Verification
without
Exposure
Verification
without
Exposure
Verification
without
Exposure
Aruspex delivers zero-knowledge verification for AI and computational systems that can't afford blind trust. From autonomous navigation to quantum benchmarking, we prove performance without revealing how systems work.
Aruspex delivers zero-knowledge verification for AI and computational systems that can't afford blind trust. From autonomous navigation to quantum benchmarking, we prove performance without revealing how systems work.
Aruspex delivers zero-knowledge verification for AI and computational systems that can't afford blind trust. From autonomous navigation to quantum benchmarking, we prove performance without revealing how systems work.
Zero-Knowledge Machine Learning (ZKML)
Zero-Knowledge Machine Learning (ZKML)
Zero-Knowledge Machine Learning (ZKML)
What It Is
Cryptographic verification of AI model outputs without exposing training data, model weights, or proprietary algorithms. Our ZKML framework generates mathematical proofs that AI predictions meet specified accuracy or safety thresholds—verifiable by third parties without revealing how the model works.
Problems We Solve
Model IP Protection: Deploy AI in multi-party environments (coalition ops, contractor networks) without exposing proprietary models
Adversarial Environments: Prove AI integrity in contested or denied environments where trust cannot be assumed
Regulatory Compliance: Demonstrate AI safety and fairness with cryptographic assurance, not just documentation
Technical Approach
Built on Circom circuit compilation and Groth16 proof systems, our framework transforms AI inference into verifiable computation. We generate zero-knowledge proofs (using BN254 elliptic curves and SnarkJS verification) that attest to model behavior without revealing architecture, weights, or training data. Outputs include verifiable artifacts (.wasm, .zkey, proof JSONs) deployable across edge and cloud environments.
Who It Is For
Defense primes integrating AI into classified systems, autonomous platform developers requiring third-party validation, and agencies deploying AI in multi-domain operations where model exposure creates operational risk.
What It Is
Cryptographic verification of AI model outputs without exposing training data, model weights, or proprietary algorithms. Our ZKML framework generates mathematical proofs that AI predictions meet specified accuracy or safety thresholds—verifiable by third parties without revealing how the model works.
Problems We Solve
Model IP Protection: Deploy AI in multi-party environments (coalition ops, contractor networks) without exposing proprietary models
Adversarial Environments: Prove AI integrity in contested or denied environments where trust cannot be assumed
Regulatory Compliance: Demonstrate AI safety and fairness with cryptographic assurance, not just documentation
Technical Approach
Built on Circom circuit compilation and Groth16 proof systems, our framework transforms AI inference into verifiable computation. We generate zero-knowledge proofs (using BN254 elliptic curves and SnarkJS verification) that attest to model behavior without revealing architecture, weights, or training data. Outputs include verifiable artifacts (.wasm, .zkey, proof JSONs) deployable across edge and cloud environments.
Who It Is For
Defense primes integrating AI into classified systems, autonomous platform developers requiring third-party validation, and agencies deploying AI in multi-domain operations where model exposure creates operational risk.
What It Is
Cryptographic verification of AI model outputs without exposing training data, model weights, or proprietary algorithms. Our ZKML framework generates mathematical proofs that AI predictions meet specified accuracy or safety thresholds—verifiable by third parties without revealing how the model works.
Problems We Solve
Model IP Protection: Deploy AI in multi-party environments (coalition ops, contractor networks) without exposing proprietary models
Adversarial Environments: Prove AI integrity in contested or denied environments where trust cannot be assumed
Regulatory Compliance: Demonstrate AI safety and fairness with cryptographic assurance, not just documentation
Technical Approach
Built on Circom circuit compilation and Groth16 proof systems, our framework transforms AI inference into verifiable computation. We generate zero-knowledge proofs (using BN254 elliptic curves and SnarkJS verification) that attest to model behavior without revealing architecture, weights, or training data. Outputs include verifiable artifacts (.wasm, .zkey, proof JSONs) deployable across edge and cloud environments.
Who It Is For
Defense primes integrating AI into classified systems, autonomous platform developers requiring third-party validation, and agencies deploying AI in multi-domain operations where model exposure creates operational risk.
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What It Is
Hardware-agnostic verification frameworks that validate AI and sensor outputs across heterogeneous systems—from space-based platforms to GPS-denied ground operations—ensuring data integrity and interoperability without centralized trust.
Problems We Solve
Multi-Vendor Integration: Verify outputs from diverse AI systems (different vendors, architectures, security domains) with standardized cryptographic proofs
Space-to-Ground Validation: Ensure sensor and inference data maintains integrity across orbital, atmospheric, and terrestrial transitions
GPS-Denied Operations: Validate SLAM mapping and semantic layout predictions in contested environments where traditional ground truth is unavailable
Technical Approach
Our verification layer sits above hardware-specific implementations, accepting outputs from CNNs, transformers, traditional algorithms, or sensor fusion pipelines. We apply zero-knowledge proofs to validate that outputs meet mission-defined thresholds (accuracy, drift bounds, semantic consistency) regardless of the underlying system architecture. Current applications include thermal signal modeling for space hardware (NASA) and semantic SLAM for autonomous mapping (DTRA).
