WebRTC + AI Session Host for Live Conferences in 2026: Auto-Moderation and Q&A

NVIDIA GTC 2026, The AI Conference 2026, and Databricks Data + AI Summit all ran the same pattern: an AI session host that holds the timer, queues Q&A from chat and on-mic, drops a 60-second summary for late joiners, and pushes live captions over WebRTC. Volunteer moderators run 4-8 sessions a day instead of one.

Use case

A 120-track AI conference runs three days of parallel sessions across San Francisco, virtual, and 14 watch-party sites. Each session has a 25-minute talk and 5 minutes of Q&A. Volunteer moderators cannot manually run timer, queue, captions, and summary; they get tired by hour three. The 2026 pattern: WebRTC ingest from speaker, an AI session host watches the transcript, manages a Q&A queue from chat plus raised hands, drops a 60-second summary every five minutes for late joiners, and dings the speaker at the 22-minute mark.

Per The AI Conference's 2026 program, hybrid sessions support 120+ speakers at Pier 48 plus virtual; the AI host is the only sane way to run them.

Architecture

```mermaid flowchart LR Speaker[Speaker Browser] -- WHIP --> Edge[Edge SFU] Mod[Moderator UI] -- approve --> Host[AI Session Host] Audience[Audience Chat] -- ws --> Host Host -- transcript --> NATS[NATS bus] Host -- summary --> Late[Late Joiner Overlay] Host -- queue --> Mod Edge -- WHEP --> Audience Host -- audit --> Audit[(115+ tables)] ```

CallSphere implementation

CallSphere's session-host stack is the OneRoof gateway pattern repointed at multi-track events:

• Pion Go gateway 1.23 + NATS terminates speaker ingest; the AI host subscribes to `conf..transcript` and `conf..chat`. Identical to the /industries/real-estate gateway pattern.
• /demo browser path — Run a single-speaker session with the AI host at /demo; see the timer, queue, and summary live.
• HIPAA — Closed-door sessions get the same audit treatment as a clinical call; transcripts hashed and signed in one of 115+ database tables.

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Build steps

```typescript // 1. Speaker ingests via WHIP await ingestWHIP("https://conf.callsphere.ai/whip/track5", videoTrack, audioTrack);

// 2. AI host watches transcript, runs timer const startedAt = Date.now(); nats.subscribe("conf.track5.transcript", async (m) => { const elapsed = (Date.now() - startedAt) / 1000; if (elapsed > 22 * 60 && !warned) await ding("2 minutes left"); if (elapsed > 25 * 60 && !ended) await endTalk(); });

// 3. Summary every 5 minutes for late joiners setInterval(async () => { const recent = await getTranscript(300); const summary = await summarizeAgent.run(recent); await nats.publish("conf.track5.summary", encode(summary)); }, 300000);

// 4. Q&A queue nats.subscribe("conf.track5.chat", async (m) => { const { user, text } = decode(m.data); if (await isQuestion(text)) await pushQueue(user, text); }); ```

FAQ

Does the AI cut off the speaker? Only soft-cues; final cutoff is producer-approved.

How is the queue ranked? Upvotes plus question quality (a small classifier scores well-formed vs vague).

Multilingual? Yes — translate-first ASR; the queue normalizes to one language for the moderator.

What if the speaker goes off-topic? A separate drift detector flags major topic shifts to the moderator UI.

Can sponsors place ads in the summary? Yes — sponsor-clean summaries are a separate template; default is content-only.

Sources

• https://aiconference.com/
• https://www.databricks.com/dataaisummit
• https://www.nvidia.com/gtc/
• https://sequel.io/event-livestreaming-services/
• https://www.dacast.com/blog/webrtc-web-real-time-communication/

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WebRTC + AI Session Host for Live Conferences in 2026: Auto-Moderation and Q&A: production view

WebRTC + AI Session Host for Live Conferences in 2026: Auto-Moderation and Q&A is also a cost-per-conversation problem hiding in plain sight. Once you instrument tokens-in, tokens-out, tool calls, ASR seconds, and TTS seconds against booked-revenue per call, the right tradeoff between Realtime API and an async ASR + LLM + TTS pipeline becomes obvious — and it's almost never the same answer for healthcare as it is for salons.

Shipping the agent to production

Production AI agents live or die on three loops: evals, retries, and handoff state. CallSphere runs 37 agents across 6 verticals, each with its own eval suite — synthetic call transcripts replayed nightly with assertion checks on extracted entities (date, time, party size, insurance, address). Without that loop, prompt regressions ship silently and you only find out when bookings drop.

Structured tools beat free-form text every time. Our 90+ function tools all enforce JSON schemas validated server-side; if the model hallucinates an integer where a string is required, we retry with a corrective system message before falling back to a deterministic path. For long-running flows, we treat agent handoffs as a state machine — booking → confirmation → SMS — so context survives turn boundaries.

The Realtime API vs. async decision usually comes down to "is the user holding the phone right now?" If yes, Realtime; if no (callback queue, after-hours voicemail), async wins on cost-per-conversation, which we track per agent in 115+ database tables spanning all 6 verticals.

FAQ

What's the right way to scope the proof-of-concept? Setup runs 24 hours, the trial is 7 days with no credit card, and pricing tiers are $19, $49, $99, and $149 — so a vertical-specific pilot is a same-week decision, not a quarterly project. For a topic like "WebRTC + AI Session Host for Live Conferences in 2026: Auto-Moderation and Q&A", that means you're not starting from scratch — you're configuring an agent template that's already been hardened across thousands of conversations.

How do you handle compliance and data isolation? Day one is integration mapping (scheduler, CRM, messaging) and prompt tuning against your top 20 real call transcripts. Day two through five is shadow-mode running, where the agent transcribes and recommends but a human still answers, so you can compare side-by-side. Go-live is the moment your eval pass-rate clears your internal bar.

When does it make sense to switch from a managed model to a self-hosted one? The honest answer: it scales until your tool catalog gets stale. The agent is only as good as the integrations it can actually call, so the operational discipline is keeping schemas, webhooks, and fallback paths green. The platform handles the rest — observability, retries, multi-region routing — without your team owning the GPU layer.

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