Why Mapped for Physical AI
Physical AI starts with a model of the real world. We build it.
Eighty percent of global economic activity is physical. Logistics, energy, construction, transport — it all moves through buildings, campuses, factories, and infrastructure. The built world is where work happens, where energy is consumed, where goods are made and stored and shipped.
AI is finally ready to operate in that world. The question isn't whether Physical AI will reshape the built environment. It's whether AI systems will have what they need to act on it.
01
The problem no one talks about
AI systems can reason. They can plan. They can execute complex sequences of actions across digital systems with remarkable precision. Agents, robots, drones, autonomous vehicles, warehouse systems, smart infrastructure. The category is broad and growing.
Put any of them in a building, and they go blind.
The built world is the largest unstructured environment Physical AI will ever face. A typical commercial building has hundreds of mechanical, electrical, and IT systems — HVAC, lighting, access control, power monitoring, occupancy sensors — from dozens of vendors, using incompatible protocols, with no common schema, no shared namespace, and no single interface for reading or writing state.
An agent can't act on an environment it can't access or understand.
Before perception, before reasoning, before any autonomous action is possible, something has to make the building legible.
That's the problem no one talks about until they try to deploy. It's also the problem Mapped solves.
02
What Physical AI actually needs
Physical AI is defined by a loop: perceive, reason, act. Each step has hard requirements.
Perceive. Ingesting live, structured data from the physical environment — sensor readings, equipment states, occupancy patterns, energy consumption — normalized into a common representation that an AI system can actually interpret. Raw telemetry from a BACnet controller is not perceivable. Tagged, schema-validated, semantically mapped data is.
Reason. Operating over a world model: a structured representation of what systems exist, how they relate to each other, where they are in space, and what they're currently doing. An agent optimizing HVAC across a campus needs to know which AHU serves which zone, which zones are occupied, and how changes in one propagate to others. That's a knowledge graph problem, not a prompt problem.
Act. Issuing commands to physical systems safely and at scale — with the confidence that the right device, in the right location, is receiving the right instruction. Bi-directional control, with auditability.
Most AI infrastructure handles the reasoning layer. Almost none of it handles the perceive-and-act layer in the built world. That gap is why Physical AI stalls at the edge of physical environments.
03
What Mapped already does
Mapped is a knowledge graph of people, places, and things in the built world — normalized to BRICK and Haystack, queryable through a single GraphQL API, with bi-directional control over connected systems.
Physical AI requires data that has been Mapped.
Perceive. Mapped ingests data from 200+ building systems and device types — BACnet, Modbus, KNX, MQTT, Niagara, and more — normalizing raw telemetry into a structured, semantically tagged representation. Every data point is contextualized: not just a value, but what produced it, where, and what it means.
Reason. Mapped builds the world model. A knowledge graph that captures the spatial and functional relationships between systems, devices, zones, and occupants across a site. 40,000+ device mappings across 900 asset classes. A ground truth representation of the built environment that an AI system can query, traverse, and reason over — in real time.
Act. Mapped exposes everything through one API. Read current state. Write commands. Trigger automation. One integration point for any agent or autonomous system that needs to reach into a building and change something.
Self-Healing. Real-world environments are constantly changing: equipment replacements, network updates, ownership changes, security patches. Mapped keeps the graph current automatically. The world model stays accurate without manual intervention.
Already at scale
1,500+
sites
400M
square feet
250M+
normalized data points
99.95%
uptime
Agent-ready data. Not someday. Now.
04
The foundation, not the agent
Mapped doesn't make decisions about your building. It makes your building legible to the systems that do.
Think of it as the first step in building Physical AI. The AI boom requires spatial intelligence to work in the real world. Spatial intelligence requires a structured, real-time representation of physical environments. That representation is what Mapped builds and maintains.
We are not the agent. We are not the robot. We are not the vehicle. We are the substrate — the world model layer that Physical AI runs on.
If you're building autonomous systems, AI agents, robotics, or operational AI products that need to operate in the built world, you don't start with the agent. You start with the foundation.
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