Technical Blueprint

Physical AI Architecture

Layer-by-layer technical specifications for enterprise Physical AI & Cyber-Physical Systems — from sensorimotor telemetry ingestion to low-latency closed-loop actuation and multi-tenant inference.

Physical AI Architecture
4 Cyber-Physical Layers
Physical AI & Hardware Standards

Model Hardware Standard (MHS)* Blueprint

Standardized MHS Protocol*Robotics · Industrial · LIS

The Model Hardware Standard (MHS)* provides a vendor-agnostic abstraction layer connecting heterogeneous physical machines, robotics, edge sensor gateways, and precision instruments to multi-tenant AI foundation models and autonomous closed-loop workflows.

FIGURE 1.1 — Unified MHS Architecture, Telemetry Ingestion & Automated Action Loops
📥 READ COMMANDS (GET)
  • › Get Sensor & State Telemetry
  • › Get Consumable & Fluid Levels
  • › Get Hardware Calibration Drift
  • › Get Anomaly Flags & Diagnostics
📤 WRITE COMMANDS (SET)
  • › Trigger Actuation / Assay Cycle
  • › Execute Autonomous Calibration
  • › Set Edge Safety & Interlock Rules
  • › Acknowledge & Route Alert Events
⚡ AUTOMATED ACTIONS LOOP
  • › Autonomous Part & Consumable Reorder
  • › Schedule Predictive Maintenance
  • › Lock Out-of-Tolerance Actuators
  • › Real-Time Field Ops Dispatch
FIGURE 1.2 — Physical Biochemistry & Hematology Analyzer (BA-HA 5000) Digital Twin

* Disclaimer: Model Hardware Standard (MHS) is a forward-looking architectural specification and future standard aspiration, subject to access and integration permissions from the OEM / Anthropic.

01 — Cyber-Physical Ingestion & Actuation Layer

Cyber-Physical Ingestion & Actuation Layer

Apache KafkaApache FlinkgRPCMQTTOptical BusAWS Kinesis
Data Flow
Physical AI / Sensors / Robots
MQTT / gRPC / Optical Gateway
Kafka (Tenant-Partitioned)
Flink Sensorimotor Processor
MHS Schema & Safety Validator
Closed-Loop Router → Actuators
Technical Specifications
  • Kafka topics partitioned by tenant_id ensuring hard isolation across physical machine fleets
  • Flink stateful stream processing for sensorimotor enrichment, deduplication, and anomaly triggers
  • Sub-20ms p95 ingest-to-actuation latency with high-rate batching and compression
  • 5M+ physical device & sensor log capacity with horizontal partition scaling
  • Model Hardware Standard (MHS)* schema registry enforcing strict hardware contracts
02 — Spatial, Hardware & Calibration RAG

Spatial, Hardware & Calibration RAG

LangChainpgvectorChromaDBFastAPISentenceTransformersCross-Encoder
Data Flow
Equipment Manuals / CAD / Runbooks
Domain-Aware Chunker
Dense Embeddings (SentenceTransformers)
pgvector / ChromaDB Index
Hybrid Retrieval (BM25 + Dense)
LLM Reranker → Reasoning Response
Technical Specifications
  • Hybrid dense + BM25 sparse retrieval for high-precision physical & calibration accuracy
  • Hardware-aware chunking prevents proprietary and sensitive operational leakage across tenants
  • Cross-encoder reranking improves answer precision by ~30% on complex multi-parameter queries
  • Automated extraction of calibration tolerances, error codes, and maintenance runbooks
  • <200ms end-to-end retrieval latency at p95 for real-time field operations
03 — Physical AI & Telemetry Fine-Tuning Pipeline

Physical AI & Telemetry Fine-Tuning Pipeline

PyTorchHuggingFace PEFTLoRAQLoRAWeights & BiasesMLflowFSDP
Data Flow
Physical Telemetry Dataset
4-bit Quantization (QLoRA)
LoRA Adapter Training
FSDP Distributed Training
W&B Telemetry Tracking
Fine-tuned Checkpoint → Registry
Technical Specifications
  • LoRA rank-16 adapters add <0.1% parameters vs. full fine-tune for fast adapter hot-swapping
  • QLoRA 4-bit NF4 quantization reduces GPU memory by 60–70% for edge and cluster training
  • FSDP sharding across 8–32 GPUs for foundation model adaptation on sensor streams
  • +15–25% improvement in physical anomaly classification and state estimation
  • W&B and MLflow for experiment comparison, telemetry lineage, and model registry
04 — Secure Edge & Cloud Inference Architecture

Secure Edge & Cloud Inference Architecture

vLLMRay ServeKubernetesIstioOPAHashiCorp VaultKEDA
Data Flow
Cyber-Physical API Request
Istio Gateway + mTLS
OPA Safety Interlock Controller
Deterministic Policy & NER Layer
Ray Serve Router → vLLM Engine
Namespace-Isolated Actuation
Technical Specifications
  • Kubernetes namespace-per-tenant for hard compute, network, and hardware isolation
  • Istio service mesh with mTLS for all inter-service traffic with SPIFFE identity
  • OPA admission controller enforces physical safety rules, compliance, and RBAC policies
  • vLLM PagedAttention + continuous batching maximizing GPU throughput with low jitter
  • KEDA scales GPU pods to zero on idle, saving 25–35% infrastructure costs
  • Vault-managed physical API credentials and hardware tenant encryption keys
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