India has achieved remarkable banking access. 559.8 million Jan-Dhan accounts, 80% penetration, yet only 27% of Indian adults are truly financially literate. The gap between having a bank account and understanding how to use it safely is vast, and it costs real people real money. Rural farmers miss out on ₹3+ lakh crore in government scheme benefits annually because they don't know they qualify. Daily wage labourers fall victim to fake loan apps, UPI reversal scams, and lottery fraud because no one has ever explained the warning signs in their language. The problem isn't infrastructure anymore, it's that every financial touchpoint, from RBI advisories to insurance claim guides, was designed for English-literate urban users. A farmer in Maharashtra asking about PM-KISAN in Marathi, a fisher in Tamil Nadu filing an insurance claim in Tamil, a shopkeeper in Gujarat confused about UPI reversals in Gujarati, they have nowhere to turn.
Sahayak AI: A Financial Guide in Your Language
Sahayak AI is a production-grade, multilingual financial inclusion assistant built to close this gap. At its core is an 11-agent LangGraph pipeline : language detection, user context, intent supervision, query decomposition, live web search, RAG retrieval, evidence synthesis, next-step recommendation, fraud safety filtering, response formatting, and feedback logging : all firing in sequence and streamed live to the user. When a query arrives, Sahayak simultaneously retrieves expert answers from a 4,811-record knowledge base using BGE-M3 hybrid vector search (dense + sparse → Qdrant RRF fusion), checks for active fraud patterns, identifies eligible government schemes, and surfaces required documents , then delivers a personalised response calibrated to the user's literacy level, in their own script. Every user gets a persistent knowledge graph across 5 financial domains that evolves with each conversation, making Sahayak progressively more useful the more you talk to it.
How Adaption Built the Brain Behind It
The knowledge base that powers Sahayak's RAG pipeline was built using Adaption's multilingual dataset processing platform. Starting from raw financial Q&A data, Adaption's pipelines localised, translated, and adapted records across 5 core domains, Banking & Digital Payments (1,498 records), Fraud & Cyber Safety (1,463), Savings & Insurance (1,214), Credit & Borrowing (545), and Government Schemes, producing a final corpus of 4,811 expert-curated records spanning 7 Indian scripts: Devanagari (Hindi and Marathi, 2,225 records), Tamil (322), Gujarati (320), Kannada (319), Telugu (318), Malayalam (318), and English (980). Each record was not just translated but culturally localised , a UPI fraud warning reads differently for a rural Bihar farmer than an urban Bangalore professional, and Adaption's adaptation recipes captured that nuance across subdomains like PMFBY crop insurance, PMJJBY life cover, UPI fraud prevention, KYC compliance, and fake government scheme scams. This dataset, encoded into Qdrant using BGE-M3's native multilingual embeddings, is what gives Sahayak's reasoning agent its depth, when a user asks something, the system retrieves genuinely similar expert cases in their language, not generic English translations. And the loop never closes: every Sahayak conversation is anonymised, Gemini-formatted, and fed back into the pipeline, so the dataset grows with every user interaction.
