LIVE
CAM-02
Humanoid — Dishes
LIVE
CAM-07
Switch Close-up
LIVE
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Living Room
LIVE
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Bedroom
LIVE
CAM-01
Switch Panel
LIVE
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Humanoid — Box Manip
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Kitchen
KVASI
The trust layer
for embodied AI.
Your policy. Our rigs. Every bottleneck found.
The Problem

Embodied AI teams are blocked by infrastructure, not ambition.

$150K–$500K+ per
3–6 months of engineering time burned on setup
Manual testing that barely scratches the surface
60–75% of budget goes to rigs, not R&D
30–75% performance drop under real-world perturbation
Bottlenecks found by accident, not by design
World Augmentation

One rig. Infinite conditions.

AUGMENTING
Source scene — conditions varying in real time

Same physical setup. Thousands of unique test conditions. Our augmentation engine turns one environment into an entire distribution — so you test the ODD, not just the lab.

Surface / MaterialRelightingObject InsertionTemporalSensor FX
24
ODD Parameters
1K+
Scenario Variants
6
Augmentation Axes
By The Numbers

Built for the teams shipping embodied AI.

Policy Teams

Find in hours what used to take months.

Every checkpoint stress-tested across thousands of real-world variants. Bottlenecks surfaced and decomposed — your next iteration is the right one.

10× faster iteration cycles
·Foundation policies
Embodied AI Startups

R&D budget goes to R&D. Not rigs.

Stop burning runway on eval infrastructure. Push your policy to our rigs and get depth of testing that would take 6 months to build internally.

$150K–$500K saved on rig setup
Zero setup cost·On-demand rigs
Research & Benchmarking

Same testbed. Every model. No excuses.

VLA, classical and hybrid stacks under identical physical conditions. Reproducible, attributable, head-to-head — the way embodied AI research should work.

16 architectures · identical conditions
Head-to-head·Bottleneck attribution
The Trust Pipeline

Predict it. Prove it. Catch it.

Forecast which bins will fail before you train. Prove what your policy actually learned. Catch every failure live on the rig — and feed it back. Three calibrated reports per push, all on real hardware.

Explore Dashboard
Report 01 · Dataset Audit · pre-training
B+
h1-domestic-kitchen-v2
overall 0.71 · CI [0.68, 0.74]
318 episodes · 47.2 hrs
Sub-grades6 dims
motion0.68
trajectory0.74
visual0.83
contact0.71
language0.66
vendi-div0.79
Scaling-law forecast+7% @ +60 demos
258 demos318 now378 forecast
Pre-flight warnings2 flagged
Low-light bin SPARSE6 demos at 50–150 lux.
ODD: lighting · −9% predicted in dim deployment
Object-position shortcut HIGHMI 0.71 on bowl placement.
policy will fail when objects move
Prescriptionranked by lift
P1 · highest lift
Collect 60 demos at 50–150 lux → forecast +7% success.
12 hrs teleop · 3 lighting bins · auto-scheduled
P2 · baseline cleanup
Drop 12 F-grade episodes (jerk + duplicates) → +2% baseline.
14 candidates flagged · review before next train
After P1+P2 · forecast 0.78 overall, ready to ship
↑ +0.07
Pre-flight verdict
Ship-Conditional
Comparison

Build internally, or with Kvasi.

Build internally
With Kvasi
Setup cost$150K–$500K+$0 infrastructure
Time to first eval3–6 weeks<10 minutes
Scenario diversityLowThousands of variants
Reset automationManualFully automated
Bottleneck attributionAd-hoc / manualAutomated to root cause
KVASI

Find every bottleneck. Accelerate every iteration.

Stop rebuilding eval labs. Start making progress.