AI-Powered Chatbots with RAG: A Healthcare Use Case
Generic chatbots are limited. By using Retrieval-Augmented Generation (RAG), we created a chatbot that truly understands a company's knowledge base.
Healthcare Chatbot (2024)
We fine-tuned an LLM on healthcare data and built a vector database (Pinecone) for semantic search. The chatbot now handles 70% of support queries, reducing response time from 24 hours to instant.
Our AI solutions can be applied to any domain—customer support, internal knowledge bases, and more.
Frequently Asked Questions
How does a RAG chatbot improve customer support?+
By using Retrieval-Augmented Generation with a vector database like Pinecone, the chatbot grounds answers in proprietary data—handling 70% of queries and reducing response time from 24 hours to instant.
Can RAG chatbots work in regulated industries like healthcare?+
Yes. We fine-tune LLMs on domain-specific data and use vector databases for semantic search, delivering accurate, grounded answers while respecting domain constraints.
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