AI Integration & AI Product Development in Kolkata.
RAG pipelines and autonomous LLM workflows built into your product. Mojo Studio delivers ai products for founders and businesses in Kolkata, 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 Kolkata's market.
Kolkata is home to India's oldest stock exchange, Presidency University, IIM Calcutta, and Jadavpur University, giving it a deep bench of analytical and engineering talent that has historically flowed into Bangalore and Hyderabad rather than staying local — a gap that makes strong in-city product studios genuinely scarce relative to demand. The city's economy still runs on legacy trading houses in jute, tea, and steel, alongside a large pharmaceutical and FMCG distribution base (companies like ITC and Emami are headquartered here), most of which are now under pressure to digitize decades-old paper-based operations. A newer wave of D2C, fintech, and ed-tech founders is emerging around Salt Lake's Sector V IT hub, but they compete for the same limited pool of experienced product-engineering vendors as the legacy trading houses.
Kolkata's finance & stock trading and jute, tea & steel trade businesses have specific needs when it comes to ai products — this is the local context that shapes how we scope every Kolkata 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.