From microsecond GPU inference to autonomous fleet coordination across industrial edge environments. NOUVX delivers mission-critical physical AI infrastructure, low-latency vision pipelines, and ROS 2 control systems.
Harnessing NVIDIA CUDA-X, TensorRT, and Isaac Sim to deliver hardware-accelerated intelligence across enterprise clouds, edge gateways, and robotics fleets.
Enterprise model serving optimized on NVIDIA Tensor Cores. Continuous dynamic batching, paged KV-cache, and quantized FP8 execution deliver blistering inference speeds with zero latency drift.
Core platform modules designed to operate together across inference, telemetry, robotics, monitoring, and automation environments.
A hardened backend layer for inference services, telemetry streams, model registries, and operator workflows.
Industrial detection, tracking, smart surveillance, edge analytics, and visual event routing for live environments.
Fleet coordination, ROS-compatible communication, control surfaces, and machine-state monitoring.
Predictive analytics, anomaly alerts, distributed compute maps, and automation pipelines for infrastructure teams.
Nine specialized engineering domains, each built on production-grade tooling and deep operational expertise.
Decision models, orchestration, agentic workflows, and supervised automation for operational environments.
Detection, tracking, segmentation, facial mapping, edge analytics, and industrial monitoring pipelines.
Robotic control software, ROS integration, machine communication, and autonomous command layers.
Model registries, inference services, distributed training, observability, and deployment automation.
Sensor fusion, planning logic, telemetry loops, fleet coordination, and real-time decision support.
Workflow automation, alerting, human-in-the-loop review, and intelligent software operations.
Enterprise APIs, event streams, data planes, identity controls, and resilient service architectures.
Dashboards, live system state, predictive analytics, and executive-level monitoring.
Hybrid cloud, edge compute, Kubernetes, message queues, and secure multi-site deployments.
A layered backend model for ingesting live signals, routing data through AI processing, executing inference, and surfacing decisions to operational dashboards and automation systems.
GPU-accelerated model execution, feature extraction, and real-time scoring on CUDA cores.
Node eu-west-1a scaled up
Queue depth exceeded threshold
GPU temp threshold reached on node-47
Model v2.4.1 deployed to inference
VisionGrid edge sync completed
INFERENCE
12ms P99 • 480K req/s
VISION
41 FPS • 12 nodes
ROBOTICS
76 fleet • Path locked
KAFKA
204 queue • 99.9% lag
KUBERNETES
12 nodes • 94% capacity
STORAGE
94% I/O • 2.4TB free
Production results and architecture breakdowns from mission-critical client implementations.
Live infrastructure telemetry, model-backed anomaly detection, alerting, and operator dashboards for a global cloud provider.
Computer vision pipelines for detection, tracking, inspection, and real-time facility analytics across 47 manufacturing sites.
ROS-compatible control surfaces, fleet telemetry, safety states, and hardware-system communication for autonomous warehouse fleet.
Forecasting, queue health, capacity signals, automated escalation, and reliability analytics for multi-region deployment.
Machine-state monitoring, production insights, robotics coordination, and automation triggers for discrete manufacturing.
Streaming data architecture, live dashboards, data quality checks, and distributed processing for fintech platform.
NEBULINT BUILD MATRIX — From model runtime to operational deployment.
8 technologies
Python 3.11+
Primary ML language
PyTorch 2.3
Deep learning framework
TensorFlow 2.16
Production ML platform
ONNX Runtime 1.18
Cross-platform inference
TensorRT 10
NVIDIA inference optimizer
vLLM 0.5
High-throughput LLM serving
Transformers 4.40
Hugging Face models
Accelerate 0.30
Distributed training
8 technologies
ROS 2 Humble
Robotics middleware
Nav2
Navigation stack
MoveIt 2
Motion planning
Isaac Sim
Robot simulation
Micro-ROS
Embedded robotics
Fast DDS
Real-time middleware
Gazebo Harmonic
Physics simulation
ROS 2 Control
Hardware abstraction
8 technologies
Kubernetes 1.29
Container orchestration
Docker 26
Container runtime
Helm 3.14
Package manager
ArgoCD 2.10
GitOps continuous delivery
Istio 1.21
Service mesh
Cilium 1.15
eBPF networking
Cert-Manager 1.14
Certificate management
External Secrets 0.9
Secret synchronization
8 technologies
React 18
UI framework
TypeScript 5.4
Type safety
Tailwind CSS 4
Utility-first CSS
Framer Motion 11
Animation library
Three.js r160
3D graphics
Recharts 2.12
Charting library
Socket.io 4.7
Real-time communication
Zustand 4.5
State management
8 technologies
FastAPI 0.110
High-performance API
Redis 7.2
In-memory store
Apache Kafka 3.6
Event streaming
