The WebRTC.ventures Blog.

Most AI applications operate in turns: a user submits input, the model processes it, and a response is returned. A delay of a second or two goes unnoticed. Live voice AI does not offer that margin. In a roleplay training session where a psychologist practices a difficult client conversation with an AI-simulated patient, every unnatural pause undermines the realism of the exercise. And with it, the training value. This was the central engineering challenge behind

The Janus WebRTC Server is one of the most pleasant WebRTC media servers to build on, and it’s a bonus that it is open source. It's small, fast, and modular: a thin C core with plugins for exactly the workloads you need, a clean signaling API, and a videoroom plugin that handles SFU-style conferences efficiently. However, moving beyond one instance and spreading rooms across a pool introduces its own challenges. Unlike some media servers, Janus

WebRTC.ventures recently completed a Kurento to LiveKit migration for a client running one of the more complex real-time platforms we've worked on, and we did it without downtime. The platform had conferences, webinars, inbound telephony (call center), outbound telephony, recordings, and live transcription and translation in English and Spanish — all in production, serving real users every day throughout the entire migration. This post covers the key decisions and findings. The full technical case study,

Vonage recently released the Video Connector SDK and a companion Pipecat Transport. These new tools let a server-side AI agent join a Vonage Video session as a full participant, with real-time audio and video. Together they make it possible to build an AI Avatar that doesn't just chat, but actually listens and speaks inside a live video call. That shift matters because most AI interaction today is still text-based — a chatbot as the first

Building real-time applications with AI requires disciplined engineering, not just clever prompting. That was the core thesis of the June 2026 AI Tinkerers San Salvador meetup, sponsored by WebRTC.ventures and AgilityFeat. In this post, we share key takeaways from the event: starting with real-world applications like an offline-first AI application for sign language translation, then we dove into the mechanics of LLMs, the power of Spec-Driven Development, and a case study on using AI to

“Real-time voice AI only works when infrastructure makes latency feel invisible. For us, that meant changing the shape of our WebRTC deployment without changing what clients expect from WebRTC itself.” A firsthand look at how OpenAI rearchitected their entire WebRTC stack for global scale, straight from Yi Zhang, co-author of their deep-dive post, How OpenAI delivers low-latency voice AI at scale. He was part of the team behind this overhaul, addressing pauses, interruptions, and delayed barge-in for

If your call center wants to use AI to automate call intake and handoff without losing the personal touch, a SignalWire Voice Agent can help. In this tutorial, you’ll build a Python-based AI receptionist that greets callers, captures the information you need, qualifies intent, and hands the call off to a human at the right moment.  The SignalWire Agents SDK makes orchestration across telephony, speech processing, and call control much easier to manage. That means

Voice agents are moving out of demos and into production systems that handle customer requests, account actions, healthcare questions, financial details, and support escalations. That changes the security model. A voice agent is listening, deciding, acting, and speaking in real time. That is why voice agent security cannot rely only on prompts or post-call transcript review. Sensitive data guardrails need to run inside the live pipeline: before sensitive data reaches the model, before tools are

Voice AI systems generate more than recordings and transcripts. Every production interaction produces a web of artifacts across multiple systems: call-setup metadata, ASR output, LLM responses, tool calls, CRM updates, escalation events, and compliance-relevant signals like caller identity verification. Most Voice AI architectures store some of these. Few store all of them in a way that survives an audit, a customer dispute, or a platform migration. That gap has a name and an emerging solution.

Choosing to go open source over a CPaaS for your WebRTC media stack is a strategic decision about control, flexibility, and long-term ownership. For teams building real-time products, an open source WebRTC media server can offer the freedom to customize media handling, integrate deeply with your architecture, and avoid the constraints of a managed platform’s pricing or roadmap. “With great power comes with great responsibility,” which is why open source is rarely the easiest path.

