Build a RAG AI chatbot for your documents or knowledge base

I will build a Retrieval-Augmented Generation (RAG) chatbot that allows users to ask questions about your own documents or knowledge base.

Instead of relying only on the model's general knowledge, the system retrieves relevant information from your provided data and uses that context to generate responses.

The base offer can include:

• Document ingestion
• Text extraction and chunking
• Embedding generation
• Vector search
• Semantic retrieval
• LLM response generation
• Basic conversational interface
• API integration
• Source-aware responses where applicable
• Basic testing

This is suitable for:

• PDF and document knowledge bases
• Educational content
• Product documentation
• Internal company information
• Support knowledge bases
• Research material
• Other structured or unstructured text sources

I will build the RAG workflow around your specific use case and existing application where applicable.

Important: The £180 package covers a clearly defined RAG implementation. Very large document collections, multiple complex integrations, advanced authentication, extensive UI development, and large production infrastructure requirements are not included in the base package and can be handled separately.

AI models and AI-assisted development are used as part of this service. The implementation is reviewed and tested against the supplied data before delivery.