S21 LASERi-X
About
The complete solution for rapid victim identification, CSAM categorisation and media review.
- Automate Victim Identification
- Automate Categorisation of CSAM Images & Videos
- School Badge Lookup
Full Features List
Import over 1 million images & videos per minute, ready to categorise
The faster you find a victim, the sooner you can safeguard them — that’s why speed matters. Import multiple exhibits (phones, computers etc.) into a single analysis. Save hours, or even days, with no requirement for pre-processing. Benefit from greater processing control by using flexible processing options per exhibit.
Beyond categorisation — built for speed and accuracy
Automatic CSAM image and video categorisation powered by AI
Smart workflows group and order images by relevance
Customisable grading with features like Autoscroll, Auto Refresh and Hotkeys
AI and intelligence databases that cut manual work and speed up review
Victim identification made faster and simpler - because Every Victim Matters
Automates Victim Identification before categorisation
Runs hundreds of automated seraches in second, delivering clear, real-time results
Supports investigations with file connections, favial analysis, offline mapping, POI and school badge lookup
Integrates with NCMEC Cybertips, Project Arachnid, Project VIC, UIK CAID, powered by S21 LASERi-X
- Supports investigations with file connections, facial analysis, offline mapping, POI, and school badge lookup
State-of-the-art image and video enhancement:
Enhance poor-quality, distorted or damaged image and video media
Reveal cues through recolouring, upscaling and deblurring
Extract frames and play corrupt video to uncover unseen content
Annotate, blur, watermark or crop media segments for disclosure
S21 Global Alliance Database (GAD) - The world's largest CSAM intelligence database
Over 3 BILLION records contributed by global law enforcement, including notes, descriptions and classifications - all designed to reduce manual review and minimise CSAM exposure for investigators