Most chatbot projects fail because the bot has nothing true to say. It is trained on a FAQ, users ask about their actual case, and the bot invents an answer or hands off — at which point it has added a step rather than removed one.
Ours are built on retrieval: the model answers from your documents, your rules and, where appropriate, the user's own record.
Where it is proven
Beena handles student queries on the Centralised Admission Portal during admission windows, reading sentiment as well as intent and sharply reducing manual intervention. Sahayak guides citizens through eNagarSeba in their own language, grounded in their municipality's actual rules. A RAG engine deployed for DVC reasons over a large document repository and answers domain-specific queries against it.
Our position
If a decision tree or a better form would answer the question more reliably, we will tell you. A model that reproduces a rulebook 94% of the time is worse than the rulebook.