NOCK
NOCK provides low-cost, privacy-first access to frontier AI language models for non-English speakers, starting with Hindi. The platform eliminates subscription fees and processes queries without retaining data, addressing the token-based pricing disparity that makes Hindi requests roughly three times more expensive than English. NOCK plans to expand to other high-overhead scripts one language at a time.
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
LLM APIs charge by the token, and most non-English scripts fragment into far more tokens than English. A Hindi speaker pays roughly 3x more than an English speaker for the same request, with some languages paying 6x or more. This invisible linguistic tax prices intelligence in someone else's language, limiting access for the 6.5 billion people who don't speak English.
Solution
NOCK provides low-cost, privacy-first access to frontier AI language models for non-English speakers, beginning with Hindi. The platform eliminates subscription fees and processes queries without retaining data, significantly lowering the cost of frontier AI for high-overhead scripts. By dismantling the linguistic AI premium, NOCK enables affordable AI usage where the cost disparity runs highest. The service is designed for pure utility pricing with no lock-in or friction, expanding one language at a time starting with the highest-overhead scripts.
Target Audience
Primary users are non-English speakers, beginning with Hindi speakers, who need affordable access to frontier AI language models without subscription fees or data retention concerns.
Features
- Token-efficiency optimization that reduces the cost disparity for Hindi and other high-overhead scripts
- No subscription model
- pure utility pricing with no lock-in
- Privacy by default: queries are processed and discarded, with no data retained beyond the session
- Expansion roadmap targeting highest-overhead scripts first, one language at a time
- Linguistic architecture grounded in computational linguistics and mathematical rigor for high-fidelity language processing