Audit the evidence
Ingest simulation, replay, test, and field evidence. Surface unsupported assumptions instead of filling gaps with guesses.
Deployment readiness for robotics teams
Is your simulation actually deployment-ready?
Reliability Studio audits what your simulation evidence actually supports, finds what has not been validated, and builds the next tests before you commit people, hardware, and customer trust.
⌖Measured limitsEmpirical boundaries from real evaluations.⌁Quantified riskUncertainty characterized across the boundary.◇Defensible decisionsEvidence, lineage, and auditability built in.
Reliability boundary instrumentMODEL UNDER EVALUATION : M–7.4
DATASET : VAL–24Q2
METRIC : TASK SUCCESS
CONFIDENCE : 95%
DATASET : VAL–24Q2
METRIC : TASK SUCCESS
CONFIDENCE : 95%
Success rate (empirical)1.000.800.600.400.200.00
Observed
limit
limit
SupportedUnresolvedNot supported
10⁻²10⁻¹11010²10³
Operating condition / relative severity
Observed limit (P95)2.48Success rate at limit0.70Slope at limit (dS/dx)−0.215Uncertainty (P95)±0.07
BUILT FOR THE PILOT-TO-ROLLOUT GAP
Simulation passed.Lighting changed.Latency moved.Friction shifted.Know whether the evidence justifies the next deployment.
FOUNDER EXPERIENCE AND EDUCATION
Morgan StanleyMicrosoftUniversity of OxfordStanford UniversityColumbia University
An evidence agent—not an autopilot.
A reliability engineering workflow for a lean robotics team.
Design the next test
Prioritize the perturbations that resolve the most important deployment uncertainty before expensive field work begins.
Keep humans at the gate
Engineers approve scope, assumptions, expensive runs, ambiguous failures, and deployment. Supported failures become regressions.
Cost of an unready deployment
Make the deployment decision economically legible.
Enter the costs your team already recognizes. Reliability Studio reports spend at risk and spend protected; it only reports verified avoided cost after the decision and counterfactual are documented.
DEPLOYMENT SPEND AT RISK
$173,000Customer-entered cost exposure—not claimed savings. It becomes verified avoided cost only after a documented deployment decision.
Audit this deploymentMeasured product validation
Real workflows. Reproducible deltas. Claim boundaries intact.
These are controlled Iso validation workflows—not customer outcomes and not claims that Studio caused a model improvement. Studio preserved the evidence, located the changed conditions, and turned remaining failures into replayable guards.
01 / TRAJECTORY WORLD MODELCPU · 4,000 transitions · 25-step rollouts
Nonlinear dynamics model vs. linear baseline
Evaluation pass rate0 → 93.3%Mean rollout RMSE−89.0%Validation loss−99.6%Laptop CPU training6.6 s
All 12 in-support evaluations passed. One of three deliberately out-of-support evaluations failed and reproduced at its pinned condition and seed. Reference dynamics were simulated; physical transport remains unclaimed.
02 / AUTONOMOUS WAREHOUSE POLICY24 runs · 4 conditions · 3 seeds
Candidate regression located before release
Baseline pass rate75%Candidate pass rate25%Regression located−50 ptsRegressed conditions2 of 4
Studio prevented a healthy process exit from being mistaken for reliable behavior, preserved the failing scenario and seed, and reproduced the failure as a permanent release guard.
Generated with versioned SDK artifacts · content-addressed lineage · no physical-world safety claim
The Studio
Click through the complete reliability loop.
Explore the current Studio workflow without creating an account: configure in Build & Run, verify truthful environment rendering, authorize execution, locate the boundary, compare versions, review evidence, and preserve the failure as a regression guard.
01 / 08 · Build & run
Configure, inspect, prepare, and execute in one workspace.
Pro guides the workflow and exposes generated code. Team can edit the complete versioned manifest before anything is persisted or billed.
WAREHOUSE AUTONOMY
Build & run
Environmentcell-04warehouse twin 4.8Scenariooccluded-pickcontrolled conditionSeeds12one run per seedExecutionManaged CPUauthorization required
WORKFLOW MANIFEST / TEAM
payload-and-friction-shift
Guided inputs and the editable manifest describe the same immutable scenario workflow.
- Schema
- studio-scenario-workflow.v1
- Perturbation
- payload mass + friction shift
- Code view
- Python + JSON generated
- Safety
- Validate first · compute starts separately
1 of 8
REALISTIC STATIC FIXTURE · NO ACCOUNT OR LIVE SYSTEM DATA REQUIRED
Two-minute quickstart
From install to an actionable report in two commands.
Generate a real, versioned reliability-boundary report locally. Inspect its disposition, uncertainty, and content digest, then import the same JSON into Studio. No account is required to run the first report.
pip install iso-obs
python -m iso_obs.quickstart
# Wrote failure-boundary-report.json
# Disposition: partial
# Schema: iso-obs.failure-boundary-map.v1
# Next: open the JSON or import it in Studio → Evidence01InstallOne published Python package.02GenerateA versioned JSON evidence artifact.03ActReview locally or import into Studio.
Work with us
Start with one consequential deployment decision.
Design partners bring a working robot, an upcoming deployment, and existing evidence. Individual researchers can use the SDK and request bounded Studio access for rigorous independent work.
01 / DESIGN PARTNER
Deployment Readiness Audit
A paid, scoped engagement covering one system and one real deployment decision. Contact us for fit and scope.
02 / INDIVIDUAL RESEARCHER
Scientifically robust tooling
Use the published SDK locally. Request time-bound Studio access when the research question needs a hosted evidence workflow.
Research Access
Research access is funded, not sold through an enterprise funnel.
Academic, nonprofit, and independent researchers can apply for time-bound Pro access and a one-time grant of 100 managed worker-hours. Bring institutional compute when you have it; request funded capacity when you do not.
Apply for research accessApplications determine subsidy—not ordinary product access. No claim over your models, datasets, or research outputs.
Design partnerships