Stage-X Infrastructure Documentation

Our Achievements

Getting Started

The sandbox era is over. Our documentation is built for the cyber-physical bridge, providing step-by-step guidance on embedding hands-on engineering directly into your VPC. Navigate through the sidebar to explore our rigorous standards for Data Privacy, Edge Security, and Thermodynamic Load Balancing.

Core Architecture

The Stage-X architecture is designed around distributed physical nodes. Unlike traditional cloud deployments where computation is centralized, Frontier Actuation requires localized inference directly on the manufacturing floor or energy grid.

Our autonomous swarms run on ultra-low power silicon (RISC-V extensions) to ensure that the orchestration layer remains operational even during primary power grid failures. Every edge node is provisioned with a stripped-down Linux kernel running a custom scheduler optimized for deterministic latency guarantees.

Node Topology

Each physical deployment consists of a minimum of three sovereign nodes arranged in a mesh topology. The mesh eliminates single points of failure and ensures that if any individual node is physically destroyed, the remaining nodes can autonomously redistribute inference workloads within 50 milliseconds. Inter-node communication uses a proprietary binary protocol over fiber-optic links, achieving 400 Gbps throughput with less than 2 microseconds of serialization overhead.

Inference Engine

The core inference engine is built on a custom fork of vLLM, optimized for air-gapped environments. Key modifications include the removal of all external HTTP dependencies, a custom CUDA memory allocator that pre-pins GPU memory at boot time to eliminate allocation jitter, and a novel speculative decoding implementation that achieves 3.2x throughput improvement on Llama-class models without sacrificing output quality.

Security & Compliance

Sovereign physical environments cannot afford external internet dependencies. The Stage-X protocol provides native support for complete network isolation. Our localized LLM weights are encrypted at rest using physical hardware keys, and all internal telemetry is routed through proprietary acoustic side-channel links to prevent RF interception.

Compliance Certifications

The Stage-X platform has been independently audited and certified against the following regulatory frameworks. All certifications are renewed annually, and full audit reports are available to enterprise customers under NDA.

• SOC 2 Type II: Verified controls for security, availability, processing integrity, confidentiality, and privacy across all physical edge deployments.
• ISO 27001:2022: Information Security Management System (ISMS) certification covering the full lifecycle of sovereign AI deployments.
• FedRAMP High: Authorization for U.S. federal government workloads requiring the highest security categorization (FIPS 199 High).
• GDPR Article 28: Full data processor compliance with binding contractual obligations for EU-based deployments, including data localization guarantees.
• HIPAA BAA: Business Associate Agreement coverage for healthcare-adjacent deployments processing Protected Health Information (PHI).

Cryptographic Standards

All data at rest is encrypted using AES-256-GCM with keys managed by on-premise Hardware Security Modules (HSMs). Key rotation occurs every 24 hours automatically. Data in transit between sovereign nodes uses TLS 1.3 with post-quantum key exchange (X25519Kyber768). Certificate pinning is enforced at the firmware level, making man-in-the-middle attacks physically impossible without hardware tampering.

Incident Response SLA

Critical security incidents are escalated within 15 minutes of detection. Our dedicated Security Operations Center (SOC) operates 24/7/365 with a guaranteed Mean Time to Acknowledge (MTTA) of under 5 minutes and a Mean Time to Remediate (MTTR) of under 4 hours for P0 incidents. All incident communications are encrypted using PGP keys pre-shared during customer onboarding.

A. Data & Privacy

In high-stakes enterprise deployments, data leakage can trigger catastrophic regulatory fines. Our approach ensures that any contextual data—such as CAD blueprints or unreleased zoning documentation—is immediately scrubbed using an isolated PII-filtering micro-model prior to being processed by the primary reasoning cluster.

Data Residency Guarantees

All inference data is guaranteed to remain within the physical boundaries of the deployment region. No data is ever transmitted to external cloud providers, third-party analytics services, or model training pipelines. Our architecture uses hardware-enforced geofencing: the network interface controllers (NICs) on sovereign nodes are physically configured to reject outbound packets destined for addresses outside the authorized subnet range.

PII Detection & Scrubbing Pipeline

Before any document enters the primary reasoning pipeline, it passes through a dedicated 7B-parameter PII detection model running on an isolated GPU partition. This model identifies and redacts 47 categories of personally identifiable information, including names, addresses, social security numbers, financial account numbers, biometric identifiers, and device fingerprints. The redaction process is cryptographically logged to ensure auditability without exposing the original PII.

• Ephemeral Memory: All scratchpad memory utilized by multi-agent swarms is cryptographically shredded using DoD 5220.22-M triple-pass overwrite immediately upon task completion. Memory pages are verified clean before reallocation.
• Sovereign State Storage: Edge data persistence relies on decentralized PostgreSQL clusters running entirely inside the customer VPC with row-level encryption and column-level access controls.
• Right to Erasure: Full GDPR Article 17 compliance. Customer data can be irreversibly purged from all nodes within 72 hours of a formal erasure request, verified by cryptographic proof-of-deletion certificates.
• Data Lineage Tracking: Every byte of data processed by the system is tagged with a provenance record indicating its origin, transformations applied, models that accessed it, and its final disposition (archived, deleted, or exported).