We built a Live Sales AI Presenter to model what a real-time AI avatar system looks like when it operates inside a real workflow rather than a demo sandbox. It is a slide-aware AI sales presenter combining deck ingestion, presentation control, live Q&A, WebRTC media, Pipecat orchestration, OpenAI Realtime, and HeyGen live avatar. You can watch the walkthrough here and the code is here. What the prototype makes visible is that a real-time AI avatar

A year ago, the question was whether AI avatar realism was convincing enough to use in front of a customer, whether for sales, support, or onboarding. That question is largely settled. These digital presenters have moved past the novelty stage and are now regular players in the Voice AI stack. HeyGen, Synthesia, D-ID, and Colossyan produce polished talking-head video with low enough latency that avatar output is no longer the primary differentiator. Today’s challenge is

Porting your business phone number to add AI call handling is an operational risk most companies don't need to take. Your number may be tied to other services. Your carrier contract may have obligations. And if something goes wrong mid-port, customer calls go nowhere. SIP forwarding sidesteps all of it. Your carrier forwards calls from your existing PSTN number to a SIP endpoint. The AI agent picks up, handles the conversation, and transfers to your

WhatsApp has over two billion active users, but for developers building real-time communications applications, it’s long been a walled garden. That’s changing. WhatsApp Business Calling is opening new doors for SIP-based integrations, and the technical landscape around it is evolving fast. In this episode, Dan Jenkins joins us to break down what combining WhatsApp Business Calling with WebRTC and SIP actually looks like in practice: the use cases, the architecture decisions, and the technical challenges that come with it.

Peermetrics is WebRTC.ventures' open-source WebRTC monitoring stack. Earlier this year, a client pushed it to a scale that stress-tested assumptions you can't easily replicate in development: thousands of video conferences a day totaling over a million events. That traffic surfaced things that only show up at volume. The result: Peermetrics SDK v2.8, @peermetrics/webrtc-stats v5.9.0, and a round of API and dashboard changes focused on server-side aggregation, Redis caching, cache pre-warming, and drilldown correctness. We also

Prompt engineering gets you a demo. Context engineering gets you a production Voice AI agent. Think of LLMs as the world's most brilliant librarians: they've read almost everything ever written, but without your help, they have the short-term memory of a goldfish. For text-based chatbots, a forgetful LLM is annoying. For a real-time voice agent, it kills the experience entirely. This is because the goal for Voice AI is human-level fluidity with sub-second latency. To

AWS autoscaling works well for stateless applications. And for the stateless components of VoIP and real-time systems like APIs and routing backends, AWS Auto Scaling groups (ASGs) and Amazon Elastic Container Service (ECS) do exactly what they're designed to do. But stateful VoIP infrastructure components like FreeSWITCH and RTPEngine maintain active SIP sessions and media streams tightly bound to specific instances. Because each instance holds live call state, scaling events must be carefully coordinated. Otherwise,

For the past several years, AV1 has been generating real excitement in the WebRTC and real-time communications space. Better compression, no licensing fees, and backing from the biggest names in tech made it look like the codec the industry had been waiting for. Then, on March 23, 2026, Dolby Laboratories filed patent infringement suits against Snap Inc., targeting Snap's use of HEVC and AV1 to encode and transcode videos in its Snapchat application. For developers

For years, developers building real-time video and audio applications on the web have worked under a quiet constraint: whatever protocol you chose, it had to work in Safari.  Well, that’s not 100% true because many WebRTC apps used to have disclaimers like “works best in Chrome”. When Safari finally fully supported WebRTC, it was a happy day for WebRTC developers. We could stop asking users “what browser are you using?” when responding to bug reports.

Your users report poor call quality, a dropped call, or a connection that never got established. But what actually happened? In this episode of WebRTC Live, we’ll break down what commonly fails in production WebRTC apps, how experienced teams debug live incidents, and how to build the visibility that keeps you ahead of problems. Guest host Alberto Gonzalez, CTO of WebRTC.ventures, sits down with Justin Williams, Senior WebRTC Engineer at WebRTC.ventures. Justin brings hands-on experience building real-time