kyc_aml · saas · workflow
Validia saves 500 hours of developer work and achieves scalable identity verification with Recall.ai
Validia needed a bot that could pull audio and video from major video conferencing platforms for real-time deepfake detection and identity authentication, but building platform-specific bots proved too complex and unscalable for their developer team.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Meeting audio/video ingested
Validia's platform pulls audio and video analysis from meetings on Zoom, Microsoft Teams, and Google Meet into their database.
Tools used
Recall.ai
Outcome
Validia saved an estimated 500 hours of developer work and now has scalable, cross-platform audio/video capture for real-time deepfake detection and identity authentication.
What failed first
Validia's initial attempts to build their own video conferencing bots were quickly abandoned when the complexity exceeded expectations and the solutions proved unlikely to scale for large enterprises.
Results
Time saved500 hours
Grounding & classification
Source type: vendor customer story
18 fields verified against source quotes.
anomaly detectioncomputer visionmeeting recordingfailure mode describedmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedsoftwareemployee productivitytime savedvendor customer storycompliance monitoringkyc amlmonitor detect alert