The only thing harder than building a real-time application is testing it.
All software needs to work on a variety of platforms, in different hardware and network configurations, and at various levels of user load. Testing a web or mobile video application is more complicated because it’s not as simple as a feature working or not. Real-time video and live streaming will behave differently across operating systems and browsers and you have multiple participants to handle. Bandwidth also impacts the experience significantly. And of course, you need to test the quality of the video itself, especially when they are part of Voice AI or AI voice agent experiences where latency and synchronization are critical.
Don’t leave the success of your real-time communication or Voice AI application to just anyone. Trust the QA experts at WebRTC.ventures to explore the inner workings of your application and identify potential break points before your users find it for you. Whether you are shipping a WebRTC app, an interactive video experience, or an AI voice agent, we can help you thoroughly test it first, even if we didn’t build it.
Why choose WebRTC.ventures for testing?
We attach great importance to software testing – we’ve even dedicated a whole office to it in Panama City, Panama with a regularly updated device lab with a wide range of medium- and high-end Android and iOS cell phones, tablets, and computers with Windows and macOS operating systems.
In addition, we incorporate embedded and edge devices such as Raspberry Pi and similar hardware into our testing environment to support IoT testing, edge computing validation, and real-world network simulation.
We also leverage leading cloud-based and specialized QA tools, including BrowserStack for cross-browser and cross-device testing, Loadero for scalable WebRTC and real-time performance testing, and tools such as Postman, Apache JMeter, and Fiddler for API testing, load and stress testing, and network traffic analysis, along with many other tools tailored to each project’s specific requirements
Testing is not as simple as buying a single tool or adopting a single methodology. It requires layering a variety of techniques, as well as expertise that most teams don’t have. Our amazing QA team works with live video and live streaming applications all the time and can provide the specific expertise to test any real-time application.
More than half of our software testers are ISTQB certified.
ISTQB is the leading global certification scheme in the field of software testing. It has given more than 1.1 million exams and issued more than 836,00 certifications in over 130 countries.
AI Voice Agent Testing
Testing an AI voice agent is nothing like testing a standard web app. You are validating an end‑to‑end conversational pipeline where WebRTC audio transport, STT, LLM reasoning, and TTS synthesis all contribute to perceived latency and conversation quality.”
We design tests that validate audio integrity, transcription accuracy, contextual correctness of responses, interruption handling, and overall response timing – not just whether an API call returned a 200.
Our AI voice agent testing covers:
- Controlled conversation simulations to establish latency baselines
- Multi‑user scenarios to validate turn‑taking and barge‑in behavior
- Environmental tests across devices, networks, and background noise
- Load tests that stress media servers, STT/TTS providers, and LLM backends
- Production monitoring setup using WebRTC‑level analytics (e.g., Peermetrics) and observability tools
We use AI-assisted QA workflows to analyze conversations, logs, and system behavior at scale, allowing us to quickly detect response inconsistencies, latency spikes, and hidden edge cases that traditional testing often misses.
Our internal QA systems, including project intelligence, shared testing frameworks, and structured AI prompting, ensure every test is consistent, repeatable, and aligned with real-world usage.
The result: faster insights, fewer production issues, and a more reliable AI voice experience for your users.
We bring together WebRTC engineering, voice AI integration, and production‑grade QA in a single practice. For a deeper dive into how we test voicebots, see our guide: QA Testing for AI Voice Agents: A Real‑Time Communication QA Framework.
Observability and WebRTC Analytics
For real‑time video, Voice AI, and AI voice agent experiences, good QA is impossible without good observability. You need to see what actually happened on the wire and in the media stack when a user says, “the call felt slow” or “the bot sounded choppy,” not just whether a backend request returned 200.
Our team instruments your application to collect WebRTC getStats data and other key metrics in a structured, repeatable way. We track packet loss, jitter, bitrate, round‑trip time, connection setup time, and more, then correlate those with your application logs and user feedback. This turns vague complaints into concrete answers like “Safari users on cellular in region X are hitting 3% packet loss during peak hours” and clear actions for your engineering team.
As part of this, we can deploy and integrate Peermetrics, the open‑source WebRTC analytics platform now maintained by WebRTC.ventures. Peermetrics provides dashboards, call detail views, and timelines that make it easier to debug failed connections, analyze call quality trends, and monitor KPIs such as call success rate, average call duration, and media reliability over time.
When we combine expert QA, WebRTC‑aware test design, and a purpose‑built observability stack like Peermetrics, you gain end‑to‑end visibility: from a single failing AI voice agent call all the way up to fleet‑wide quality trends across browsers, devices, and regions.
