AI safety for every community, in every context.
Trust at the edges, because that’s where harm begins.
AI works brilliantly on average. But when meaning shifts, systems fail, and vulnerable communities pay the price. We build contextual safety infrastructure that helps AI understand not just what people say, but what they mean.
What We Build
Verified Context Layers — adaptive safety infrastructure that goes beyond sentiment analysis and keyword lists to capture:
- Cultural nuance and code-switching that automation misses
- Risk-stratified scenarios showing how meaning shifts across contexts
- Privacy-preserving synthetic data that scales without exposing individuals
The result: AI that interprets meaning as well as language, and knows when the difference matters.
Privacy by Design
All insights are transformed into synthetic, anonymized scenarios. We capture patterns, not personal data, ensuring safety without surveillance.
Where We Start
Youth digital language is the fastest-moving, hardest-to-parse test case. If AI can reliably interpret slang across Discord, TikTok, and Roblox, the same infrastructure can scale to healthcare, biosecurity, and any setting where misunderstanding creates real harm.
Our Methodology
The real problem is not just words, it is context collapse at scale. Crowdsourced glossaries record definitions. Automated systems flag keywords. Neither captures how meaning shifts with audience, culture, or stakes.
Our three-layer approach:
- Ethnographic immersion in fast-moving communities to observe meaning in use
- Risk-stratified validation mapping how the same phrase signals low, medium, or high risk across scenarios
- Synthetic augmentation generating privacy-preserving data that retains cultural patterns at scale
This produces what static datasets cannot: contextual ground truth that evolves with language. Safety becomes a continuous capability, not a brittle filter.
Our approach relies on immersion automation cannot replicate and cultural fluency that crowdsourcing consistently misses. It is scalable because validated patterns seed synthetic generation. It is necessary because every major AI lab faces the same blind spot: systems work brilliantly on average, but break where context determines safety.
Our Mission
Our mission is to make AI trustworthy at the edges, where small context failures can cascade into real-world harm.
Contact Us
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