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CyberAkanksha
AI Solutions· 2025Production System

Nova: Enterprise Multimodal Document AI & RAG

Private retrieval-augmented generation engine transforming 50,000+ complex contracts into verified insights.

Engineered an enterprise-grade private RAG pipeline that ingests heterogeneous legal contracts, parses complex tabular data, and enables real-time citation-backed conversational queries with zero data leaks.

Client Profile
Enterprise Legal & Compliance Advisory
Engagement Type
Production System
Core Discipline
AI Solutions
Timeline
2025
System Architecture Case StudyID: nova-ai-document-intelligence
Services Delivered
Custom AI EngineeringVector Search InfrastructureFull-Stack Web App DevelopmentZero-Data-Retention Compliance
Context & Pain Points

The Challenge

The client's compliance attorneys spent over 18 hours weekly manually cross-referencing multi-jurisdiction supplier agreements. Standard off-the-shelf chatbots frequently hallucinated clause numbers and could not interpret nested tables or scanned addendums.

Strategic Targets

Core Objectives

Build a private document parser capable of extracting structured tables from scanned PDFs
Implement a hybrid vector + keyword (BM25) search pipeline to achieve >98% precision
Enforce mandatory source-document citation highlighting in the frontend UI
Guarantee absolute client confidentiality by hosting inside private isolated infrastructure
Execution Blueprint

Approach & Architecture

We developed a specialized OCR and semantic chunking pipeline that preserves document layout and hierarchy. Ingested vectors are stored in a self-hosted Qdrant cluster with metadata filtering. Queries run through a re-ranking model before reaching the LLM.

01

Hierarchical semantic chunking preserving section headers and table structure

02

Two-stage retrieval: dense embeddings + BM25 keyword matching re-ranked by Cohere model

03

Streaming SSE (Server-Sent Events) Next.js frontend with real-time PDF page jump-links

Defensive Security & Compliance Measures

Every engagement incorporates zero-trust engineering standards to ensure data sovereignty, cryptographic integrity, and compliance readiness.

Defense Factor 01
Dedicated single-tenant PostgreSQL database with AES-256 column encryption for document text
Defense Factor 02
Zero model training opt-outs on all external API requests with contractual enterprise SLAs
Defense Factor 03
Role-based document access boundaries preventing cross-department document leakage
Results & Verification

Verified Outcomes

85% Time Savings

Contract analysis time dropped from 3.5 hours per file to under 25 minutes

Zero Hallucination Rate

Enforced strict citation refusal when supporting clauses were absent

50,000+ Files Processed

Scalable asynchronous Celery workers handling bulk batch uploads

Technologies & Frameworks Utilized

Next.jsPython / FastAPIQdrant Vector DBClaude 3.5 SonnetPyMuPDFDockerTailwind CSS

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