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Generative AI & AI Agents

DocuMind

Enterprise Document Intelligence

Enterprise AI projectGenerative AIRAGDocument Processing
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Architecture at a glance

Containerised deploymentIngestionQuestion answeringDocumentscollectionsExtraction& chunkingEmbeddingsmeaning vectorsVector indexsimilarity searchWeb appchat · dashboardAPI layerauthenticatedRetrievalgrounded answersLanguage modelresponse generationDatabaseapp data
Swipe to see the full diagram →Documents are chunked, embedded and indexed; questions flow through an authenticated API to retrieval over that index, and the language model answers from the passages it finds.

In brief

DocuMind is a Directrix Technologies project: Enterprise Document Intelligence. Summary: Make large document collections searchable and answerable in natural language using RAG. Outcome: The project demonstrates an approach to enterprise knowledge retrieval that can reduce manual document searching and make internal information more accessible.

Overview

Organizations often manage large volumes of documents containing important operational, legal, and business information. Finding relevant information manually can be time-consuming and difficult to scale.

DocuMind is an AI-powered document intelligence solution designed to make document collections searchable and easier to interact with using natural language.

What we built

  • Document ingestion and processing workflows.
  • Text extraction and document chunking.
  • Embedding generation for semantic retrieval.
  • Vector-based document search.
  • Retrieval-augmented generation (RAG) for question answering.
  • Conversational knowledge retrieval.
  • Authenticated APIs and request controls.
  • Dashboard capabilities for analytics and monitoring.

How we built it

We designed a backend API to support document processing and retrieval workflows. Documents were prepared for indexing, transformed into vector representations, and stored in a searchable vector index.

Similarity search over that index feeds a retrieval pipeline that connects document loading, text splitting, retrieval and language-model response generation.

A relational database holds structured application data, a web interface gives users the assistant and dashboards, and containerised deployment keeps environments consistent.

Services this project shows

Project outcome

The project demonstrates an approach to enterprise knowledge retrieval that can reduce manual document searching and make internal information more accessible.

Potential applications

  • Internal document search.
  • Legal and compliance knowledge retrieval.
  • Policy and procedure assistants.
  • Enterprise knowledge management.
  • Research document discovery.

Interactive sample · runs in your browser

Search documents with role-based access

Explore one idea from this project with fictional sample content. It is not connected to the project's systems or data.

Ask the sample document library

Each answer cites its source, only uses documents your role may read, and stays silent when no source exists.

View as

1 cited passage for Employee · 1 restricted

  • Multi-factor authentication

    Best match

    Source: Sample IT guide · Account access · Section 4

    If your multi-factor prompt does not arrive, check the registered device first. If that fails, contact the IT service desk so an administrator can reset the second factor.

One matching passage is limited to administrators, so its content is not shown to the employee role.

How this sample differs from the project: Local keyword matching over a few illustrative passages stands in for the embeddings, vector index and language model used in the project. It returns cited passages; it does not generate answers. It shows an interaction pattern, not a measured project result.

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