Internal AI assistant: reliable, sourced answers over company documents
IASWITCH delivered a secure internal AI assistant (RAG pipeline) that answers in seconds from company documents, cites its sources and has its quality continuously evaluated. Data stays protected and every answer is traceable.
In brief
- Before: scattered information, experts constantly interrupted
- After: an assistant that answers in seconds and cites its sources
- Result: reliable, traceable answers that meet security requirements
- Stack: LangChain, pgvector, RAG
The challenge
The client wanted an assistant able to answer over internal documentation, without data leakage or made-up answers, 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.
What it means for an SME
Procedures, contracts, technical sheets, customer FAQs: every SME has an under-used document base. An assistant like this frees internal experts from repetitive questions and speeds up onboarding of new staff.
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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