Precision control for machines that act in the real world.

Robots, autonomous vehicles and industrial systems increasingly take instructions in plain language and act on what their sensors see. ADG Precision is building the control layer that checks each instruction’s intent against the task, the environment and your safety rules before a single motor moves.

Physical AI · Exploring

operator: “Move the pallet from dock 2 to bay 3.”

intent: relocate · pallet #A-114 · dock 2 → bay 3

context: bay 3 occupied · person detected in aisle 4

decision: hold — route to operator for approval

operator: approve via alternate route

action: executed within safety envelope · logged

Physical AI needs a precision layer.

Models are getting better at deciding what to do. The risk is in what happens between the decision and the motion.

Language is ambiguous

The same instruction can mean different things on a different shift, line or site. Intent has to be resolved in context, not guessed.

The world changes

People, objects and conditions move between the moment a plan is made and the moment it is carried out.

Mistakes are physical

A wrong action cannot be rolled back like a bad database write. Checks have to happen before execution, not after.

The same control loop, applied to motion.

Whether an instruction comes from a person, a planner or a model, it passes through the same loop before a machine moves, and every result feeds the next decision.

Knowledge

What do we know about the situation?

The live scene: maps, sensor readings, task status, people nearby and the machine’s safety limits.

Intent

What outcome is wanted?

What the instruction really means: the task, the objects involved and the constraints that matter, even when the words are vague.

Proposed action

What change could serve that goal?

One specific motion plan, with the reason it fits the task.

Governance

Is the change justified, within the rules, and authorized?

Checked against the scene and the safety rules. Routine moves go ahead under rules the operator set; high-risk ones wait for an operator, with the reason shown.

Execution

What actually changes?

Only the approved motion, re-checked against the scene at the moment it starts, carried out within limits and recorded.

Feedback

What happened?

Sensor results flow back into Knowledge, so the next move starts from where things really are.

Results feed the next decision

Built for operators of real machines.

Warehouse & logistics

Mobile robots and picking systems that take task instructions from people and planning software.

Manufacturing & industrial

Cells and lines where an AI-proposed change must respect tolerances, interlocks and procedures.

Field & inspection

Drones and field robots that operate around people, property and changing conditions.

Service & humanoid robots

General-purpose robots that follow open-ended natural-language requests in shared spaces.

Proven first where mistakes cost money.

We run this discipline in production today for Google Ads: every change is prepared, approved outside the AI session and applied once, with a full audit trail. Physical AI brings the same approach to places where mistakes are physical.

Help shape precision control for physical AI.

Early access for Physical AI is open. Tell us about your machines and the decisions you need controlled.