Enterprise RAG assistant: reliable, traceable answers over internal data
IASWITCH delivered a secure RAG pipeline over internal data, with continuous evaluation, guardrails and full source traceability for every answer.
In brief
- Context: need for a reliable assistant over internal data
- Solution: secure, continuously evaluated RAG pipeline
- Result: traceable answers with sources
- Stack: LangChain, pgvector, RAG
The challenge
The client wanted an assistant able to answer over internal documentation, without data leakage or uncontrolled hallucination, with source traceability required for compliance.
The solution
We built a RAG pipeline with LangChain and pgvector: secure document indexing, contextual retrieval, output guardrails and systematic source citation. A business evaluation set measures quality continuously.
The results
A reliable assistant whose every answer is traceable to its sources, continuously evaluated, and compliant with data security requirements.
Frequently asked questions
What is a RAG pipeline?
RAG (Retrieval-Augmented Generation) combines retrieval of relevant documents with LLM generation, producing answers grounded in your data and citing their sources.
How do you prevent AI assistant hallucinations?
With a well-designed RAG pipeline, output guardrails, source citation and continuous evaluation against a business test set, as in this engagement.
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