Enterprise video SDK integrations frequently expose critical security vulnerabilities. Client-side secrets, unvalidated webhooks, and bypassed authentication layers create attack vectors that persist in production systems. These implementations typically function correctly during development, but fail security audits and present significant risks in enterprise environments. We've built a real-time video platform architecture that addresses these fundamental security gaps. The implementation uses the Zoom Meeting SDK as the video foundation, chosen for its comprehensive enterprise feature set, embedded

If you're in the RTC community, you know that WebRTC.ventures excels at assessing, building, testing, and managing real-time communication applications. But what happens when your success leads to bigger ambitions? When you need full engineering teams, not just project-based development? When you're ready to scale beyond individual applications to building comprehensive development capabilities? This is where AgilityFeat, our parent company, comes in. From WebRTC Success to Strategic Scaling You’ve reached a pivotal moment. Your application

At WebRTC.ventures, quality testing is at the heart of everything we do. Robust API testing is the foundation of reliable performance, especially for WebRTC applications and AI integrations which depend on real-time performance and consistent connectivity. In our QA testing lab here in Panama City, Panama, we simulate real-world scenarios and network conditions to ensure APIs don't just work, but perform reliably under pressure. That's why Postman is one of our favorite tools in the

My name is Octavio Alvarado and I study Software Engineering at the Technological University of Panama, currently in my fifth year of my degree. I was part of the UTP's Professional Internship program, which allows students to join companies according to their skills and profiles. Through this program, I had the opportunity to work with the Quality Assurance and WebRTC testing team at WebRTC.ventures, an experience that has been very enriching for my professional development.

WebRTC VoIP systems enable voice calling directly through web browsers and mobile apps without requiring any software downloads or plugins. This makes them ideal for customer support platforms, telehealth consultations, sales calls, and any application where you want to add voice communication without asking people to install additional software. These systems are more flexible than traditional phone systems, but they also create operational challenges for DevOps teams. Instead of managing hardware-based PBX systems, you're now

Voice AI agents have unique deployment needs. Operational complexity multiplies quickly. You're not just deploying code; you're orchestrating real-time audio pipelines that need to maintain call quality under load, coordinate between AI services that each have their own scaling characteristics, and handle the networking complexities of audio delivery across diverse client environments. When choosing a deployment strategy, you'll find three main options, each making different trade-offs: Managed services handle all infrastructure but limit customization  Serverless

If you've worked with WebRTC applications long enough, you've probably heard these complaints:  "The video froze, but I could still hear them."  "The audio kept cutting in and out."  "The call just dropped for no reason." You check your application performance monitoring (APM) dashboard and everything looks normal. Server CPU is steady, error logs are quiet, infrastructure metrics are green. So what happened? Traditional monitoring tools were built for backend systems, not for catching the

If you’re looking to integrate Zoom into your web application, you have two main options: the Meeting SDK and the Video SDK. While both run in the browser, support popular frameworks like React, Vue, and Angular, and use JWT for authentication, they’re designed for very different purposes. Understanding the differences will help you decide which SDK fits your project best.  In this post, we will take a look at how they work and break down

In an era where artificial intelligence is transforming every aspect of customer service, Interactive Voice Response (IVR) systems remain a critical touchpoint for millions of daily interactions across call centers and customer service departments. As explored in my previous article on "Building a Smart IVR Agent System with LiveKit Voice AI", conversational AI has revolutionized the IVR space. Yet, traditional phone authentication systems have proven insecure and create friction that drives customers away, adding a

At ClueCon 2025, a premier conference for telecommunications developers and innovators, WebRTC.ventures served once again as a sponsor and myself as a speaker. Presented by our partners at SignalWire, ClueCon once again delivered a packed schedule of technical deep dives and real-time communications insights, with AI-powered innovation taking center stage even more than before.  Sitting in this year’s session, one thing was crystal clear: we’re moving beyond human-to-human calls into continuous, AI-augmented information flows that

