Vision detection pipeline.
From raw frames to structured actions — capture, detect, track, and act in real time.
Five stages from signal to action
Each frame flows through the pipeline. Click a stage or watch it auto-cycle.
Capture
Camera, video, images
Preprocess
Resize, normalize, augment
Detect
YOLO, custom CNNs, classification
Track
Object tracking, persistence
Act
Alerts, tickets, workflows
Run where it makes sense
Edge for speed, cloud for power, hybrid for intelligence.
Edge
Deploy models directly on cameras or gateways for ultra-low latency inference.
< 50ms
typical latency
Cloud
Run heavy ensemble models with GPU clusters for complex multi-frame analysis.
200–500ms
typical latency
Hybrid
Smart routing sends simple frames to edge, complex scenes to cloud.
50–200ms
typical latency
Precision tuned in real time
Confidence thresholds, drift monitoring, and trade-off visualization keep your pipeline reliable.
Confidence threshold
Minimum score for a detection to count as valid.
Low
< 0.70
Medium
0.70–0.85
High
> 0.85
Drift monitoring
Detects when model accuracy degrades over time.
FP / FN trade-off
Precision vs recall — finding the right operating point.
Detections become decisions
Every confirmed detection routes to the right action — alerts, tickets, workflows, dashboards.
Alert systems
Real-time notifications via SMS, email, push, and dashboard alerts.
Ticket creation
Automatic issue tickets in Jira, ServiceNow, or custom platforms.
Workflow triggers
Kick off downstream processes — escalation chains, approval flows.
Dashboard updates
Live metrics, heatmaps, and detection feeds pushed to dashboards.
Describe what you want to detect.
Bring your cameras, feeds, or images. We configure the pipeline.