The WebRTC.ventures Blog.

The annual ClueCon conference founded by the creators of FreeSWITCH has always been where the real-time communications community gathers to share what's working, what's breaking, and what's next. This year's edition, August 10-14 at The Midland Hotel in Chicago, made one thing unmistakably clear: AI is no longer a side experiment in the telecom world. It's embedded in production systems, and the conversation has shifted from ‘look what AI can do’ to ‘how do we

SIP and RTP feed a call and its media into a voice AI pipeline. What happens next runs through the control plane: what the voice agent is told, and what it decides to do about it.  In my talk at ClueCon this year, From SIP to Tokens: Deterministic Telephony Meets Real-Time Voice AI, I walked through this idea. The voice AI control plane, not the model, decides whether an agent hears something correctly and acts

Human speech is often unstructured and messy. To keep up with real conversation, Voice AI bots must parse partial sentences, filler words, and interruptions, all without the visual cues a human listener relies on. On top of that linguistic challenge, real-world latency budgets and network conditions test every Voice AI pipeline, and users don’t forgive one that stutters. In this episode of WebRTC Live, host Arin Sime talks with Dr. Varun Singh, Chief Product Technology Officer

In the space of thirteen months, Google took real-time speech translation from an English-and-Spanish demo at I/O 2025 to Gemini 3.5 Live Translate, a 70-language streaming model announced in June 2026 and now expanding into Google Meet through private preview, on top of the five-language version already generally available there. That speed says something: real-time translation is fast becoming a baseline expectation, not a differentiator. That shift won't stop at video conferencing. Contact centers, telehealth,

We built a self-hosted video calling system for a healthcare platform client, replacing the usual "meeting link" pattern with a real phone-call flow: clinician dials, patient's phone rings, patient answers. Highlights: Web-to-Android calling with one-tap answer Jitsi Android SDK startup optimization for near-instant connect Server-owned call lifecycle: ring, answer, decline, timeout, stale-call replacement Jitsi on Kubernetes, portable across cloud, hospital, and on-premise Prometheus, Grafana, and OpenTelemetry observability Load-tested and handed off for client-owned operation Read

STUNner is a Kubernetes-native TURN server, TURN being the relay protocol that gives WebRTC clients a public endpoint to send media through. It builds on the Gateway API to terminate TURN at a single load balancer endpoint and relay media to the pod behind it, so media-server pods stay on ordinary ClusterIP Services, cluster-internal only, with no public IPs of their own. Examples in this post use Elastic Kubernetes Service (Amazon EKS), but the resource

Voice AI tooling broadly falls into two categories: managed platforms that get you a production agent fast, and custom-built frameworks that give you full ownership of your voice pipeline. Within each category, the specific tools differ in ways that are worth understanding before you commit engineering time or lock yourself into constraints you'll hit later at scale. This voice AI comparison looks at three representative managed voice AI platforms: Retell AI, Bland AI, and Synthflow,

AI-powered voice agents continue to expand into telephony, healthcare, and enterprise customer service. Teams building these systems face a shared set of problems: keeping latency low, keeping calls reliable, and keeping the underlying infrastructure fast enough to support real-time audio at scale. Two members of the WebRTC.ventures team will address these problems from different angles at ClueCon 2026, taking place August 10–14 at The Midland Hotel in Chicago. Alberto Gonzalez: Deterministic Telephony Meets Real-Time Voice

Telephony agents built on Voice AI platforms like Retell AI make it easy for businesses to run live phone conversations through an AI agent instead of an IVR or human call center staff. Getting one of these agents into production means solving two things: integrating it with existing telephony infrastructure, and adding a compliance layer that preserves the organization's security and governance standards. On the integration side, the agent must connect to existing PBXs, SIP

How can AI scale clinical roleplay training beyond what live sessions with human trainers can deliver? For CETA Global, a nonprofit that trains frontline mental health providers around the world, the answer was an AI “flight simulator”: a real-time platform where psychologists practice difficult client sessions with an AI-generated patient, complete with a lip-synced streaming avatar, live coaching, and replayable feedback. Behind the scenes, a team of six AI agents works in concert to play the

An agentic workflow is the disciplined form of what's often called "vibe coding": giving a coding agent a task and the context it needs, then letting it plan, edit, and test across your codebase while you review the results. When it's done well, the productivity gains can be dramatic. In his vibe-coding WebRTC post, Philipp Hancke describes getting changes merged into libWebRTC, Firefox, and even Pion, work that used to wait on finding a free

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.