AirMouse runs six AI models simultaneously — on your device, at sub-millisecond latency — so every gesture, voice command, and workflow feels instant and effortless.
AirMouse Aurora ships a custom neural stack: voice ASR, gesture CNN, screen OCR, context LLM, workflow compiler, and a predictive action engine — all running concurrently on-device.
# Aurora AI stack — boot sequence loading voice_asr_v4 ✓ 3.2ms loading gesture_cnn_v3 ✓ 1.8ms loading screen_ocr_v2 ✓ 2.1ms loading context_llm_mini ✓ 4.6ms loading workflow_compiler ✓ 0.9ms loading predict_engine_v5 ✓ 0.7ms aurora all models ready — 13.3ms aurora listening for input
A lightweight LLM continuously interprets your active app, clipboard, and usage history to build a real-time context graph — so commands like "reply to that" just work.
Our on-device ASR model was trained on 280,000 hours of speech across 42 languages. It understands accents, ambient noise, and natural speech patterns with 98.7% accuracy.
A convolutional neural network trained on 80 million gestures classifies your touch patterns with 99.2% accuracy and maps them to AI-powered actions in real time.
The workflow compiler parses natural language into an executable action graph — no scripts, no YAML, no clicking through settings panels.
Describe a workflow in plain English. The NL compiler converts it into a validated, optimised action graph instantly.
Native connectors for Slack, Notion, Linear, GitHub, Figma, Jira, and 195 more apps — with an open REST API for custom integrations.
Workflows branch on AI-evaluated conditions. "If the PR is failing, notify the team and open the error log" compiles to a fully conditional execution graph.
Aurora watches for repetitive patterns and proactively suggests automations — then builds them the moment you approve.
After 3 repetitions, Aurora recognises a routine and quietly builds an automation draft for your review.
Once a workflow is compiled, it executes in under 50 ms. Background, foreground, or silent — your choice.
500+ community-built workflow templates. One tap to install, fork, and customise for your own stack.
The prediction engine builds a Markov-inspired action graph from your personal usage data, locally. It surfaces the next likely action as a gentle suggestion — tap to accept, swipe to dismiss.
Our five-stage ML pipeline combines large-scale pre-training with privacy-preserving on-device fine-tuning.
Base models trained on 280K hours of speech and 80M gestures from consenting opt-in users with differential privacy.
Model gradients — never raw data — are aggregated across devices with noise injection to preserve privacy while improving accuracy.
INT8 and INT4 quantisation reduces model size 8× while preserving 98% of accuracy — enabling real-time on-device inference.
A tiny adapter layer learns your specific patterns locally — accent, gestures, workflows — in under 50 epochs with no data leaving your device.
Every model update is A/B tested silently on a 1% canary cohort. Automated accuracy benchmarks gate every deployment. Any model that regresses rolls back in under 90 seconds.
Every AI capability is governed by explicit user consent layers, hardware-backed isolation, and continuous red-team audits.
AI models run inside a dedicated secure enclave. Neither the OS nor other apps can access model inputs or outputs.
Federated learning adds calibrated noise to all gradient updates, making it mathematically impossible to reconstruct any individual's data.
Every AI feature that accesses app context or screen data has a granular toggle. Revoke any permission instantly.
We're building toward a world where your devices anticipate your needs before you voice them — a persistent AI co-pilot woven seamlessly into every tool you use.
Unified AI operating layer — always on, always private
Open REST and WebSocket APIs give developers direct access to voice, gesture, workflow, and context streams. Build custom integrations in minutes.
# Subscribe to voice intent stream const ws = new WebSocket( "wss://api.airmouseai.com/v2/voice" ); ws.onmessage = ({ data }) => { const event = JSON.parse(data); // { type: "intent", action: "open_app", // payload: { app: "VS Code" }, // confidence: 0.97 } if (event.type === "intent") { handleIntent(event); } }; # Also available: gesture, workflow, # context, prediction streams
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