AI-Powered Assistant for Intelligent Document Processing
For organizations operating in regulated sectors like finance, pharmaceuticals, and legal, accessing reliable answers from internal documents is a constant challenge. Public AI tools can’t be trusted with sensitive data, and basic keyword searches don’t provide context or traceability.
Our client needed a compliant, secure, and source-grounded solution for internal document-based question answering.
Solution
Unidatalab delivered a secure, self-hosted AI assistant that only answers questions based on uploaded internal documents. It combines Retrieval-Augmented Generation with a lightweight web interface and secure Docker deployment, offering precise answers with full source traceability and compliance logging.
How it works
Uploaded documents (PDF, DOCX, HTML) are parsed, chunked, and stored with metadata in a vector database for traceable indexing.
The system performs semantic search and uses an LLM to generate grounded answers, each linked to its original document source.
A web interface allows users to ask questions, view answers, and trace citations, with admin-only upload controls.
The entire solution is packaged as a Dockerized microservice, ready for secure deployment in private or on-premise infrastructure.
Our challenges:
General-purpose AI tools risk data leakage and hallucinations
Standard LLMs often fabricate answers or fail to cite sources, unacceptable in compliance-driven environments.
Lack of version control and access restrictions
Many internal tools don’t offer audit logs, role-based access, or document metadata tracking, making compliance reviews difficult.
Inability to self-host on private infrastructure
Cloud-only or SaaS solutions pose risks for clients with strict data protection requirements.
Project stages
We build the core functionality including document indexing, semantic search, and the RAG-based answer engine, focused on up to 30–40 English-language documents.
A lightweight browser-based interface is developed for question submission, answer display with source citations, and role-based document management.
We deliver full technical documentation, usage instructions, and a roadmap outlining next steps for scaling, feature expansion, or further integration.
Summary
