Building GenAI chatbots that actually resolve queries
Why RAG + human-in-the-loop beats raw LLMs for regulated support. We break down the architecture behind our Tier-1 resolution across 10+ languages.
Raw LLMs are not a support strategy
A general-purpose model will answer almost anything — including questions it should not answer, with confidence it has not earned. In regulated support, a fluent wrong answer about fees, KYC status or a policy clause is worse than no answer at all.
Ground every answer with retrieval
Retrieval-augmented generation (RAG) constrains the model to your own knowledge base: product docs, SOPs and historical resolutions. The bot retrieves the relevant passages first, then answers from them — and can cite where an answer came from.
Your data stays in an isolated pipeline and is never used to train shared models.
Design the handoff before the happy path
Human-in-the-loop is not a fallback; it is part of the design. Low-confidence answers, sensitive intents and frustrated customers are routed to a live agent with the full conversation attached, so nobody has to repeat themselves.
Speak the customer's language
Supporting 10+ languages means more than translation. Intent detection, retrieval and tone all have to work in Hindi, Gujarati, Tamil and English alike — and conversation analytics should show where each language is resolving well and where it is not.