webxrvoiceprototyping

Voice + VR + TypeScript: prototyping an assistant for virtual meeting rooms

UUnknown

2026-02-16

11 min read

Blueprint for a TypeScript WebXR assistant: voice commands, context-aware help, in-VR UI patterns, and fallbacks for platform shutdowns.

Hook: You're building a VR meeting assistant — now plan for the shutdown

If you're prototyping an assistant for VR meeting rooms in 2026, you face two converging challenges: shipping robust, type-safe logic for voice and spatial UI, and protecting your investment from the platform churn we've seen recently. Meta discontinued its standalone Workrooms app on February 16, 2026, and major shifts (including cross-vendor AI integrations like Apple tapping Google’s Gemini) have re-shaped where compute and voice services live. This blueprint gives you a practical, TypeScript-first path to prototype a voice-enabled assistant for WebXR meeting spaces, build context-aware help and in-VR UI patterns, and design reliable fallbacks so your assistant survives platform shutdowns.

The 2026 context: why this matters now

By 2026, three trends are decisive for VR/AR assistants:

Platform consolidation and shutdown risk: Major vendors have shut or restructured native meeting products (for example, Meta's Workrooms discontinuation), raising the bar for cross-platform portability.
AI consolidation: Big players are packaging LLM and multimodal models into platform services (Apple + Google Gemini deals), so your architecture should separate model access from UI logic.
Open WebXR maturity: WebXR and complementary specs (WebXR Layers, WebXR DOM Overlays, WebAudio and WebRTC) matured through 2024–2026, making browser-based XR viable for enterprise meeting experiences.

Result: a TypeScript-based WebXR assistant lets you iterate quickly, keep strong typing across modules, and remain platform-agnostic. Let's design one.

High-level architecture

Keep the system modular and host-agnostic. Key layers:

XR UI Layer — WebXR scene, 3D panels, spatial audio, and input handling. Frameworks: Three.js, React Three Fiber, or A-Frame.
Voice & Audio Layer — Microphone capture, local VAD (voice activity detection), STT (speech-to-text) via local browser APIs or remote models, and TTS for responses.
Assistant Logic — TypeScript command parsing, intent routing, context management, and policy checks.
Model & Services — LLM/LLM+multimodal access, knowledge connectors, and enterprise identity/auth (separated behind server-side endpoints).
Sync & Collaboration — Shared room state using WebRTC DataChannels, CRDTs, or a server-backed room service for persistent state.

Keep the UI and assistant logic client-side (TypeScript), and delegate heavy AI and persistence to secure services so you can pivot if a vendor shuts down.

Why TypeScript?

Type safety for command signatures and event payloads reduces runtime ambiguity in spatial UIs.
Refactor-friendly for iterative prototyping across different WebXR runtimes.
Tooling (tsserver, linters, type-aware tests) speeds iteration when dealing with complex domain models: meeting objects, permissions, and multimodal inputs.

Voice pipeline: practical TypeScript blueprint

There are two pragmatic approaches for voice:

Client-first using browser APIs (Web Speech API for STT, SpeechSynthesis for TTS) — fastest for prototypes but inconsistent cross-browser.
Hybrid: capture audio client-side, stream to a server-side STT or LLM via WebRTC or WebSocket. More reliable and enterprise-ready.

Minimal TypeScript interface for commands

Start by defining strong types for parsed intents and actions. This keeps UI, network, and AI code consistent.

type UserId = string;

export type MeetingObject = {
  id: string;
  type: 'whiteboard' | 'screen' | 'participant' | 'file';
  displayName?: string;
};

export type AssistantIntent =
  | { kind: 'join'; roomId: string }
  | { kind: 'summarize'; target?: string }
  | { kind: 'pin'; objectId: string }
  | { kind: 'list'; what: 'participants' | 'files' };

export interface AssistantResponse {
  text?: string;
  speechUrl?: string; // optional TTS
  actions?: Array<{ type: 'highlight' | 'open' | 'notify'; payload: any }>;
}

Client-first STT example (prototype)

Use the Web Speech API where available; wrap it in a typed adapter so you can swap to server-side STT later.

export class SpeechAdapter {
  private recognition: any;

  constructor(private onResult: (text: string) => void) {
    const SpeechRecognition = (window as any).webkitSpeechRecognition || (window as any).SpeechRecognition;
    if (!SpeechRecognition) throw new Error('SpeechRecognition not available');
    this.recognition = new SpeechRecognition();
    this.recognition.continuous = false;
    this.recognition.interimResults = true;
    this.recognition.onresult = (ev: SpeechRecognitionEvent) => {
      const text = Array.from(ev.results)
        .map(r => r[0].transcript)
        .join('');
      this.onResult(text);
    };
  }

  start() { this.recognition.start(); }
  stop() { this.recognition.stop(); }
}

Wrap in try/catch and provide fallback paths that stream audio to a server STT if browser support is missing. If you need better capture quality for testing, see field recorder recommendations for recorded audio and test inputs (field recorder comparison).

