Pop
A Pop is the core building block of EyePop.ai.
A Pop defines how visual data is processed by combining AI models, logic, prompts, and workflows into a reusable pipeline. Every image, video, livestream, or camera feed analyzed by EyePop runs through a Pop.
Think of a Pop as a visual intelligence workflow that transforms raw media into structured, actionable data.
Why Pops Exist
Most real-world visual AI problems require more than a single model.
For example, reading a license plate may involve:
Detecting a vehicle
Locating the license plate
Cropping the plate
Running OCR
Returning structured results
A Pop allows these steps to be combined into a single reusable workflow.
What Can a Pop Contain?
A Pop can include:
Object detection models
Classification models
OCR models
Tracking
Segmentation
Keypoint detection
Vision Language Models (VLMs)
Structured extraction
Prompting logic
Data transformations
Multi-stage processing pipelines
Examples
License Plate Reading
Construction Site Monitoring
Retail Shelf Analytics
Visual Intelligence
Inputs
A Pop can process:
Images
Video files
Livestreams
RTSP camera feeds
RTMP streams
WebRTC streams
Outputs
A Pop returns structured JSON data that can be consumed by applications, dashboards, workflows, or business systems.
Outputs may include:
Bounding boxes
Labels
Counts
OCR results
Tracking IDs
Classifications
Structured fields
Event detections
Visual Intelligence responses
Reusability
Once created, a Pop can be used across multiple environments:
Dashboard applications
REST APIs
SDKs
Mobile applications
Livestreams
On-prem deployments
Permanent Sessions
This allows teams to build a workflow once and deploy it anywhere.
Deployment Options
A Pop can run in:
Cloud
Hosted and managed by EyePop.ai.
On-Prem
Deployed within a customerβs infrastructure.
Hybrid
A combination of cloud and edge processing.
Permanent Session
A dedicated runtime where a single Pop remains continuously active for low-latency and real-time workloads.
Best Practices
When designing a Pop:
Start with the simplest workflow that solves the problem.
Use detection models to narrow the scope before running more expensive analysis.
Return structured outputs whenever possible.
Test with representative production data.
Reuse Pops across applications rather than duplicating workflows.
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