AI Integration & AI Product Development in Siliguri.
RAG pipelines and autonomous LLM workflows built into your product. Mojo Studio delivers ai products for founders and businesses in Siliguri, 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 Siliguri's market.
Siliguri's entire economic identity is built around geography — it sits in the narrow 'Chicken's Neck' corridor that connects mainland India to the Northeast, and to Nepal, Bhutan, and Bangladesh, making it the logistics and wholesale trading hub for the entire region. Tea (from the Darjeeling and Dooars gardens), timber, and tourism to the Himalayan hill stations drive a large base of trading firms, transporters, and hospitality businesses that are only beginning to move booking, inventory, and dispatch operations online. University of North Bengal anchors a modest local tech talent pool, but most digital work in the city is still commissioned out to Kolkata or Guwahati agencies, leaving a clear opening for locally-responsive studio work.
Siliguri's tea trade (darjeeling/dooars) and cross-border logistics & wholesale trade businesses have specific needs when it comes to ai products — this is the local context that shapes how we scope every Siliguri 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.