Command parsing and intent routing

Use a small, typed router that maps intents to handlers. Keep handlers pure and side-effect-free where possible for testability.

type Handler = (intent: T, ctx: AssistantContext) => Promise<AssistantResponse>;

const handlers: Record<string, Handler<any>> = {
  join: async (i: { kind: 'join'; roomId: string }, ctx) => {
    await ctx.joinRoom(i.roomId);
    return { text: `Joining ${i.roomId}` };
  },
  summarize: async (i, ctx) => {
    const summary = await ctx.server.summarizeCurrentMeeting();
    return { text: summary };
  },
};

export async function route(intent: AssistantIntent, ctx: AssistantContext) {
  const h = handlers[intent.kind];
  if (!h) return { text: "Sorry, I don't understand that command." };
  return h(intent as any, ctx);
}

Context-aware help: what to capture and how to expose it

Context is what makes your assistant helpful in VR. Typical context elements:

Room state — current agenda, active speaker, shared objects.
User state — role (presenter vs attendee), permissions, preferences, last interaction.
Spatial state — which object is near the user, gaze target, pinned elements.

Model context as a typed snapshot that is cheap to transmit and easy to diff. For shared state, prefer CRDTs or operational transforms to avoid hard failures when network partitions happen in VR rooms.

export interface AssistantContextSnapshot {
  roomId: string;
  users: Array<{ id: UserId; name: string; role: 'host'|'attendee' } >;
  pinnedObjectId?: string;
  focusedObjectId?: string; // what the user's gaze is on
  lastChatMessages: string[];
}

Expose the snapshot to the assistant logic and to any UI components that render contextual help panels.

In-VR UI patterns that actually work

Design for low friction. In-VR assistants should respect spatial norms and reduce cognitive load.

Non-intrusive hints: transient floating labels and subtle haptics rather than modal takeovers.
Pin-and-follow panels: allow users to pin a 2D panel to a virtual wall or attach it to their wrist/controller.
Spatial audio cues: use positional audio to cue attention to pinned content or other participants.
Progressive disclosure: show summary first, deep details on demand.

Example: a compact help bubble follows the user when the assistant detects repeated errors or long silence with open questions. Allow quick actions like “show transcript” or “jump to slide 4.”

TypeScript-driven UI component interface

export type UiAction =
  | { type: 'showPanel'; panelId: string; attachTo?: 'wrist' | 'wall' | 'head' }
  | { type: 'highlightObject'; objectId: string }
  | { type: 'playSound'; soundId: string };

export interface UiRenderer {
  render(actions: UiAction[]): void;
}

Integration with LLMs and multimodal services (2026 tips)

By 2026, expect vendor services to provide multimodal LLMs and accessible APIs—but keep them behind a server layer for security, billing, and swapping providers.

Use server-side adapters for model access (OpenAI-family, Google Gemini, Anthropic, or on-prem options) with strict rate limits and policy checks. Implement the facade so you can scale it independently (see server scaling patterns like auto-sharding blueprints).
Design prompts to be deterministic for command parsing and more generative for meeting summarization.
Cache frequent queries and summaries locally to remain resilient when services are temporarily unavailable.

Keep tokenization and prompt engineering as a server concern; client sends typed intents with context references (IDs), not raw transcripts.

Prototyping recipe: from zero to playable demo (TypeScript-first)

Bootstrap a WebXR scene with Three.js or React Three Fiber. Add a simple panel for assistant text.
Wire microphone capture to a SpeechAdapter (client-first) or to a small Node/Edge STT service (hybrid). For audio testing and recorded inputs, consult field recorder comparisons to pick reliable capture gear (field recorder comparison).
Implement typed intents and the routing table shown earlier.
Connect a simple server endpoint for summarization or model calls (REST + WebSocket). Keep an API facade for swapping providers.
Implement basic UI actions (showPanel, highlightObject) and test them in a browser-based XR session (Chrome/Edge with WebXR support).

The goal is a working loop: speak > parse > route > UI action. Iterate quickly, keep types strict, and add telemetry (use developer tooling that surfaces runtime telemetry and UX) to see where users expect the assistant to help.

Designing fallbacks for platform shutdowns and vendor churn

Given recent events (for example, Meta discontinuing Workrooms), you must plan for your assistant to survive platform changes. These are concrete strategies:

1. Adopt web standards as first-class

Build on WebXR, WebAudio, WebRTC, and browser storage. A browser-based assistant can run on many headsets and in non-native shells.

