A generic chatbot guesses. A retrieval-augmented (RAG) assistant looks up the answer in your own content first: help articles, policies, product data, contracts, past tickets. Then it replies with a link to the source so the user can check it.
We build these assistants for customer support, sales enquiries and internal knowledge, and we deploy them where your users already are: your website, WhatsApp, Microsoft Teams, Slack, or a panel inside your own application.
Where can the chatbot be deployed?
Anywhere a conversation happens. We ship a lightweight website widget that does not slow your pages down, WhatsApp Business integrations for markets where customers prefer chat, Microsoft Teams and Slack bots for internal use, and embedded assistants inside your SaaS product or admin panel.
What content can it learn from?
PDFs, Word and Excel files, web pages, Confluence and SharePoint, help-desk articles, product catalogues and database records. We build an ingestion pipeline that re-indexes on a schedule or on change, so answers stay current without anyone retraining a model.
- Chunking tuned to your document types (tables, FAQs, long contracts)
- Hybrid keyword + vector search with re-ranking
- Multilingual answers, including Hindi and Gujarati
- Metadata filters for product, region, plan or department
How is this different from a scripted chatbot?
Scripted bots follow decision trees and break the moment a customer phrases something unexpectedly. A RAG assistant understands free-text questions and composes an answer from your content. We still use structured flows where they help, for example collecting order numbers or booking a slot, and hand those to your existing APIs.