The Technology
Used by Big Sister is developed and tested in house and is patent pending.
DEtection Methods
Big Sister uses five complex detection methods to identify potential dangers. Our Danger Detection algorithm encodes child safety advice and is guided by our panel of experts. Explore the technology below.
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NLP uses code to analyse text, interpret tone, emotion, and context, including:
Detecting sarcasm
Identifying bullying language, even when unrecognized by victims
Flagging violent rhetoric as potential radicalisation indicators
Recognizing "love bombing" as a possible grooming tactic
This technology enables deeper understanding of communication, aiding in early detection of concerning behaviors.
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AI in Big Sister focuses on child protection through:
Standalone code for image analysis, detecting:
Problematic objects (guns, drugs, radicalisation symbols)
Nudity
Text extraction from images
Automated processing to avoid human exposure to sensitive content.
High-speed analysis of large image volumes
Human oversight:
Monthly audits
Algorithm training and testing
This approach balances privacy concerns with child safety, ensuring sensitive data remains protected while effectively identifying potential threats.
Privacy Safeguards:
No external data sharing
Limited human interaction with sensitive content
Regular audits to maintain system integrity
By using AI for initial screening, Big Sister minimizes privacy risks associated with human review of sensitive materials while maximizing child protection efforts.
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Some apps are never appropriate for children (18+, gambling, porn, dark web browsers) and some sites are not either. There are also typical words and phrases that are used when someone is radicalised. Emojis with the conjunction of NLP can give a full picture of the meaning of the word in messages and content your child is exposed to.
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Grooming detection is crucial as it often precedes four major danger categories:
Extreme content
Exploitation
Child sexual abuse
Illegal activities
Key points:
Encoded grooming patterns enable early detection
AI identifies cumulative patterns indicating risks like:
Suicide
Eating disorders
This approach allows for proactive intervention in potentially dangerous situations involving children.
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A sudden change in behaviour or a sudden new contact non normal behaviour is a very early indicator of a safeguarding issue. Big Sister gathers “normal” behaviour for a while and then can trigger for non normal and sudden changes.