2. Keep UI and state portable

Store room definitions, object metadata, and user preferences in neutral formats (JSON-LD or typed JSON schemas). Provide import/export endpoints so organizations can migrate data. For cached transcripts and exported artifacts, consider edge-friendly storage models (edge storage).

3. Separate model access from app logic

Never bake API keys into client apps. Keep a server facade that you control: if a vendor deprecates a model or service, you change the facade instead of every client.

4. Progressive enhancement and local-first features

Implement essential assistant features locally: local STT/TTS (where possible), transcript caching, and offline help content. These features keep the assistant useful when remote services or platform features go offline.

5. Export and open-sourcing strategy

Offer a one-click export of meeting artifacts (transcripts, highlights, pinned objects) in standard formats. Consider open-sourcing key client parts to give organizations a path to self-host or fork if the platform disappears.

6. Multi-host architecture

Allow the assistant to run in three hosting modes: embedded (in hosted XR runtime), web-hosted (WebXR in browser), and self-hosted (Dockerized server + static client). Test and document switching between modes. If you plan to run inference at the edge, consult edge reliability patterns for inference nodes (edge AI reliability) and low-latency sync stacks (edge AI & low-latency sync).

Example: migrating off a proprietary meeting app

Scenario: Workrooms-like vendor shuts down. Your assistant was built as a plugin. How to migrate?

Export room definitions and assets (JSON + glTF) from the vendor.
Stand up a WebXR-hosted room shell and import assets.
Start the assistant’s server facade on an internal host; point clients to the new URL. If your facade needs to scale, follow server scaling and sharding best-practices (auto-sharding blueprints).
Switch STT/TTS to hybrid endpoints (or use browser-based fallbacks while migrating).
Notify users and provide an in-app migration guide (auto-import, credentials mapping).

Because your assistant code used typed interfaces and a server facade, the migration is mostly configuration and asset import — not a rewrite.

Testing and observability

Focus tests on intent routing, context snapshots, and UI action rendering:

Unit tests for command parsing and handler logic (TypeScript + Jest).
Integration tests that simulate audio input and verify assistant responses (use recorded audio).
End-to-end tests in an emulated browser XR environment (Puppeteer + headless WebXR support where available).
Telemetry: record intent success rate, fallback activation, and user-accepted suggestions to guide UX iteration.

Security and privacy best practices

Explicit consent for audio capture; minimize retention of raw audio.
Server-side policy enforcement for model outputs and redaction of sensitive content.
Role-based access control for assistant actions that affect meeting state.
Encryption in transit (TLS for REST/WebSocket; SRTP for WebRTC audio).

Actionable takeaways

Build on Web standards: prefer WebXR + WebAudio + WebRTC so your assistant runs across runtimes.
Type everything: model your intents, context snapshot, and UI actions in TypeScript to make migration trivial.
Abstract AI: use a server facade for model access so you can swap providers and handle vendor shutdowns.
Local-first fallbacks: implement browser STT/TTS and transcript caching so the assistant remains useful offline or during service outages.
Export & migrate: provide export tools for room assets and metadata as a migration safety net.

Future predictions (late 2025–2026 trends)

Expect to see these developments in the near term:

Hybrid compute: more LLMs run in edge or private cloud settings for latency-sensitive VR assistants.
Multimodal native APIs: platform SDKs will expose richer multimodal intents (gesture + voice) while the WebXR ecosystem will adopt similar patterns.
Policy-first assistants: enterprise assistants will embed governance rules to keep automated actions auditable.

Design for these by keeping your assistant modular and policy-aware today.

"Meta discontinued Workrooms as a standalone app on Feb 16, 2026" — a reminder: platform changes are real; plan for portability.

Final checklist before your first demo

Typed intent schema and routing implemented.
Microphone capture and one STT path (client or hybrid) working.
Simple UI actions (showPanel, highlightObject) implemented and tested in WebXR.
Server facade for model calls with a mock provider for offline demos.
Export/import tested for room data and assets.

Call to action

Start with a one-day spike: scaffold a WebXR scene, add a SpeechAdapter, and implement two intents (join room, summarize). Use the TypeScript interfaces provided here as your contract. If you want, fork a lightweight reference repo (I recommend starting with React Three Fiber + TypeScript) and iterate with your team. If you hit a platform shutdown mid-project, remember: standards and typed contracts are your best insurance. Ship the assistant logic as portable, and the UI will follow.

Want a starter repo and a TypeScript template to accelerate the spike? Export a sample manifest and I’ll provide a minimal WebXR + assistant template you can run locally and deploy as a static site behind an optional server facade.

Unknown

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.