10×
faster evaluation cycles
Projection stages the same scene. Vision verifies it before each run. Less scene-to-scene noise lets teams compare policies in 30 controlled runs instead of 300 brute-force runs.
Prism
Stop guessing. Make real progress
in Physical AI.
Prism controls and checks
each test condition.
Your team can find regressions,
measure progress, and see
where a robot fails.
Find progress. Catch regressions.
Iterate faster.
See Prism in the lab.
Open the field gallery
Example 1 of 6
Stage
Set the
test condition.
Project the condition. Show the required object pose, work area, and task state in the workcell. Prism shows the operator how to set the scene.
Verify
Check the
physical state.
Verify the scene. Use vision to confirm the physical state before each run starts. Check object identity, pose, occlusion, and tolerance before the robot moves.
Track
Keep the projection
locked to the scene.
Prism tracks the surface and objects as the physical scene moves. Project the required object position and task setup into the workcell.
Capability maps
Map where the robot
works and fails.
Prism changes one test condition at a time: position, orientation, clutter, objects, or light. The result shows where each policy works and fails.
Evaluation method
Compare policies under
the same physical
conditions.
Prism uses projection to stage the scene and vision to verify it. Compare Policy A with Policy B under the same physical conditions, so each result shows a policy change instead of room noise.
Evaluation estimates from pilot customers
3%
physical test variability
From 40%. The estimated change in results between repeated runs of the same staged test condition.
20,000 hr
engineering time saved
Pilot estimate to date. Estimated time not spent on manual resets, repeated runs, and result cleanup across pilot evaluations.
The Light Company Robotics
Deploy Prism
in your lab.
Use Prism for policy evaluation, regression testing, and capability mapping.