“I wish this device came with ChatGPT”
This post is the first official introduction to Queue0 — an on-device intelligence layer connecting devices, machines, and robots to business operations in the era of Universal Automation.

Automating physical operations is hard — extremely hard.

We don’t have the universal standards and protocols we take for granted in cloud SaaS. No two customer environments are the same — even two locations of the same customer behave differently. Each site drifts from the start, developing its own operational personality.

After several projects integrating with IoT devices, industrial machines and robots, it became clear: most of the pain could be solved if our language models could understand each and every device as deeply as they understand well-documented software — and interact with it as powerfully as they do with my code editor.

That realization led to Queue0 — an on-device intelligence layer combining digital twins, LLM-powered agents, and a ledger-backed orchestration engine that makes machines capable of understanding, explaining, and acting — reliably.

What We’re Building

Queue0 gives each device an Operational Digital Twin — a live, structured representation of its state, capabilities, and history.

This twin speaks open, vendor-neutral standards like Web of Things, OpenAPI, and OPC UA, making every machine interoperable by design.

On top of that, we embed AI agents that deeply understand that specific machine. These aren’t generic chatbots — they reason with live context: current readings, historical events, and known parameters.

The LLM interprets context and makes decisions, while the orchestration engine guarantees reliable outcomes through causal, ledger-stream execution — merging adaptability and operational certainty.

Designed for the Full Lifecycle

Queue0 stays with the machine throughout its life:

Development – Engineers interact with the digital twin directly to test, simulate, and debug before deployment. System integration engineers don’t have to wait for test devices to arrive.
Operation – Operators can ask natural questions like “Why is cycle time slow?” and get actionable, contextual answers.
Maintenance – Technicians see what changed, what was tried, and what worked.

It’s one continuous intelligence layer that compounds knowledge over time.

Core Principles

Local-first — Operates offline. Cloud optional.
Vendor-agnostic — Built on open standards.
Operational reliability — Orchestration ensures consistent performance and recovery even as AI handles contextual decisions.
Causal traceability — Every event forms part of a verifiable ledger stream, capturing not just what happened, but why — a full causality chain.

What's Next

We're building Queue0 — and we're getting ready to share it with the world.

Our technology is already in production with early partners in foodtech and automation — powering real machines in real environments. In January 2026, we'll release the first public beta, making those same core capabilities available to a broader audience: on-device digital twins, intelligent orchestration, and embedded AI agents that enable machines to reason, act, and coordinate reliably.

If you'd like to get started early, you can join our pilot program today.

We'll schedule a demo to walk you through how Queue0 can integrate into your machines and operations right away.

And stay tuned — we'll be sharing our public roadmap, case studies from pilot programs, feature updates, and more as we move toward the public release.