Infinite Innovation
AI · IoT · Digital Twins
by Dr. Amit Puri · Founder, OpenAGI Stack
Engineering enterprise platforms for Physical AI & Cyber-Physical Systems — high-throughput sensor telemetry at massive scale, spatial & calibration intelligence, LoRA fine-tuning for physical domain accuracy, and secure low-latency inference serving.
Platform Capabilities
Five production-proven pillars powering enterprise AI, IoT, and digital twin infrastructure at scale.
Multi-Tenant Physical AI Platform
5M+ Logs · 30k–60k Events/Day
Designed and scaled a high-availability multi-tenant platform ingesting over 5 million physical sensor and telemetry logs daily with 99.9% uptime SLA across robotics, edge nodes, and precision hardware.
Spatial & Calibration RAG
Hardware, Calibration & Operational Intelligence
Built enterprise RAG and document intelligence systems for hardware calibration, physical diagnostics, equipment runbooks, and clinical workflows with hybrid retrieval and cross-encoder re-ranking.
Physical AI Fine-Tuning
+15–25% Anomaly & State Classification
Implemented production-grade LoRA and QLoRA fine-tuning pipelines, optimizing domain accuracy on physical telemetry and sensorimotor datasets while reducing GPU memory requirements.
Secure Edge & Cloud Inference
Ray Serve · Kubernetes · Hard Isolation
Designed secure multi-tenant inference architecture using Ray Serve and vLLM on Kubernetes with hard tenant isolation, automated NER redaction, and deterministic closed-loop safety controls.
GPU FinOps & Edge Compute
25–35% Infrastructure Cost Reduction
Reduced platform infrastructure cost by 25–35% through GPU utilization optimization, KEDA-based autoscaling, dynamic batching, and edge-to-cloud workload distribution.
Multi-Tenant Telemetry in Action
Multi-Tenant Telemetry & Sensorimotor Ingestion
Robotics, Physical AI & Digital Twins
Bridging physical machines, robotics, edge sensors, and AI cloud foundation models with the Model Hardware Standard (MHS)* — unified sensorimotor telemetry, automated calibration diagnostics, and closed-loop actuation workflows.
Cyber-Physical Digital Twin & Autonomous Hardware Ingestion
Real-Time Sensorimotor Streams · Closed-Loop Telemetry to AI Cloud
High-throughput cyber-physical instrumentation integrated with the Model Hardware Standard (MHS)*. Continuously captures sensorimotor states, physical parameters, reagent & fluid dynamics, and environmental metrics, streaming encrypted high-rate telemetry to cloud foundation models for autonomous closed-loop optimization.
* Disclaimer: Model Hardware Standard (MHS) is a forward-looking architectural specification & future aspiration for physical AI & heterogeneous hardware, subject to access and integration permissions from the OEM / Anthropic.
Interactive Architecture Explorer
Platform Blueprint Explorer
Click a layer to explore its design, tech stack, and performance metrics.
⚡ Physical AI Ingestion
High-throughput multi-tenant ingestion pipeline with tenant-aware routing, schema validation, and real-time sensorimotor stream processing. Kafka partitioned by tenant ID ensures strict isolation for cyber-physical fleets.
- 5M+ physical logs/day
- 30k–60k sensor events
- Sub-20ms p95 ingest latency
GPU FinOps & Document Intelligence
Real models behind real production impact.
Infrastructure Cost Optimizer
Model real-world 25–35% infrastructure savings from GPU optimization strategies.
Clinical Document & PHI Redaction Demo
Calibration intelligence + clinical NER pipeline with automatic PHI redaction for HIPAA compliance.
Patient: John D. (DOB: 03/14/1978) MRN: 7823-AXQ | SSN: 555-23-4891 Referring Physician: Dr. Sarah Mitchell, MD Contact: jdoe@email.com | +1 (555) 234-7890 CALIBRATION REPORT — Device Lot: CAL-2025-087B Instrument: Glucose Analyzer GA-3000 Calibration Date: 2025-11-15 | Next Due: 2026-02-15 Tolerance: ±2.5 mg/dL | Result: PASS ✓ CLINICAL NOTES: Patient presents with fasting glucose 126 mg/dL (elevated). HbA1c: 7.2%. Referred for continuous glucose monitoring. Insurance: BlueCross ID #BC-9920-X | Group: 4478-HMO EXTRACTED ENTITIES: - Biomarker: Glucose 126 mg/dL [Elevated] - Biomarker: HbA1c 7.2% [Elevated] - Device Calibration: PASS — Lot CAL-2024-087B - Workflow: Continuous Glucose Monitoring Referral
Technology Ecosystem
AI / ML
Serving & Infra
Data & Streaming
Observability
Cloud & FinOps
Security & Compliance
Measurable Results
Multi-tenant AI platform SLA sustained over 12+ months, processing 5M+ device logs across concurrent tenant workloads.
LoRA and QLoRA fine-tuning pipelines achieving 15–25% uplift in domain-specific classification tasks vs. base model baselines.
Combined GPU utilization optimization, KEDA autoscaling, and vLLM batching delivering 25–35% GPU compute cost reduction.
Let's Build at Scale
Interested in enterprise AI platform engineering, RAG pipelines, LLM fine-tuning, or GPU FinOps? Connect with Dr. Amit Puri.