Step 1: Multi-Language Test Scenario Creation
We developed comprehensive test scenarios in multiple languages and accents, ensuring coverage of diverse customer interactions and complex requests.
Hamming AI has raised $3.8M in Seed funding to make AI voice agents more reliable.
Podium, a global provider of 24/7 AI-driven customer support, leverages Hamming to ensure their voice agents provide reliable service across multiple languages and high-traffic periods.
Prior attempts at multi-language support often struggled with consistency and scalability. With Hamming, Podium tests and refines their voice agents across thousands of scenarios, ensuring they handle diverse languages, varying accents, and peak traffic periods with precision.
Director of Engineering @ Podium
Jordan Farnworth, Director of Engineering, Podium
We rely on our AI agents to drive revenue, and Hamming ensures they perform without errors. Hamming's load testing gives us the confidence to deploy our voice agents even during high-traffic campaigns.
Hamming's AI Voice Agent A/B testing makes it easy for us to test new AI voice agent providers and roll out the best performing AI voice agent to our customers.
We developed comprehensive test scenarios in multiple languages and accents, ensuring coverage of diverse customer interactions and complex requests.
Our platform simulated thousands of concurrent calls across different languages, measuring response times, accuracy, and system stability under various load conditions.
We implemented real-time monitoring to track performance metrics and identify potential issues before they impact customer experience.
Our voice agents are trained to mimic human conversations, including accents, background noise, and pauses.
For each call, we automatically score the call quality and provide detailed analytics.
Our AI voice agents can call your agents in any language, including English, French, German, Hindi, Spanish, Italian, and more.
Concurrent calls tested daily across multiple languages
Response accuracy maintained during peak loads
Continuous monitoring and optimization