Asansol, West Bengal

AI Integration & AI Product Development in Asansol.

RAG pipelines and autonomous LLM workflows built into your product. Mojo Studio delivers ai products for founders and businesses in Asansol, senior engineers only, weekly demos, full IP ownership.

How we build it

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.

Custom RAG pipelines over your own data
LangChain / LangGraph agent workflows
Semantic search & vector database integration
Guardrails against hallucination in production
Local context

Built for Asansol's market.

Asansol anchors the eastern end of the Raniganj-Asansol coal belt, one of India's oldest coalfields, and sits on the Eastern Railway's Howrah-Delhi main line, giving it a historic role as an industrial and transport junction rather than a commercial or startup center. IISCO's Burnpur steel works (now under SAIL) and a cluster of smaller coal-linked and railway-ancillary manufacturers make up the core economy, alongside a growing trading and wholesale base serving the wider Bengal-Jharkhand border region. Digital adoption among these legacy industrial and coal-linked businesses is still early, with most operations run on paper ledgers and phone-based coordination.

Asansol's coal mining (raniganj coalfield) and steel (iisco burnpur/sail) businesses have specific needs when it comes to ai products — this is the local context that shapes how we scope every Asansol engagement, not a generic playbook applied everywhere.

Coal mining (Raniganj coalfield)Steel (IISCO Burnpur/SAIL)Railway junction & logisticsWholesale trade
Our Process

From brief to launch.

01

Use-case scoping — where AI actually adds value vs. hype

02

Data pipeline & vector store architecture

03

Model integration (OpenAI, Gemini, Claude, or fine-tuned)

04

Evaluation, guardrails, and production monitoring

Common Questions

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.

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