PostgreSQL 16
Relational database
ClickHouse 23.12
Analytical database
TimescaleDB 2.14
Time-series database
NATS 2.10
Messaging system
gRPC 1.62
RPC framework
8 technologies
MLflow 2.13
ML lifecycle
Kubeflow 1.8
ML on Kubernetes
Feast 0.32
Feature store
DVC 3.4
Data version control
Great Expectations 0.18
Data quality
Airflow 2.9
Workflow orchestration
Prefect 2.18
Modern workflows
Temporal 1.22
Durable execution
Cross-category technology compatibility matrix
| TECHNOLOGY | CATEGORY | VERSION | PURPOSE | STATUS |
|---|---|---|---|---|
| Python 3.11+ | AI RUNTIME | v3.11 | Primary ML language | ACTIVE |
| PyTorch 2.3 | AI RUNTIME | v2.3 | Deep learning framework | ACTIVE |
| TensorFlow 2.16 | AI RUNTIME | v2.16 | Production ML platform | ACTIVE |
| ONNX Runtime 1.18 | AI RUNTIME | v1.18 | Cross-platform inference | ACTIVE |
| TensorRT 10 | AI RUNTIME | v10 | NVIDIA inference optimizer | ACTIVE |
| vLLM 0.5 | AI RUNTIME | v0.5 | High-throughput LLM serving | ACTIVE |
| Transformers 4.40 | AI RUNTIME | v4.40 | Hugging Face models | ACTIVE |
| Accelerate 0.30 | AI RUNTIME | v0.30 | Distributed training | ACTIVE |
| ROS 2 Humble | ROBOTICS CONTROL | vHumble | Robotics middleware | ACTIVE |
| Nav2 | ROBOTICS CONTROL | v1.1 | Navigation stack | ACTIVE |
| MoveIt 2 | ROBOTICS CONTROL | v2.9 | Motion planning | ACTIVE |
| Isaac Sim | ROBOTICS CONTROL | v2023.1 | Robot simulation | ACTIVE |
| Micro-ROS | ROBOTICS CONTROL | v2.3 | Embedded robotics | ACTIVE |
| Fast DDS | ROBOTICS CONTROL | v2.10 | Real-time middleware | ACTIVE |
| Gazebo Harmonic | ROBOTICS CONTROL | vHarmonic | Physics simulation | ACTIVE |
| ROS 2 Control | ROBOTICS CONTROL | v1.4 | Hardware abstraction | ACTIVE |
| Kubernetes 1.29 | INFRASTRUCTURE | v1.29 | Container orchestration | ACTIVE |
| Docker 26 | INFRASTRUCTURE | v26 | Container runtime | ACTIVE |
| Helm 3.14 | INFRASTRUCTURE | v3.14 | Package manager | ACTIVE |
| ArgoCD 2.10 | INFRASTRUCTURE | v2.10 | GitOps continuous delivery | ACTIVE |
| Istio 1.21 | INFRASTRUCTURE | v1.21 | Service mesh | ACTIVE |
| Cilium 1.15 | INFRASTRUCTURE | v1.15 | eBPF networking | ACTIVE |
| Cert-Manager 1.14 | INFRASTRUCTURE | v1.14 | Certificate management | ACTIVE |
| External Secrets 0.9 | INFRASTRUCTURE | v0.9 | Secret synchronization | ACTIVE |
| React 18 | OPERATIONS INTERFACES | v18 | UI framework | ACTIVE |
| TypeScript 5.4 | OPERATIONS INTERFACES | v5.4 | Type safety | ACTIVE |
| Tailwind CSS 4 | OPERATIONS INTERFACES | v4 | Utility-first CSS | ACTIVE |
| Framer Motion 11 | OPERATIONS INTERFACES | v11 | Animation library | ACTIVE |
| Three.js r160 | OPERATIONS INTERFACES | vr160 | 3D graphics | ACTIVE |
| Recharts 2.12 | OPERATIONS INTERFACES | v2.12 | Charting library | ACTIVE |
| Socket.io 4.7 | OPERATIONS INTERFACES | v4.7 | Real-time communication | ACTIVE |
| Zustand 4.5 | OPERATIONS INTERFACES | v4.5 | State management | ACTIVE |
| FastAPI 0.110 | BACKEND PLATFORM | v0.110 | High-performance API | ACTIVE |
| Redis 7.2 | BACKEND PLATFORM | v7.2 | In-memory store | ACTIVE |
| Apache Kafka 3.6 | BACKEND PLATFORM | v3.6 | Event streaming | ACTIVE |
| PostgreSQL 16 | BACKEND PLATFORM | v16 | Relational database | ACTIVE |
| ClickHouse 23.12 | BACKEND PLATFORM | v23.12 | Analytical database | ACTIVE |
| TimescaleDB 2.14 | BACKEND PLATFORM | v2.14 | Time-series database | ACTIVE |
| NATS 2.10 | BACKEND PLATFORM | v2.10 | Messaging system | ACTIVE |
| gRPC 1.62 | BACKEND PLATFORM | v1.62 | RPC framework | ACTIVE |
| MLflow 2.13 | DATA & ML OPS | v2.13 | ML lifecycle | ACTIVE |
| Kubeflow 1.8 | DATA & ML OPS | v1.8 | ML on Kubernetes | ACTIVE |
| Feast 0.32 | DATA & ML OPS | v0.32 | Feature store | ACTIVE |
| DVC 3.4 | DATA & ML OPS | v3.4 | Data version control | ACTIVE |
| Great Expectations 0.18 | DATA & ML OPS | v0.18 | Data quality | ACTIVE |
| Airflow 2.9 | DATA & ML OPS | v2.9 | Workflow orchestration | ACTIVE |
| Prefect 2.18 | DATA & ML OPS | v2.18 | Modern workflows | ACTIVE |
| Temporal 1.22 | DATA & ML OPS | v1.22 | Durable execution | ACTIVE |
Bring us a system problem — AI product development, computer vision, robotics software, automation, backend infrastructure, or monitoring platforms.
Security
Zero-trust, encryption, compliance
Reliability
99.999% uptime, chaos engineering
Autonomy
Self-healing, adaptive systems
Intelligence
AI-native, model-driven ops
Every engagement is a systematic progression from problem definition to production deployment, with clear milestones.
System requirements, constraints, and operational context analysis.
Technical design, technology selection, and integration planning.
Rapid validation, proof-of-concept, and iterative refinement.
Production rollout, monitoring setup, and knowledge transfer.
Reach out directly — our engineering team responds promptly with a qualified technical review.
Deployment Modes
Core Work
