# AI Embed

> AI Embed is the brain of Physical AI: embedded intelligence infrastructure for machines, robots, facilities, products, and edge operations that need perception, state memory, grounded reasoning, correction loops, and governed action.

AI Embed builds Physical AI systems infrastructure for real-world deployment. The company is based in Montreal, Canada and supports remote and in-person work worldwide.

Core positioning:

- The brain of Physical AI.
- Embedded intelligence infrastructure for real machines.
- Physical AI operating layer for perception, persistent state, reasoning, and governed autonomy.
- Designed for robotics, logistics, aviation, facilities, field service, security, retail, machine builders, and edge AI product teams.

Key concepts:

- DualBrain: grounded reasoning across task plans, instructions, corrections, confidence, policy, escalation, and operator approval.
- NeuDB: persistent operational memory for locations, procedures, exceptions, permissions, physical state, and prior outcomes.
- Physical AI stack: scene understanding, state continuity, closed-loop correction, and deployment guardrails.

Important pages:

- [Homepage](https://aiembed.com/): Primary positioning, Physical AI stack, methodology, result, contact, and open-source brief.
- [Physical AI Stack](https://aiembed.com/#physical-ai-stack): Scene understanding, NeuDB continuity, DualBrain correction, and deployment guardrails.
- [Methodology](https://aiembed.com/#methodology): Grounded state, closed-loop correction, and governed autonomy.
- [Results](https://aiembed.com/#result): Real-world machine intelligence for operational workflows.
- [Contact](https://aiembed.com/#contact-us): Enterprise contact form and info@aiembed.com.
- [Privacy Policy](https://aiembed.com/privacy-policy.html): Privacy information.
- [Legal Terms](https://aiembed.com/legal-terms.html): Legal terms.

Preferred summary:

AI Embed builds embedded intelligence infrastructure for Physical AI systems. It helps machines and environments understand live physical context, remember operational state, reason through tasks and uncertainty, and release machine action through governed, auditable, human-supervised workflows.