What questions can good testing answer?
- Does my application perform fast and reliably?
- Does it work consistently across browsers, devices, and operating systems?
- How well does it perform on mobile in real-world conditions?
- What happens when network quality drops or becomes unstable?
- How many users or calls can the system handle without issues?
- Will it scale smoothly as usage grows?
- Does our AI voice agent feel natural and responsive to users?
- Can our agent handle interruptions and real conversation scenarios without breaking?
- Do we have the observability to explain why a given call felt ‘slow’ or ‘glitchy’ to a user?
- Are there specific devices, browsers, or regions where performance drops?
What kind of testing does WebRTC.ventures offer?
Level 1: Manual Testing
Our manual testers have access to a lab of different mobile devices and computers so that they can test applications in a variety of different browsers and operating systems. We follow test scripts we develop with you. Manual testing is particularly important for WebRTC applications as the commonly used testing tools for regular web applications do not generally accommodate video call testing. Manual testing may be done independently or in parallel to the other testing layers.
For AI voice agents, our manual testers run scripted and exploratory conversations to evaluate perceived latency, barge‑in handling, and audio artifacts that automation alone cannot catch.
Level 2: Exploratory and Use Cases
This is the default type of testing we apply to our development clients, where we dedicate a tester to your project team so that they get to know your specific use case and application features. This allows them to do exploratory manual testing, write test cases, and look for the issues developers may have missed. Because of their intimate knowledge of your product, these Level 2 testers can also develop the test scripts to be used by other testing layers.
On AI voice agent projects, Level 2 testers become familiar with your voice agent’s domain so they can design realistic conversation flows, edge‑case prompts, and failure scenarios that reveal where the conversational experience breaks down.
Level 3: Test Automation
Level 3 Test Engineers are part tester, part developer, and part DevOps engineer. They automate reliability into your system by producing test automation scripts and continuous development environments that allow an automated suite of tests to run against your application in a production-like environment. The scripts are written using GUI level automation tools such as Selenium so that they can be based on scripts provided by Level 2 Testers, and will automate “happy paths” and multiple scenarios across your application.
For Voice AI, Level 3 engineers integrate with your simulation or call‑generation tools to automate conversation scenarios, capture latency and accuracy metrics, and validate regressions across STT, LLM, and TTS services.
Level 4: Load Testing and Advanced DevOps
Load testing is the only reliable way to know how far your application can scale. Our Level 4 Test Engineers provide a variety of DevOps consulting and load testing services to clients with the most demanding requirements for their production applications. These team members can assess your current architecture and recommend improvements to allow it to auto-scale as the number of users grows. To confirm system performance under load, they can also build on top of automation scripts like those developed by our Level 3 Test Engineers, and deploy those scripts to server farms to similar large numbers of calls against your application.
In AI voice architectures, Level 4 engineers stress‑test your media servers, STT/TTS providers, and AI backends under realistic traffic patterns to uncover scaling bottlenecks before they impact users.
Ongoing Support and Maintenance
Once your WebRTC or Voice AI application is in production, we can also provide ongoing managed support and monitoring so you don’t have to go it alone. Our managed services team helps keep your infrastructure stable, patches issues quickly, and scales capacity as your real‑time video and AI voice agent usage grows.
Testing FAQ: WebRTC, Real‑Time Video, and Voice AI
Our WebRTC, real‑time video, and Voice AI testing typically uncovers issues that directly affect call quality, reliability, and latency in production. We routinely find call setup and reconnection problems, audio and video quality issues, AI voice agent timing or barge‑in bugs, device and browser compatibility gaps, and scaling bottlenecks in your media servers or AI backends.
We test the full conversational experience, not just individual components. This includes evaluating response timing, transcription accuracy, contextual relevance, and how the voice agent handles interruptions or multi-turn conversations.
Yes. We design tests that introduce realistic packet loss, jitter, bandwidth constraints, and concurrent users so you can see how your WebRTC or Voice AI experience behaves under real‑world conditions.
We combine standard QA tooling with WebRTC‑aware observability, including getStats‑based metrics and open‑source platforms like Peermetrics for session‑level analytics and debugging. This helps correlate user‑reported issues with specific network, media, or AI pipeline problems.
Yes. In addition to pre-launch testing, we support ongoing monitoring and optimization in production. Using observability tools and real-time analytics, we help identify issues like call quality degradation, latency spikes, or failed sessions, and work with your team to diagnose root causes and improve performance over time.
Are you ready to work with an experienced QA team to validate the quality and performance of your WebRTC, real‑time video, and Voice AI applications?
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