— Physical intelligence infrastructure
BUILD PHYSICAL INTELLIGENCE FROM REALITY.
Physicore helps Physical AI systems become more capable and reliable in the environments where they are ultimately expected to operate.
— One system
PLAYGROUND. THE STANDARD. FIELDFORCE. BEDROCK.
REAL ENVIRONMENTS.
Operating environments made usable for Physical AI development, evaluation and improvement.
REAL-WORLD MEASUREMENT.
A common structure for understanding how performance changes across physical conditions.
PHYSICAL DATA OPERATIONS.
Targeted capture of real-world behaviour, variation, failure and recovery.
MODEL-GRADE INFRASTRUCTURE.
Structured, traceable physical-world data built for training, post-training and evaluation.
— Programme infrastructure
FROM REQUIREMENT TO REAL-WORLD EXECUTION.
A Physical AI programme can require more than a test environment. It may require multiple sites, specialist facilities, field operators, robotics expertise, data capture, evaluation and clearly defined evidence.
Physicore brings those requirements together into one managed real-world programme.
ENVIRONMENTS
Real operating sites and specialist test infrastructure.
PEOPLE
Field operators, engineers and relevant domain specialists.
EXECUTION
Testing, data capture, evaluation, failure analysis, remediation and re-testing.
EVIDENCE
Structured outputs showing what held, what failed, what changed and whether improvement transferred.
One programme can combine multiple environments, capabilities and geographies around a common requirement.
— The Physicore system
FROM CAPABILITY TO RELIABILITY.
We establish what a system can do, expose where that capability breaks as reality changes, determine what is missing, and prove whether the resulting improvement holds.
BASELINE
Establish performance under normal operating conditions against clearly defined tasks and success criteria.
STRESS
Systematically vary the conditions around the task to expose where capability begins to degrade.
FIND
Map the precise conditions, behaviours and failure modes under which reliability breaks.
DIAGNOSE
Use real-world evidence, system behaviour and available training and evaluation data to determine the capability or coverage gap behind the failure.
BUILD
Create the targeted real-world demonstrations, failures and recovery data required to address the identified gap, with clear provenance and rights.
PROVE
Re-test the improved system against the original conditions and unseen variations to determine whether reliability actually improved.
— Real-world learning
FIND WHAT THE MODEL HAS NOT LEARNED.
Physicore measures how systems behave in real environments, identifies the conditions that expose weakness and captures the data required to close the gap.
Then the system returns to reality.
— Data
BUILD ONLY WHAT IS MISSING.
Real-world testing reveals the situations a system has not learned to handle.
Physicore turns those gaps into targeted model-grade data covering the behaviours, failures and recoveries needed to improve capability, with provenance and rights built in from the start.
— Real-world evaluation
MEASURE WHAT CHANGES.
Physicore measures how intelligence behaves as the physical world changes around it.
Not one score.
A picture of where performance holds, where it degrades and where it breaks.