Before switching video SDKs, upgrading to a new version or renewing a vendor contract, engineering leaders want to know how their call quality compares with competing products and where it falls short. WebRTC benchmarking answers that by running each video calling product through identical network limits, such
AI-driven QA testing is reshaping how teams validate real-time applications. Doing it well requires intentional processes, shared knowledge, and a collaborative culture that allows teams to use AI responsibly and consistently. Our WebRTC.ventures QA team has approached this with a clear mindset: the real value of AI
Testing an AI voice agent is nothing like testing a standard application. You’re validating a live, real-time pipeline where WebRTC audio streaming, speech-to-text, LLM reasoning, and text-to-speech synthesis work together within milliseconds, every time a user speaks. Traditional QA processes and frameworks weren’t built for this. They
Expert WebRTC testing is what separates functional real-time applications from reliable ones. A platform that works for two developers on the same high-speed office network can quickly fall apart when hundreds of users try to join simultaneously from coffee shops, firewalled corporate networks, and crowded classrooms. While
