Hamming AI provides an automated voice agent testing platform that simulates thousands of concurrent calls to evaluate AI voice agents' performance and identify issues in real-time. This technology enables teams to enhance call quality and reliability, significantly reducing the time and cost associated with manual testing processes.
Funding
$4.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
AGOTFounders
Product
Problem
Testing AI voice agents is challenging, time-consuming, and often relies on limited manual testing, making it difficult to identify potential issues before deployment. Small changes to prompts, function calls, or model providers can significantly impact call quality, leading to unreliable performance in production environments.
Solution
Hamming AI offers an automated voice agent testing and analytics platform that simulates thousands of concurrent calls to evaluate AI voice agent performance and identify bugs. The platform provides tools for prompt management, optimization, and a playground for testing LLM outputs, enabling efficient prompt engineering. It actively tracks and scores user interactions in production, flagging critical issues using LLM judges and facilitating the conversion of calls and traces into test cases. This allows teams to proactively improve call quality, ensure reliability, and reduce the time and cost associated with manual testing.
Target Audience
Hamming AI targets AI engineers, product managers, AI researchers, and domain experts building AI voice agents in industries where accuracy and reliability are critical, such as healthcare, finance, and customer service.
Features
- Automated voice agent testing using simulated voice characters to create thousands of concurrent phone calls
- Prompt management for storing, versioning, and syncing prompts with voice infrastructure providers
- Prompt optimizer and playground for automated prompt generation and LLM output testing with quality scoring
- Production call analytics to track and score user interactions, flagging issues with LLM judges
- Python and Typescript SDKs for integration with existing LLM frameworks and voice infrastructure
- Support for testing voice and conversational aspects, including handling pauses, interruptions, background noise, and complex personas
- Customizable metrics for measuring voice and conversational quality, with the ability to define bespoke metrics
- HIPAA compliance and on-premise deployment options for enterprises requiring greater control over security and data handling