RAG Chatbot for Your Documents
Custom RAG chatbot development: answers from your own documents using embeddings and vector search, on Groq, OpenRouter or local Ollama LLMs.
A retrieval-augmented chatbot that answers questions from your own documents, including long ones, on cloud or fully local LLMs.
The challenge
A generic chatbot knows nothing about your documents, and long documents are too big to paste into a prompt, so people keep searching by hand.
Our solution
We build retrieval-augmented generation (RAG) chatbots that index your documents, find the passages relevant to each question using embeddings and vector search, and have an LLM answer from them. Long documents are handled through retrieval, and the model can run in the cloud or entirely on local hardware.
What it delivers
- Answers drawn from your own documents
- Long documents become searchable by asking a question
- The option to keep everything on your own hardware
Key features
- Answers grounded in your own documents
- Embeddings and vector search for retrieval
- Long-document handling through retrieval
- Cloud models (Groq, OpenRouter) or local models (Ollama)
- Built around each client’s own content
Where it fits
- Internal knowledge bases and policy documents
- Customer support and FAQ automation
- Legal, finance and compliance document search
- Training manuals and product documentation
Technologies
Python, RAG, Embeddings, Vector Search, Groq, Ollama
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