autonomously test both inbound and outbound voice assistants. This new feature eliminates the need for manual testing while providing comprehensive, consistent, and scalable testing capabilities.
The Immediate Value of Automated Voice Bot Testing
Eliminating Manual Testing Overhead
Traditional voice bot testing requires developers after each prompt or code change to:
- Manually dial test numbers repeatedly
- Speak the same test phrases dozens of times
- Monitor call quality and response accuracy
- Document results manually
This process is not only tedious but also expensive in terms of developer time and inconsistent in execution. Human testers may vary their speaking pace, pronunciation, or even forget to test specific scenarios. Automated testing eliminates these variables while providing 24/7 testing availability.
Comprehensive Test Coverage
Sipfront AI voice bots can execute hundreds of test scenarios in the time it takes a human to complete a single call. This includes:
- Testing various user intents and utterances
- Validating call flow logic across different conversation paths
- Stress testing with rapid-fire interactions
- Testing edge cases and error conditions
- Measuring response times and call quality metrics
The result is higher quality voice bots that have been thoroughly tested across a comprehensive range of scenarios, leading to better customer experiences and reduced support costs.
Technical Implementation: Building SIP Clients with AI Backends
The Core Challenge: Real-Time Audio Processing
Implementing voice bots that can both make and receive SIP calls while maintaining natural conversation flow presents several technical challenges:
- Real-time audio capture and streaming from SIP calls
- Seamless integration between telephony infrastructure and AI services
- Low-latency processing to maintain natural conversation pace
- Bidirectional audio handling for both inbound and outbound scenarios
Implementing our own AI voice bots however helped us to experience the challenges first hand, that both Voice AI bot developers face when implementing the technical core of such systems, and system engineers deploying and prompting those bots to make sure they behave properly and within their scope.
Architecture Overview
Our solution leverages our existing baresip SIP client foundation used for all our SIP test calls, enhanced with a custom-built audio driver that interfaces with OpenAI’s Realtime API. The architecture consists of three main components:
SIP Call ↔ Baresip Client ↔ Custom Audio Driver ↔ OpenAI Realtime API
Implementing the Virtual Audio Device
The heart of our implementation is a virtual audio device driver that acts as a bridge between the RTP audio stream from the SIP call, and OpenAI’s realtime processing pipeline. This driver:
- captures audio from incoming SIP calls in real-time
- streams audio chunks to OpenAI’s Realtime API for processing
- receives AI-generated audio responses and injects them back into the call
Audio Pipeline
Baresip provides a flexible audio pipeline with several key audio processing modules out of the box.
audio tx pipeline: openai_rt —> aubuf —> auresamp —> sndfile —> mixminus —> PCMU audio rx pipeline: openai_rt <— aubuf <— auresamp <— sndfile <— mixminus <— PCMU
The key concept is that we don’t wait for complete sentences or user silence before processing. Instead, we stream audio continuously, allowing OpenAI’s API to handle voice activity detection and end-of-utterance detection automatically. This approach minimizes latency and creates more natural conversation flow.
- Streaming responses that can be converted to speech as they’re generated
- Built-in voice activity detection that eliminates the need for custom VAD algorithms
- Automatic speech-to-text with real-time transcription
- Context-aware responses that maintain conversation state
Our implementation maintains a persistent connection to the Realtime API, allowing for seamless conversation flow without the overhead of establishing new connections for each turn.
Handling Both Inbound and Outbound Scenarios
Inbound Call Testing (Called Party Simulation)
When testing inbound voice bots, our AI agents act as calling parties who initiate conversations with customer voice bots. This allows customers to:
- Test their bot’s greeting and initial response logic
- Verify call routing and queue management
- Test various user intents and conversation flows
- Measure response times and call quality
The AI agent can simulate different types of callers:
- Frustrated customers who need immediate assistance
- Technical users who ask complex questions
- Non-native speakers with various accents
- Users with specific use cases relevant to the business
Outbound Call Testing (Calling Party Simulation)
For outbound call testing, our AI agents act as called parties who receive calls from customer voice bots. This enables testing of:
- Outbound call initiation and dialing logic
- Answer detection and greeting responses
- Call completion and hangup handling
- Retry logic for failed calls
This bidirectional capability is particularly valuable for call center automation and outbound marketing campaigns where voice bots need to handle both incoming and outgoing calls seamlessly.
