01 / MEASURE
Expose the interaction
Measure how masking, reduced sensing, and selective data sharing affect task outcomes, perception, and response time.
Assurance technology for Physical AI
Advance Physical AI with confidence.
TORVANU develops assurance technology that connects safety, reliability, and privacy—helping teams improve humanoid behavior, validate releases, and make informed deployment decisions.
SafetyEvaluate hazards within defined scenarios
ReliabilityMeasure the effects of system changes
PrivacyTest controls and residual exposure
EvidencePreserve what investigations need
The problem
Better performance must also mean trustworthy behavior.
A release can improve task success while exposing sensitive data. A privacy control can remove information a task needs. TORVANU brings these interactions into release evaluation so teams can identify risks and choose the right configuration.
02 / INVESTIGATE
Preserve useful evidence
Test which video, events, and telemetry an investigation needs—and what sensitive information can be removed.
03 / COMPARE
Isolate the regression
Compare releases with and without privacy controls to identify scenario-specific changes and their uncertainty.
04 / DECIDE
Support the next action
Link findings to reproducible tests so teams can revise a control, investigate a failure, or request more evidence.
Technology for deployment confidence
Validate changes. Advance deployment.
Joint evaluation connects operational performance, privacy controls, and incident evidence in one decision workflow. Identify regressions, assess information exposure, and preserve the evidence teams need to resolve failures and advance releases.
1
Set deployment requirements
Define task expectations, operating conditions, privacy policies, and acceptance criteria for your system.
2
Evaluate releases and controls
Compare software versions and privacy configurations against the same scenarios. Evaluate their effects on behavior and on retained incident evidence.
3
Measure outcomes and exposure
Connect task performance, safety events, and privacy exposure to the scenarios and configurations that need attention.
4
Act on traceable findings
Use versioned findings and source evidence to investigate failures, refine controls, and guide the next release.
For Physical AI teams
From first evaluation to every deployed humanoid.
Connect release evaluation, task readiness, and operational evidence across the humanoid lifecycle.
Who uses it
For the teams bringing humanoids into the world.
For humanoid manufacturers, Physical AI developers, systems integrators, and operators responsible for reliable deployment. Align engineering, safety, and privacy teams around evidence they can act on.
Deployment decisions
- Does a release improve tasks while creating new exposure?
- Which privacy controls preserve required performance?
- Can a minimized incident record still explain the failure?
- What evidence is needed before the next deployment step?
Independent by design
Specialized assurance within your existing stack.
TORVANU’s assurance architecture connects specialized evaluation with simulation, robotics middleware, and operational data—bringing decision-ready findings into existing engineering workflows.
Interoperable architecture
Work across development ecosystems.
An adapter-based architecture connects recorded runs, simulation results, and operational events to a common evaluation model, with interfaces designed around established robotics formats and workflows.
Integration targets: ROS 2 · MCAP · Gazebo · ManiSkill · Isaac
Specialized assurance technology
Turn evaluation into engineering action.
Evaluator packs, scenario-based comparisons, privacy exposure checks, and evidence-quality analysis connect technical findings to release and deployment decisions. The architecture supports customer-controlled data boundaries.
Evaluators · Benchmarks · Privacy probes · Evidence
Service portfolio
Assurance that grows with each humanoid.
A service portfolio spanning release assurance, task qualification, and recurring evaluation for each active humanoid. Work with us to define the scope for your program.
01 / RELEASE
Joint regression
Compare releases and privacy controls to identify operational regressions, sensitive-data exposure, and gaps in incident evidence.
02 / CAPABILITY
Task qualification
Define task-specific acceptance criteria and connect capability evidence to configuration, operating conditions, and software version.
03 / DEPLOYMENT
Humanoid assurance
A framework for recurring assurance per active humanoid, connecting deployment changes, permissions, and behavioral performance.
04 / LIFECYCLE
Trusted lifecycle records
Connect incident evidence, configuration changes, and capability records across operators, manufacturers, and assurance stakeholders.
Work with TORVANU
Advance your next deployment.
Bring your release, task-readiness, or privacy requirements. Let’s define an assurance engagement around the decisions your team needs to make.
Discuss your deployment ↗