Mohit Rai — Founder

Most AI answers are wrong for one simple reason: they’re generated from static model memory in a live world. I’m building Wizang AI — real-time AI data & reasoning infrastructure that lets AI apps answer questions using live internet data, not stale training cutoffs. Wizang isn’t a chatbot. It’s the layer underneath AI apps that need fresh, verifiable answers — weather, finance, news, markets, anything that changes. What we’ve built so far: • A production-ready real-time enrichment engine • Domain-based routing (weather, finance, general) • Live data fetch → rank → summarize pipeline • Semantic + hash-based caching for speed • Public API + JavaScript SDK • Benchmarked against Perplexity & Gemini for freshness and speed I share here: • What it actually takes to build real-time AI infra • Lessons from shipping early, not theorizing • Where LLMs break — and how live data fixes it • The difference between AI demos and AI systems If you’re interested in: • AI search • Live data infrastructure • Building foundational AI products Follow along. 👉 Building an AI app that needs real-time answers? DM me.

Stackforce AI infers this person is a SaaS entrepreneur with a focus on AI infrastructure and real-time data solutions.

Location: Ghazipur, Uttar Pradesh, India

Experience: 1 yr 2 mos