We’re thrilled to announce that Héctor Zelaya, WebRTC.ventures Developer Advocate and AI enthusiast, has launched an El Salvador chapter of AI Tinkerers, a global initiative designed to empower local professionals in artificial intelligence. And it will have its first meetup on Saturday, August 30, 2025.  Through demos, workshops, and guest speakers, the AI Tinkerers El Salvador chapter will help grow the country’s presence in the global AI conversation. “We want to connect professionals working with

When WebRTC was first introduced by Google over a decade ago, it came with the promise of simplicity. “Just drop in a little JavaScript and you’ve got video chat in the browser with no downloads necessary!” While that vision helped kickstart a wave of innovation in real-time communication, the reality has always been more complex. Especially when building real-time communication apps at scale. At WebRTC.ventures, we regularly hear from teams who have working demos that

WebRTC is a foundational technology behind many real-time communication applications, including telehealth platforms handling sensitive health data and enterprise collaboration tools exchanging confidential business information. While WebRTC offers strong security by design, building a secure application requires more than just relying on its built-in protocols. This WebRTC Security 2025 guide explores WebRTC’s core security mechanisms, such as DTLS and SRTP, alongside the most common implementation risks and best practices. Whether you're developing a browser-based video

Voice AI applications are changing how businesses handle customer interactions and how users navigate digital interfaces. These systems process spoken requests, understand natural language, and respond with generated audio in real time. Building a voice AI application requires understanding speech processing, language models, and real-time communication infrastructure. You need systems that can detect when users finish speaking, convert audio to text, generate appropriate responses, and convert those responses back to audio. This guide covers the

AI-powered voice agents are transforming communications across industries like telecom, healthcare, and enterprise customer service. But delivering low-latency, natural-sounding AI responses in real time at low costs remains a major challenge. Leveraging the “lightweight AI” of Small Language Models (SLMs) and free open-source stacks can help overcome the latency and cost barriers of real-time voice AI. At ClueCon 2025, taking place in Chicago from August 4–7, WebRTC.ventures CTO Alberto Gonzalez will give a talk entitled,

WebRTC applications present unique operational challenges that traditional monitoring tools cannot address. Unlike conventional web applications, real-time communication systems operate with complex peer-to-peer connections, dynamic network conditions, and media processing pipelines that can fail silently or degrade gradually. The primary challenge lies in observability. WebRTC applications generate extensive telemetry data for example, ICE candidate exchanges, DTLS handshakes, media statistics, and network quality metrics, but this data remains largely inaccessible to development teams. When users report

In a previous post, Reducing Voice Agent Latency with Parallel SLMs and LLMs, we showed how to reduce response times and create more natural conversational experiences using the LiveKit Agents framework. But optimization is only half the equation. Once your voice agents are deployed and handling real conversations, you need comprehensive monitoring to measure latency, track costs, and ensure optimal performance. Voice AI agents present unique observability challenges. Real-time audio processing, variable LLM latency, and

Voice assistants powered by real-time AI are increasingly being used to automate phone-based customer interactions. Whether for contact centers, internal help desks, or voice-driven workflows, a reliable architecture needs to support low-latency audio streaming, accurate speech-to-text (STT), intelligent response generation, and real-time speech synthesis. In this post, we walk through how to build a serverless Voice AI assistant that runs entirely on the scalable power of the AWS infrastructure, using Twilio ConversationRelay as the telephony

Bringing WebRTC and SIP together is a powerful way to connect modern web applications with traditional phone systems. Whether you’re enabling voice and video in the browser, or linking your app to a PBX and SIP trunk, WebRTC SIP integration allows users to communicate across platforms without needing special hardware or downloads. While the goal is clear, the integration process can be complex. WebRTC and SIP use different signaling protocols, media formats, and security models.

Large Language Models (LLMs) have dominated conversations about AI integration in WebRTC, particularly when it comes to voice-based features like transcription, summarization, and intent detection. But there’s an emerging layer that many outside of research circles are missing: Vision Language Models (VLMs). Unlike LLMs, which work with text and speech, VLMs are capable of understanding and generating language based on visual inputs—opening up new possibilities for analyzing what’s happening on camera during a WebRTC session.