Independent verification for autonomous systems

Independent verificationfor Physical AI.

BoundaryScore™ independently validates existing performance metrics through measured motion and recognizes qualifying claims as BoundaryScore™ Verified.

See how it works Coming in 2027

Why It Matters

Important performance metrics are difficult to verify independently.

Physical AI companies use different metrics, definitions, and reporting practices. The result is evidence that can be difficult to verify independently.

BoundaryScore™ is being developed to provide the independent external reference the market is missing.

01

Different measures

Different metrics serve different performance objectives.

02

Different evidence

Self-reported data can lack independent physical validation.

03

Independent external reference

A consistent framework creates a credible basis for decisions.

How It Works

From a machine in motion to trusted, traceable evidence.

A measurement device is installed on the machine. Using TraceQ™, it observes real-world motion independently of the machine’s own software and reported state.

Measurement

  1. 01

    Install

    Fit an independent measurement device to the machine.

  2. 02

    Measure

    Observe real-world motion independently of onboard systems.

  3. 03

    Reconcile

    Compare observed behavior with machine-reported state.

  4. 04

    Verify

    Produce a traceable verification result with supporting evidence.

Designed across Physical AI

One framework for machines that move.

  • Autonomous vehicles
  • Passenger and commercial vehicles
  • Bipedal robots
  • Quadruped robots
  • Mobile and industrial machines

Verified Performance

A result people can read—and evidence experts can examine.

BoundaryScore™ Verified is the designation applied when a defined metric or performance claim meets the applicable verification requirements. The verification record preserves what was independently observed, what was participant-supplied, the conditions of the result and the evidence supporting the conclusion. It should not be read as a standalone safety certification.

BoundaryScore™ Verified — a designation for independently supported performance claims. Independently observed. Traceable evidence. Verified under defined conditions.

Standards and Neutrality

Built on standards. Designed to evolve.

BoundaryScore™ works with applicable standards, regulation, and domain expertise. It is designed to extend as technologies, operating conditions, and market requirements change.

Open and extensible

Built to expand across systems, domains, and evidence types.

Standards-aligned

Complements applicable standards and regulatory frameworks.

Auditable

Inputs, calculations, and evidence remain traceable and reviewable.

Documented methodology

Version-controlled methods support consistent, auditable verification.

Governance

Independent verification. Traceable evidence.

Verification

BoundaryScore™

The independent framework for verifying and communicating defined Physical AI performance metrics and claims.

Infrastructure

Paverly

Builds and operates the measurement, reconciliation, assessment and verification infrastructure.

Who It Serves

Verified evidence. Better decisions across the market.

BoundaryScore™ gives stakeholders independently verified evidence supporting existing performance metrics, reducing reliance on self-reported claims.

01

Oversight and public trust

Regulators, standards bodies, media, and the public gain a clearer, traceable reference.

02

Capital and risk

Insurers, financial institutions, and investors gain independently verified evidence to inform risk and investment decisions.

Resources

The thinking behind independent verification.

Methodology, governance and evidence for decisions across the market.

Foundational paper

Independent Verification for Physical AI

Explains the case for an external reference beyond the machine's own reporting systems.

Read whitepaper ↗

Data protection brief

BoundaryScore™ Data Covenant

Eight commitments governing how confidential participant evidence is collected, protected, used and disclosed.

Read brief ↗

Data rights Q&A

BoundaryScore™ Data Rights Q&A

Answers what evidence is required, who owns it, and who can — and cannot — see participant-specific data.

Read Q&A ↗

Interactive guide

Explore the Data Covenant

The eight commitments and the Who Can See What? tool, in one interactive page.

Explore →

Regulators & standards Q&A

A Real-Time Trust and Validation Layer

Explains how BoundaryScore™ complements oversight with independent motion verification and traceable performance evidence.

Read Q&A ↗

AV safety brief

The Missing Independent Signal for AV Safety Leaders

Explains why AV safety teams need an independent motion signal for monitoring, validation and investigation.

Read brief ↗

Insurance Q&A

Quantifying Performance and Insurer Risk

Shows how consistent, physics-based and auditable evidence can improve risk understanding and pricing.

Read Q&A ↗

Physical AI Q&A

Building Trust in Physical AI

Shows how independent, granular evidence supports builders, operators and the deployment ecosystem.

Read Q&A ↗

AV performance white paper

Independent Verification for AV Performance Metrics

A comprehensive look at how motion-based validation strengthens existing AV safety and performance metrics — from exposure and collisions to braking and trajectory — with a defined evidence chain for internal and external stakeholders.

Read whitepaper ↗

Physical AI brief

Independent Verification for the Metrics You Already Use

Explains how BoundaryScore™ adds trusted, provenance-backed evidence around metrics OEMs, regulators, insurers and standards bodies already rely on — without requiring a new proprietary score.

Read whitepaper ↗

Technical Q&A

Motion Validation Envelope™

Shows how alignment creates confidence and meaningful discrepancy creates evidence for review.

Read Q&A ↗

Foundational paper

TraceQ™: A New Way to Measure Motion

Introduces direct motion measurement as the technical foundation for independent machine-performance validation.

Read whitepaper ↗

Technical Q&A

Why Motion?

Explains why motion is universal, economical, privacy-enabling and uniquely suited to independent validation.

Read Q&A ↗

Technical Q&A

Verify the Metrics You Already Use

Explains how BoundaryScore™ adds independent motion evidence to the safety, reliability and performance metrics a company already uses — without replacing them.

Read Q&A ↗

Foundational paper

Verify Performance Metrics with Motion

Shows how independently observed motion checks machine-reported metrics and preserves a separate, traceable evidence chain.

Read whitepaper ↗

Foundational paper

The BoundaryScore™ Assurance Architecture

Explains how bounded claims, zero-trust evidence, motion reconciliation and a reproducible audit trail strengthen verification of existing Physical AI performance metrics.

Read whitepaper ↗

Foundational paper

The BoundaryScore™ Assurance Architecture — One-Page Overview

Summarizes how independent physical evidence, documented methodology and traceability support BoundaryScore™ Verified recognition for defined performance metrics.

Read whitepaper ↗

Technical Q&A

Technical Confidence in Six Questions

Explains how independent observation, zero-trust evidence, motion reconciliation and controlled change support trustworthy verification of existing metrics.

Read Q&A ↗

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