Audio Quality and Codec Handling
Voice bot testing requires handling various audio codecs and quality levels. Our implementation supports:
- G.711 (PCMU/PCMA) for traditional telephony compatibility
- Opus for high-quality VoIP connections
- AMR for mobile network compatibility
- Automatic transcoding between different formats
The virtual audio device handles codec conversion transparently, ensuring that audio quality is maintained throughout the testing process while providing realistic testing conditions.
Conversation State Management
Maintaining conversation context across multiple turns is crucial for realistic testing. Our implementation:
- Tracks conversation history for context-aware responses
- Manages user intent across multiple exchanges
- Handles conversation flow and branching logic
- Simulates realistic user behavior including interruptions and topic changes
This allows the AI agent to engage in multi-turn conversations that test the voice bot’s ability to maintain context and handle complex user interactions.
Real-World Testing Scenarios
Customer Service Bot Testing
Our AI agents can simulate various customer service scenarios:
AI Agent: "Hi, I need help with my account. I'm having trouble logging in."
Voice Bot: "I'd be happy to help you with your login issue. Can you provide your account number?"
AI Agent: "Sure, it's 123456789. But actually, I also wanted to ask about my recent charges."
Voice Bot: "I can see your account. Let me help with both issues. First, let's address your login problem..."
This tests the bot’s ability to:
- Handle multiple intents in a single conversation
- Maintain context across topic changes
- Provide helpful responses without losing track of user needs
Sales and Marketing Bot Testing
For outbound call testing, our AI agents can simulate various customer responses:
Voice Bot: "Hi, this is Company X calling about your recent inquiry. Are you available to discuss our solutions?"
AI Agent: "Actually, I'm not interested anymore. I went with a competitor."
Voice Bot: "I understand. Would you be willing to share what led you to choose them instead?"
AI Agent: "They had better pricing and faster delivery. Can you match that?"
This tests the bot’s ability to:
- Handle rejection gracefully
- Gather competitive intelligence
- Adapt to customer feedback
- Maintain engagement despite initial resistance
Performance and Scalability
Concurrent Call Testing
Our infrastructure can handle multiple concurrent test calls, allowing for:
- Load testing of voice bot systems
- Parallel testing of different scenarios
- Stress testing under realistic conditions
- Performance benchmarking across various configurations
Automated Test Execution
Tests can be scheduled and executed automatically:
- Continuous integration testing after code changes
- Regression testing to ensure new features don’t break existing functionality
- Performance monitoring to detect degradation over time
- Quality assurance before production deployments
Future Enhancements and Roadmap
Advanced Testing Capabilities
We’re working on additional features including:
- Multi-language testing for international voice bots
- Emotion simulation to test bot responses to various emotional states
- Background noise injection for realistic environmental testing
- Call quality degradation simulation to test robustness
Integration with Existing Testing Frameworks
Our voice bot testing can be integrated with:
- CI/CD pipelines for automated testing
- Test management systems for comprehensive reporting
- Monitoring platforms for real-time quality metrics
- Analytics tools for detailed performance analysis
Conclusion
The launch of OpenAI’s Realtime API has opened new possibilities for automated voice bot testing. By implementing our own AI-powered testing agents, Sipfront is enabling customers to test their voice bots more thoroughly, more consistently, and more efficiently than ever before.
The combination of bidirectional call handling, real-time AI processing, and comprehensive test coverage provides a powerful foundation for ensuring voice bot quality. This not only improves the development process but also leads to better customer experiences and reduced operational costs.
As voice AI continues to evolve, having robust, automated testing capabilities will become increasingly important. Sipfront’s voice bot testing solution represents a significant step forward in this direction, providing the tools needed to build and maintain high-quality voice AI systems.
For more information about our voice bot testing capabilities or to schedule a demonstration, please contact our team. We’re excited to help you take your voice AI testing to the next level.
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