AI Integration & AI Product Development in Patna.
RAG pipelines and autonomous LLM workflows built into your product. Mojo Studio delivers ai products for founders and businesses in Patna, senior engineers only, weekly demos, full IP ownership.
Built right,
shipped fast.
We integrate production-grade AI into real products — custom RAG pipelines over your own data, autonomous LLM agent workflows, and semantic search, built with the same engineering rigor as the rest of your stack. This isn't a chatbot bolted onto a landing page; it's AI wired into your actual product logic, with the guardrails to keep it from hallucinating in front of customers.
Built for Patna's market.
Patna is Bihar's administrative and commercial center, anchored by IIT Patna, Patna University (one of India's oldest, founded 1917), and a large government and public-sector employment base that shapes much of the local services economy. Bihar has historically exported its educated workforce to Delhi, Bangalore, and Mumbai rather than retaining them, but a visible wave of returnee entrepreneurs and a fast-growing retail, real estate, and coaching/ed-tech sector (Patna is one of India's largest hubs for competitive-exam coaching institutes) are now driving fresh local demand for apps and websites. Most of that demand is still served by out-of-state vendors, since Patna's own digital-agency ecosystem remains thin relative to its population and economic size.
Patna's government & public administration and coaching/ed-tech institutes businesses have specific needs when it comes to ai products — this is the local context that shapes how we scope every Patna engagement, not a generic playbook applied everywhere.
From brief to launch.
Use-case scoping — where AI actually adds value vs. hype
Data pipeline & vector store architecture
Model integration (OpenAI, Gemini, Claude, or fine-tuned)
Evaluation, guardrails, and production monitoring
AI Products FAQ.
Grounded retrieval (RAG) over your actual data, tight prompt scoping, output validation layers, and explicit fallback behavior when the model isn't confident — hallucination is a design problem, not something you patch after launch.