Who It Is For
System integrators managing multi-vendor AI pipelines, mission planners requiring cross-domain data assurance, and program offices standardizing AI validation across diverse platforms (air, space, ground, undersea).
What It Is
Hardware-agnostic verification frameworks that validate AI and sensor outputs across heterogeneous systems—from space-based platforms to GPS-denied ground operations—ensuring data integrity and interoperability without centralized trust.
Problems We Solve
Multi-Vendor Integration: Verify outputs from diverse AI systems (different vendors, architectures, security domains) with standardized cryptographic proofs
Space-to-Ground Validation: Ensure sensor and inference data maintains integrity across orbital, atmospheric, and terrestrial transitions
GPS-Denied Operations: Validate SLAM mapping and semantic layout predictions in contested environments where traditional ground truth is unavailable
Technical Approach
Our verification layer sits above hardware-specific implementations, accepting outputs from CNNs, transformers, traditional algorithms, or sensor fusion pipelines. We apply zero-knowledge proofs to validate that outputs meet mission-defined thresholds (accuracy, drift bounds, semantic consistency) regardless of the underlying system architecture. Current applications include thermal signal modeling for space hardware (NASA) and semantic SLAM for autonomous mapping (DTRA).
Who It Is For
System integrators managing multi-vendor AI pipelines, mission planners requiring cross-domain data assurance, and program offices standardizing AI validation across diverse platforms (air, space, ground, undersea).
Cross-Domain Verification
Cross-Domain Verification
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What It Is
Hardware-agnostic verification frameworks that validate AI and sensor outputs across heterogeneous systems—from space-based platforms to GPS-denied ground operations—ensuring data integrity and interoperability without centralized trust.
Problems We Solve
Multi-Vendor Integration: Verify outputs from diverse AI systems (different vendors, architectures, security domains) with standardized cryptographic proofs
Space-to-Ground Validation: Ensure sensor and inference data maintains integrity across orbital, atmospheric, and terrestrial transitions
GPS-Denied Operations: Validate SLAM mapping and semantic layout predictions in contested environments where traditional ground truth is unavailable
Technical Approach
Our verification layer sits above hardware-specific implementations, accepting outputs from CNNs, transformers, traditional algorithms, or sensor fusion pipelines. We apply zero-knowledge proofs to validate that outputs meet mission-defined thresholds (accuracy, drift bounds, semantic consistency) regardless of the underlying system architecture. Current applications include thermal signal modeling for space hardware (NASA) and semantic SLAM for autonomous mapping (DTRA).
Who It Is For
System integrators managing multi-vendor AI pipelines, mission planners requiring cross-domain data assurance, and program offices standardizing AI validation across diverse platforms (air, space, ground, undersea).
Cross-Domain Verification
Verifiable Autonomy
Verifiable Autonomy
What It Is
Audit-ready AI frameworks that make autonomous system decisions explainable, traceable, and cryptographically verifiable—meeting DoD's Responsible AI requirements while maintaining operational security.
Problems We Solve
Regulatory Barriers: Meet emerging AI safety and explainability requirements without exposing sensitive model details
Post-Mission Audits: Provide cryptographic evidence of AI decision-making for after-action reviews, legal compliance, or failure analysis
Human-AI Teaming: Enable operators to understand and trust AI recommendations in time-critical situations
Technical Approach
We combine explainable AI techniques (architectural priors, semantic grounding) with zero-knowledge verification to create "explain without exposing" systems. For autonomous navigation, we use structural constraints (building orthogonality, symmetry patterns) to make layout predictions interpretable, then generate ZK proofs that predictions satisfy mission constraints. Outputs include both human-readable spatial hypotheses and cryptographic attestations of inference validity.
Who It Is For
Autonomous vehicle programs requiring explainable AI, counter-WMD operations needing auditability under threat conditions, and any system where "trust the algorithm" isn't acceptable to operators or oversight bodies.
What It Is
Audit-ready AI frameworks that make autonomous system decisions explainable, traceable, and cryptographically verifiable—meeting DoD's Responsible AI requirements while maintaining operational security.
Problems We Solve
Regulatory Barriers: Meet emerging AI safety and explainability requirements without exposing sensitive model details
Post-Mission Audits: Provide cryptographic evidence of AI decision-making for after-action reviews, legal compliance, or failure analysis
Human-AI Teaming: Enable operators to understand and trust AI recommendations in time-critical situations
Technical Approach
We combine explainable AI techniques (architectural priors, semantic grounding) with zero-knowledge verification to create "explain without exposing" systems. For autonomous navigation, we use structural constraints (building orthogonality, symmetry patterns) to make layout predictions interpretable, then generate ZK proofs that predictions satisfy mission constraints. Outputs include both human-readable spatial hypotheses and cryptographic attestations of inference validity.
