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TechnologyEnterprise AI RAG PlatformIn Production

Enterprise AI Knowledge Base & RAG Assistant

An internal AI knowledge assistant using Retrieval-Augmented Generation (RAG) to query thousands of private enterprise PDFs, policies, and contracts securely.

Project Parameters
  • ServiceAI Development
  • Client TypeCorporate Enterprise
  • Duration3 Months
Primary Tech Stack
PythonOpenAI APIspgvectorNode.jsReact

The Business Problem

Employees wasted hundreds of hours weekly searching through messy internal drive folders for policy and contract answers.

Technical Obstacles

  • 01.Document chunking and vector index creation without losing semantic context.
  • 02.Role-based document privacy.

Engineering & Architecture

How we designed, built, and secured the solution.

System Architecture

RAG pipeline using Python for document parsing, pgvector for semantic vector storage, and Node.js for API governance.

Planning & Design

Phase 1: Document taxonomy audit and permission role mapping.

Design: Clean conversational AI interface displaying exact source document citations alongside answers.

Development & Testing

Sprints: Iterative prompt engineering and vector retrieval parameter tuning over 3 months.

QA: Evaluation of semantic response accuracy against reference document answers.

Security & Scale

Sec: Private data ingestion pipeline ensuring enterprise text is never used to train public AI models.

Scale: pgvector HNSW index delivering sub-100ms semantic document retrieval over 500,000 document chunks.

The Delivered Solution

Reduced internal employee information search time by 80% with verified document citations.

Core Features Implemented

Retrieval-Augmented Generation (RAG)

Document Source File Citations

Role-Based Access Control

Automated Document Parsing

Lessons Learned

RAG systems must return exact page number citations to build user trust in AI responses.

Future Improvements

Adding multi-modal search support for technical architectural diagrams and scanned images.

Related Ecosystem

Project FAQs

Is our internal company data private?

Yes. Enterprise API usage explicitly guarantees prompt data is strictly confidential and not used for model training.

Build Your Architecture.

Every project begins with a rigorous technical discovery. Contact Morgan Dynamics to map out the exact architecture, timeline, and cost for your enterprise solution.

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Deep Dive

Explore technical architectures, cost breakdowns, and enterprise solutions related to this topic.

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