AI PDF Assistant
VeriVox LedgerDocs.
An intelligent offline RAG (Retrieval-Augmented Generation) document search engine designed to inspect legal and technical PDFs securely.
The Problem
Auditors and document teams must analyze and compare huge collections of legal papers. Running simple search queries fails to extract context, while querying public AI APIs leaks confidential metadata and file logs to corporate nodes.
The Solution
VeriVox LedgerDocs establishes a local RAG pipeline. It handles document text parsing, generates text embeddings, indexes metadata inside a local FAISS vector store, and prompts offline language models, returning markdown context via a Next.js streaming client.
System Architecture
PDF parsing & chunk extraction
Semantic FAISS Vector Indexing
Local LLM Context Injection
This multi-stage pipeline ensures legal data never traverses the internet, fulfilling privacy constraints.
Key Deliverables
Semantic Retrieval
Allows users to ask conversational legal questions instead of relying on exact keyword searches.
FAISS Vector Store
Integrates FAISS vector indices, achieving local document lookup times of under 2 seconds.
Streaming Typewriter UI
Renders server response tokens chunk-by-chunk using a fast server-sent events stream client.
Local Privacy Bounds
Ensures legal documents and user logs remain completely local, preventing leak vulnerabilities.