Who It Is For
Autonomous vehicle programs requiring explainable AI, counter-WMD operations needing auditability under threat conditions, and any system where "trust the algorithm" isn't acceptable to operators or oversight bodies.
Find Out More
Verifiable Autonomy
What It Is
Audit-ready AI frameworks that make autonomous system decisions explainable, traceable, and cryptographically verifiable—meeting DoD's Responsible AI requirements while maintaining operational security.
Problems We Solve
Regulatory Barriers: Meet emerging AI safety and explainability requirements without exposing sensitive model details
Post-Mission Audits: Provide cryptographic evidence of AI decision-making for after-action reviews, legal compliance, or failure analysis
Human-AI Teaming: Enable operators to understand and trust AI recommendations in time-critical situations
Technical Approach
We combine explainable AI techniques (architectural priors, semantic grounding) with zero-knowledge verification to create "explain without exposing" systems. For autonomous navigation, we use structural constraints (building orthogonality, symmetry patterns) to make layout predictions interpretable, then generate ZK proofs that predictions satisfy mission constraints. Outputs include both human-readable spatial hypotheses and cryptographic attestations of inference validity.
Who It Is For
Autonomous vehicle programs requiring explainable AI, counter-WMD operations needing auditability under threat conditions, and any system where "trust the algorithm" isn't acceptable to operators or oversight bodies.
Find Out More
Quantum and Advanced Computing Validation
Quantum and Advanced Computing Validation
What It Is
Zero-knowledge benchmarking frameworks for quantum computers and emerging computational platforms where traditional testing methods fail or expose proprietary system details.
Problems We Solve
Vendor Claims Verification: Independently validate quantum computer performance (error rates, fidelity, logical qubit counts) without accessing proprietary control systems
Multi-Architecture Comparison: Standardize benchmarking across superconducting, trapped-ion, photonic, and topological qubit platforms
IP-Protected Testing: Enable quantum hardware developers to prove performance milestones to customers/investors without revealing architectural details
Technical Approach
We adapt our ZKML verification framework to quantum-specific metrics: gate fidelity, error correction thresholds, coherence times, and algorithmic benchmarks. Our hardware-agnostic approach accepts performance data from any qubit platform, generates cryptographic proofs of threshold achievement, and enables independent validation—critical as the quantum industry moves toward fault-tolerant, utility-scale systems.
Who It Is For
Quantum computing companies needing third-party validation, government agencies evaluating quantum investments, and independent testing labs requiring standardized verification methods.
What It Is
Zero-knowledge benchmarking frameworks for quantum computers and emerging computational platforms where traditional testing methods fail or expose proprietary system details.
Problems We Solve
Vendor Claims Verification: Independently validate quantum computer performance (error rates, fidelity, logical qubit counts) without accessing proprietary control systems
Multi-Architecture Comparison: Standardize benchmarking across superconducting, trapped-ion, photonic, and topological qubit platforms
IP-Protected Testing: Enable quantum hardware developers to prove performance milestones to customers/investors without revealing architectural details
Technical Approach
We adapt our ZKML verification framework to quantum-specific metrics: gate fidelity, error correction thresholds, coherence times, and algorithmic benchmarks. Our hardware-agnostic approach accepts performance data from any qubit platform, generates cryptographic proofs of threshold achievement, and enables independent validation—critical as the quantum industry moves toward fault-tolerant, utility-scale systems.
Who It Is For
Quantum computing companies needing third-party validation, government agencies evaluating quantum investments, and independent testing labs requiring standardized verification methods.
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Quantum and Advanced Computing Validation
What It Is
Zero-knowledge benchmarking frameworks for quantum computers and emerging computational platforms where traditional testing methods fail or expose proprietary system details.
Problems We Solve
Vendor Claims Verification: Independently validate quantum computer performance (error rates, fidelity, logical qubit counts) without accessing proprietary control systems
Multi-Architecture Comparison: Standardize benchmarking across superconducting, trapped-ion, photonic, and topological qubit platforms
IP-Protected Testing: Enable quantum hardware developers to prove performance milestones to customers/investors without revealing architectural details
Technical Approach
We adapt our ZKML verification framework to quantum-specific metrics: gate fidelity, error correction thresholds, coherence times, and algorithmic benchmarks. Our hardware-agnostic approach accepts performance data from any qubit platform, generates cryptographic proofs of threshold achievement, and enables independent validation—critical as the quantum industry moves toward fault-tolerant, utility-scale systems.
Who It Is For
Quantum computing companies needing third-party validation, government agencies evaluating quantum investments, and independent testing labs requiring standardized verification .
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“Intelligence is the ability to adapt to change.”
—Stephen Hawking
“Intelligence is the ability to adapt to change.”
—Stephen Hawking
“Intelligence is the ability to adapt to change.”
—Stephen Hawking