B. Edge Security & Air-Gapping

Sovereign physical environments cannot afford external internet dependencies. The Stage-X protocol provides native support for complete network isolation. Our localized LLM weights are encrypted at rest using physical hardware keys, and all internal telemetry is routed through proprietary acoustic side-channel links to prevent RF interception.

Air-Gap Implementation

True air-gapping goes beyond simply disconnecting from the internet. Our implementation includes physical removal of WiFi and Bluetooth chipsets from sovereign nodes, epoxy-sealed USB ports to prevent unauthorized peripheral connections, and custom firmware that refuses to boot if any unauthorized network interface is detected. Model weight updates are delivered via encrypted physical media (tamper-evident USB drives) using a two-person integrity verification protocol.

Acoustic Side-Channel Communication

For environments where even fiber-optic connections introduce unacceptable electromagnetic signatures, our acoustic routing protocol transmits data between nodes using ultrasonic frequencies (40–80 kHz) through purpose-built waveguides. This achieves throughput of up to 10 Mbps—sufficient for command-and-control signaling—while being completely undetectable by standard RF surveillance equipment.

• Zero-Trust Actuation: Every physical command must be cryptographically signed by at least two sovereign nodes before execution. Single-node commands are rejected by hardware-level policy enforcement in the Trusted Platform Module (TPM 2.0).
• RF-Silent Mode: Completely disables all radio-frequency emissions, relying solely on fiber-optic and acoustic routing. Verified by independent RF spectrum analysis during deployment commissioning.
• Self-Destruct Protocol: In the event of a physical breach, nodes can securely wipe all parameter weights and operational data in under 400 milliseconds using hardware-accelerated cryptographic erasure. The erasure is verified by on-die integrity monitors that confirm all NAND flash cells have been overwritten.
• Physical Tamper Detection: Each node chassis is equipped with accelerometers, light sensors, and pressure sensors that detect unauthorized physical access attempts and trigger immediate key zeroization.

C. Thermodynamic Load Balancing

Deploying frontier models at the edge introduces significant heat dissipation challenges. Our Thermodynamic Orchestrator monitors ambient temperatures and physical heat sinks across the entire cluster, intelligently migrating inference workloads to physically cooler zones of the warehouse to maintain optimal efficiency without requiring massive active cooling systems.

Metabolic Cost Model

Every inference request is assigned a "metabolic cost" measured in watts-per-token. This metric captures not just the GPU power draw, but the full thermodynamic impact including memory bus activity, NVLink traffic, and the resulting heat output that must be dissipated by the cooling infrastructure. The orchestrator continuously solves a constraint optimization problem to minimize total system entropy while satisfying latency SLAs.

Heat-Aware Scheduling

The scheduler maintains a real-time thermal map of every GPU die, memory module, and VRM across the cluster, sampled at 100 Hz via IPMI sensors. When a GPU junction temperature exceeds 78°C, workloads are preemptively migrated to cooler nodes before thermal throttling occurs. This proactive approach eliminates the 15–30% throughput degradation typically seen in thermally-constrained data centers.

Passive Cooling Integration

For deployments in environments where active cooling is impractical (e.g., underground bunkers, remote substations), the orchestrator integrates with passive cooling systems including heat pipes, phase-change materials, and geothermal heat exchangers. By coordinating inference scheduling with the thermal capacity of these passive systems, the platform can sustain 70% of peak throughput indefinitely without any fan or compressor-based cooling.

D. Accountability

The shift from generative AI to actuative AI brings strict liability requirements. Every action orchestrated by the Antigravity engine is tied to an immutable cryptographic ledger, ensuring full forensic transparency in the event of an industrial incident.

Forensic Audit Framework

When an autonomous system recalibrates a robotic arm on a manufacturing floor or reroutes megawatts of power across a grid, the causal chain must be fully reconstructable. Our forensic framework captures: the raw sensor input that triggered the decision, the exact model weights and configuration used for inference, the full chain-of-thought reasoning trace, the final actuation command, and the physical outcome measured by independent verification sensors.

Liability Attribution Engine

For regulated industries, the platform provides automated liability attribution. Each autonomous decision is scored on a responsibility matrix that assigns weighted accountability to: the model provider, the deployment operator, the data source owner, and the physical equipment manufacturer. This matrix is pre-negotiated during deployment onboarding and encoded as smart contract logic that executes automatically in the event of an incident.

• Immutable Audit Trails: All multi-agent negotiations and system calibrations are logged into an append-only distributed ledger using Merkle tree structures. Each entry is cryptographically chained to its predecessor, making retroactive modification computationally infeasible.
• Hardware-Level Timestamps: To prevent causality manipulation, log entries utilize hardware clock pulses verified via internal stratum-1 atomic standards with ±100 nanosecond accuracy.
• Chain-of-Thought Archival: The complete reasoning trace of every multi-agent decision is archived in compressed binary format. Traces can be replayed in a sandboxed environment to verify that the same inputs produce the same outputs (deterministic reproducibility).
• Regulatory Export: Audit logs can be exported in standardized formats compatible with NIST AI RMF, EU AI Act Article 13 transparency requirements, and ISO/IEC 42001 AI Management System standards.