Strip PII from your visual pipeline - at scale.
Automatically remove faces, license plates, and biometric identifiers from high-volume image and video streams. Keep the signal your product runs on; drop the personal data you shouldn’t be holding.
Data-pipeline diagram - frames flow through a “PII redaction” node and come out with faces/plates removed, scene and objects intact.
Three steps. Nothing stored.
Send media
Push a file or URL via REST API - or trigger at the moment your users upload.
Scanon detects PII
Faces, plates, IDs, badges, and tattoos identified across images and video.
Redacted media returned
You get the clean asset back. Nothing is stored on our side. Zero retention.
Built for this vertical, not repurposed for it.
Built for throughput
Redaction across high-volume streams, not one file at a time.
Signal in, PII out
Remove faces, plates, identifiers while leaving scenes and objects intact.
Cleaner training data
Strip PII up front so datasets and outputs aren’t built on personal data.
Zero-retention architecture.
Zero-retention - frames processed and returned, never stored.
Coverage: faces, license plates, ID documents, badges, and tattoos - across images and video.
Helps you meet GDPR, CCPA, and BIPA, and lets you tell enterprise customers PII is removed before it hits your models.
No ML infra to build. One API call.
Stream frames or batches through a single endpoint; async video with webhook delivery for long jobs. Runs in your ingest pipeline with no ML infrastructure of your own.
- • REST API + SDKs (Node, Python)
- • Images and video from a single endpoint
- • Redact at point of upload
POST https://api.scanon.ai/v1/stream/redact
Authorization: Bearer sk_live_[YOUR_KEY]
{
"stream_id": "ingest_[PIPELINE_ID]",
"targets": ["faces", "plates", "biometric_ids"],
"webhook": "https://your-app.example.com/scanon/callback"
}