ONLINE
24 CAMS
3 ANOMALIES
482 FPS
CAM_07 — LINE_7_STATION_B
RES: 1920x1080 @ 60FPS
CODEC: H.265 / NVENC
LAT: 47ms — NOMINAL
REC
FRAME: 00184729
UPTIME: 72h:14m:08s
GPU: H100 — 83% UTIL
DETECTED: INDUSTRIAL_VISION_PLATFORM — CONF 99.2%

What the robot sees,
the robot understands

Clobotics runs GPU-accelerated computer vision on industrial robots — detecting defects, guiding pick-and-place, and coordinating fleets across the factory floor in real time.

THROUGHPUT: 1,200 parts/hr
LATENCY: 47ms p99
ACCURACY: 99.2%
STREAMS: 2,048
detected_modules — 6 objects

Vision modules

Each module runs as an independent inference pipeline on Triton Server, hot-swappable without dropping frames. Deploy only what your production line needs.

99.2%
MOD_001 — DEFECT_DETECT

Surface Defect Detection

YOLOv8 + ResNet pipeline detects micro-defects on moving parts — scratches, dents, coating voids, misalignment. Sub-pixel precision at 482 FPS.

482 FPS0.1mm min defectTensorRT INT8
99.7%
MOD_002 — POSE_EST

Pick-and-Place Guidance

6-DoF pose estimation guides robotic arms for bin picking and component placement. Trained on Isaac Sim synthetic data, refined with real-world fine-tuning.

6-DoF pose3,600 placements/hrIsaac Sim
98.8%
MOD_003 — FLEET_RL

Fleet Coordination

Multi-agent RL coordinates hundreds of mobile robots. Dynamic task allocation, collision avoidance, and route optimization via NVIDIA cuOpt.

10K envs120+ robotscuOpt
94.1%
MOD_004 — PRED_MAINT

Predictive Maintenance

Time-series anomaly detection on motor current, vibration, and temperature. Predicts component failures 72 hours in advance — 60% fewer unplanned outages.

72h forecast-60% downtimeLSTM
99.9%
MOD_005 — TELEOP

Remote Operations

Cloud-based teleoperation with WebRTC video streaming and GPU-accelerated decision support. Operators intervene when confidence drops below threshold.

80ms glass-to-glassWebRTCAuto-handoff
99.5%
MOD_006 — EDGE_INF

Edge Inference

Models quantized to INT8 and deployed on NVIDIA Jetson Orin for on-robot inference. Zero cloud dependency for real-time control loops. OTA fleet updates.

30 FPS on-deviceJetson OrinINT8
live_inspection_feed

Real-world deployments

Live inspection log from production lines running Clobotics vision systems across automotive, electronics, logistics, and pharmaceutical manufacturing.

● INSPECTION_LOG — LINE_07B
CAM_07 — 1920x1080 — H.265 — FRAME 00184729
14:22:18
PASS Automotive panel A-2241 — no defects detected
99.8%
OK
14ms
14:22:19
WARN Surface scratch 0.3mm on panel A-2242 — within tolerance
87.2%
TOLERABLE
12ms
14:22:20
PASS PCB component placement — 64/64 components verified
99.9%
OK
11ms
14:22:21
FAIL Solder bridge on U17 — flagged for rework station
23.1%
REJECT
14ms
14:22:22
PASS Fleet robot AGV-047 reached station 12 — collision-free path
98.4%
OK
8ms
14:22:23
PASS Tablet inspection — color, shape, coating within spec
99.7%
OK
15ms
14:22:24
WARN Motor vibration anomaly on robot R-12 — predicted maintenance in 68h
76.3%
SCHEDULE
9ms
14:22:25
PASS Bin picking — 6-DoF pose acquired, 3 objects located
97.8%
OK
11ms
system_architecture

Three-layer pipeline

From camera ingestion to robotic actuation — every layer runs on NVIDIA accelerated computing. Edge inference stays local for real-time control; training and fleet management run in the cloud.

EDGE
Camera Array
24x GigE — 1080p60
Jetson Orin
INT8 inference — 30FPS
NVENC H.265
Stream encode
ROS2 Bridge
Robot control
| | | | DATA_FLOW — 25 Gbps
INFER
Triton Server
Multi-model serving
TensorRT
Engine optimization
DeepStream
Stream processing
Post-Proc
NMS + tracking
| | | | GRADIENT_SYNC — 40 Gbps
CLOUD
H100 Cluster
8x GPU training
Model Registry
Versioned weights
Fleet Manager
OTA + monitoring
Data Lake
200TB+ storage
10K+
Sim Environments
Isaac Sim parallel
47ms
Inference SLA
p99 latency
100K+
GPU Training Hours
H100 + A100
99.2%
Detection Accuracy
40+ customers
training_pipeline — 5 stages

Sim to production in 48 hours

Our sim-to-real pipeline generates millions of labeled images in Isaac Sim, pre-trains on H100 clusters, fine-tunes on customer data, and deploys optimized models to the edge — all in under 48 hours.

STAGE_01

Synthetic Data

Generate millions of labeled images in Isaac Sim with domain randomization and physics-accurate rendering.

STAGE_02

Pre-Train

Train base models on H100 clusters with mixed precision, gradient accumulation, and multi-GPU distribution.

STAGE_03

Fine-Tune

Adapt to customer-specific data with LoRA fine-tuning on real production images from the factory floor.

STAGE_04

Optimize

Quantize to INT8, compile TensorRT engines, validate on Jetson Orin target hardware for latency.

STAGE_05

Deploy

Push to fleet via OTA updates. Monitor with Triton metrics, DeepStream analytics, and live inspection log.

technology_stack — 12 components

Built on NVIDIA

Every layer of our stack runs on NVIDIA accelerated computing — from training on H100 clusters to inference on Jetson Orin at the edge. Optimized for maximum throughput and minimum latency.

H100
Training
A100
Fine-tune
Jetson Orin
Edge GPU
Isaac Sim
Simulation
TensorRT
Optimize
Triton
Serve
DeepStream
Stream
CUDA 12
Kernels
cuOpt
Routing
cuDNN
Primitives
PyTorch
Training
ROS2
Robot I/O
// INITIATE_DEPLOYMENT

Ready to see what your robots see?

Talk to our team about your production line, throughput targets, and integration requirements.