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AeyeOS Platform

One operating system for modern law enforcement.

We designed and built AeyeOS end-to-end — architecture, backend, edge compute, AI voice pipeline, and every screen an officer or dispatcher touches. One login, one role model, one audit trail across every module.

11 Modules under one shell
2 Products shipping today
5 In active development
Edge + Cloud Jetson Thor on patrol

AI Command — operations workbench.

The command-centre application for agencies running ALPR patrol fleets. Dispatchers, investigators, and supervisors open one workspace — no more juggling vendor portals.

Dashboard Hotlist hits, severity donut, hourly plate reads, unit performance
Vehicle Plates Searchable reads with overview, hit history, and alert context
Live Map On-duty units, real-time reads, severity-colored markers
Hotlists Agency watchlists — 5 severity tiers, 3 match strategies
Fleet Management Unit locations, camera status, 5-second telemetry refresh
Traffic Overview Aggregate traffic patterns, flow analysis, trends
Search Audit Every search logged, timestamped, exportable — CJIS-ready

AI Partner — voice agent for patrol.

A hands-free conversational agent that rides with every officer. Activate by badge number, run the entire shift through voice — plates, alerts, notes, traffic stops, department policy.

Smart Gate Pipeline

A Gate LLM classifies every utterance as execute, respond, or skip. TF-IDF picks the right tool. An Executor LLM runs it. A Communicator LLM formats the spoken reply.

Edge-First Voice

STT runs Qwen3-ASR-1.7B on the Jetson Thor. TTS runs Kokoro-82M locally. Voiceprint verification via 3D-Speaker CAM++ — 512-dim embeddings per session.

Sub-Agent Workflows

Vehicle checks, traffic stops, alert review, and policy questions each open a dedicated sub-agent — separate LLM chains with their own tool sets.

Voice Pipeline
Hardware Processing LLM Intelligence
Mic
VAD
Silero
ASR
Qwen3
Gate
Classify
TF-IDF
Route
Executor
Run tool
Comm.
Format
TTS
Kokoro
Speaker
skip / respond ~500ms 1 LLM
execute 1.3–3.1s 3 LLMs + tool

Edge-first. Cloud-backed.

Every layer is independent. Edge handles latency-critical AI. Cloud handles coordination, storage, and admin.

Application AeyeOS Shell Next.js 16 · React 19 · TypeScript · Tailwind 4
Product AI Command 7 modules · ALPR · Fleet
Product AI Partner Voice AI · 16 commands
Admin 5 Modules Access · Depts · Devices · Patrol · Log
Backend NestJS + NATS 5 microservices · each owns its Postgres · JWT admin · Ed25519 devices
Hardware
Edge NVIDIA Jetson Thor aarch64 · CUDA · per patrol unit · NATS :4222 · local STT/TTS/inference

Three models. Same standard.

Architecture Review

Deep assessment of an existing platform's architecture, security posture, and compliance readiness. 2–4 weeks. Produces a prioritized findings report.

Embedded Engineering

Senior architectural leadership embedded within your program structure. 3–12 months. Task order aligned, clearance-ready.

Full Platform Build

End-to-end delivery from system design through deployment. 6–18 months. Five-phase methodology, gate-driven.

See what disciplined engineering looks like.

We'll walk you through the platform, the architecture, and the decisions behind it.