AI Inside Live Video and Voice — Built at the Media-Path Level, Not Bolted On.

WebRTC AI means running AI inference inside a live WebRTC video or voice session — as the media streams — rather than on a recorded file after the fact. It covers real-time content moderation and analytics on live video, video enhancement (super-resolution, denoise, virtual background), voice AI agents inside calls, per-speaker live captions and translation, and delivering that AI output back to clients over WebRTC data channels. The defining challenge isn’t the model; it’s the transport layer — where inference runs relative to the SFU, whether it fits the latency budget, and how the result is delivered in sync with the stream.

It depends on your latency and cost targets and on data-residency rules — and it’s the first architectural decision, not a detail. Client-side adds the least latency and keeps media on the device but is bounded by the client and sees only that participant. SFU-side is where most live moderation and analytics belong, because you extract frames or audio from RTP where the media already lives. Edge sits between the two for latency or residency reasons. Cloud is the most scalable model-side but pays a network round-trip on every frame — the latency you can’t afford in a tight loop. We choose the placement on a scoping call, per the deep dive above.

You can’t add zero latency — inference takes time — but you can keep the added latency inside the budget that makes a call still feel real-time, which is the whole discipline. We do it by running inference where the media already is (usually SFU-side, not a cloud round-trip), sampling frames at the right cadence rather than every frame, holding a latency contract between detection and the action it triggers, and delivering results over the data channel so they arrive in sync with the stream. The goal is added latency the user never perceives — not the impossible promise of none.

A voice agent on WebRTC runs a pipeline inside the call: capture audio, transcribe or feed a speech-to-speech model, generate a response, and play it back — all against a latency budget tight enough to feel conversational. Barge-in means the agent stops talking the instant the user starts, which requires low-latency voice-activity detection and the ability to cut the agent’s own audio mid-utterance. Turn-taking means knowing when the user has actually finished rather than just paused. Getting both right is a real-time-systems problem tuned against the WebRTC path. The full breakdown lives in our voice-assistant practice.

This needs care. Whether any model provider is usable in a HIPAA context depends on having a signed Business Associate Agreement (BAA) that covers the specific data and modality you’re sending — and the coverage for real-time audio specifically is the gap teams get wrong, because BAA terms and product coverage change and may not extend to every endpoint. We architect around it: we confirm the current BAA position for your exact use case, keep PHI out of paths that aren’t covered, and design a fallback (including compliant or self-hosted speech models) so a compliance decision never blocks the build. We built real-time audio AI in a HIPAA setting for Martti across 4,000+ hospitals, so this is architecture we’ve shipped, not theory. Treat any specific vendor claim as something to verify at build time.

By pulling frames out of the media path and acting inside the window before a viewer sees them. In practice: extract frames from the SFU at a sampling cadence tuned to catch the event without burning GPU, run a real-time detector (a YOLO-class model or a hosted vision model, depending on latency and cost), and — on a positive — trigger the action (mute, blur, or cut) fast enough to land inside the latency contract. The output flags travel back over the data channel in sync with the stream. It’s a media-path pipeline, not a batch API call. Our AI content-moderation architecture guide covers the build.

The media path. A general AI development company works at the model, data, and application layer — RAG, agents, orchestration, ML pipelines — and does that well for AI that lives in a web app or a backend. WebRTC AI is AI inside live video and voice, where the hard part is transport-layer: SFU topology, frame extraction from RTP, latency budgets, and data-channel delivery. That’s protocol-level WebRTC depth, not general AI engineering. For the non-real-time side — agents, orchestration, RAG, ML/data work — see our sibling AI Development hub; most real products need some of each, and we build both.

No — and that’s deliberate. We’re model-agnostic: we integrate the best available models for the job (OpenAI, Anthropic, Gemini, Deepgram, and open-source models) and, where compliance or latency demands it, self-host open models. Our value is not a proprietary foundation model; it’s the real-time engineering that makes any of those models work inside a live WebRTC stream — the placement, the extraction, the latency contract, and the delivery. If a project genuinely needs a fine-tuned or custom model, we’ll build to the task, but we won’t sell you a research project you don’t need.

A dedicated Tech Lead with direct CTO access, a shared Slack channel, board access (Jira/Linear), and sprint demos every two weeks. Our team is based in Ukraine (GMT+2) with overlap across EU and US business hours, plus European offices in Valencia and London.