DEV Community: Shaik Mohammed Huzaifa

KnowledgeForge: Turning Enterprise Chaos into a Living Knowledge Graph 🧠

Shaik Mohammed Huzaifa — Mon, 05 Jan 2026 07:52:37 +0000

This is a submission for the DEV's Worldwide Show and Tell Challenge Presented by Mux

Modern enterprises generate an overwhelming amount of unstructured information—emails, documents, meeting transcripts, dashboards, and chats. While this data holds immense value, most of it remains siloed, unsearchable, and disconnected.

KnowledgeForge is an AI-powered knowledge management platform designed to solve this exact problem. It transforms unstructured enterprise content into a queryable knowledge graph, enabling teams to explore their organizational knowledge using natural language.

Instead of searching through folders, inboxes, or dashboards, you can simply ask questions like:

“What did Rajesh Kumar discuss last quarter?”
“Which topics are most associated with declining KPIs?”

KnowledgeForge handles the rest.

The Core Idea

At its heart, KnowledgeForge combines three powerful concepts:

LLM-driven understanding of unstructured content
Graph-based modeling of people, topics, and metrics
Natural language access to both unstructured and structured data

By blending vector search, entity graphs, and virtualized data access, KnowledgeForge becomes a single intelligence layer over your organization’s knowledge.

Key Capabilities

Intelligent Entity Extraction

Every piece of content—whether it’s an email, document, or meeting transcript—is analyzed using large language models. The system automatically extracts:

People mentioned
Topics and themes
Business metrics and KPIs
Temporal references

This turns raw text into structured, reusable knowledge.

A Living Knowledge Graph

Extracted entities don’t live in isolation. KnowledgeForge builds relationships between:

Knowledge items (documents, meetings, emails)
People
Topics
Metrics
Time dimensions

Over time, this forms a rich graph that reflects how information, decisions, and outcomes are connected across the organization.

Semantic Search That Understands Meaning

Traditional keyword search fails when phrasing changes. KnowledgeForge uses vector embeddings to enable semantic search, meaning you can retrieve relevant information even if the wording doesn’t match exactly.

This is especially powerful for exploratory questions and research-style queries.

KB Genie: Conversational Knowledge Access

On top of the knowledge graph sits KB Genie, a conversational AI assistant.

KB Genie:

Detects entities in user questions
Traverses the knowledge graph
Retrieves relevant document chunks using vector search
Queries structured data when needed
Generates clear, contextual answers with supporting evidence

The result feels less like “search” and more like talking to your organization’s collective memory.

Bridging Unstructured and Structured Data with Denodo

One of KnowledgeForge’s differentiators is its integration with Denodo AI SDK.

While vector databases and LLMs excel at unstructured data, enterprises still rely heavily on structured systems like data warehouses. KnowledgeForge uses Denodo to:

Translate natural language questions into SQL
Query virtualized views across multiple data sources
Combine structured query results with unstructured knowledge

This creates a unified experience where documents, metrics, and dashboards all answer the same question.

How the System Works

Ingestion Pipeline

When new content is ingested, it flows through a multi-step pipeline:

Content is received (email, document, or transcript)
LLMs extract entities and relationships
Entities are resolved and deduplicated
Content is chunked into semantically meaningful sections
Embeddings are generated for each chunk
Data is stored across relational and vector databases

Each step adds structure and context without losing the original content.

Query Flow

When a user asks a question:

The system identifies entities in the query
The knowledge graph is traversed to find related information
Relevant document chunks are retrieved using semantic search
Structured data is queried via Denodo when needed
A language model synthesizes a final answer

This hybrid approach ensures responses are both accurate and contextually rich.

The Data Model

KnowledgeForge is built around a central concept: the knowledge item.

Every document, email, or meeting acts as an anchor node, connected to:

People
Topics
Metrics
Time

This design makes it easy to ask cross-cutting questions like:

“Which people are most associated with this topic?”
“What metrics tend to appear in discussions about delays?”

Built for Real-World Engineering Teams

The platform is designed with modern stacks and scalability in mind:

A FastAPI backend exposes ingestion, search, and agent endpoints
A React-based frontend provides dashboards, exploration tools, and chat
Vector storage enables fast semantic retrieval
Relational storage ensures consistency and traceability
Virtualized data access avoids duplicating enterprise data

Everything is modular, making it easy to extend or adapt to different enterprise environments.

Why KnowledgeForge Matters

Enterprises don’t suffer from a lack of data—they suffer from a lack of understanding.

KnowledgeForge turns scattered information into:

Connected knowledge
Searchable insights
Conversational answers

It doesn’t replace your existing systems. It connects them, learns from them, and makes them usable by anyone who can ask a question.

Github: https://github.com/Shaik-mohd-huzaifa/knowledge-forge

Permitron: AI-Powered Task Management with Fine-Grained Permissions

Shaik Mohammed Huzaifa — Mon, 05 May 2025 06:15:16 +0000

This is a submission for the Permit.io Authorization Challenge: AI Access Control

Permitron: AI-Powered Task Management with Fine-Grained Permissions

Github Repo: https://github.com/Shaik-mohd-huzaifa/permitron

Project Overview

Permitron is an intelligent task management application that combines the power of LLM-based chatbots with Permit.io's robust permission management system. The application enables users to create, update, track, and manage tasks using natural language through a conversational interface, while enforcing strict permission controls based on user roles and context.

Key Features

Natural Language Task Management
- Create tasks via conversational commands: "Create a task for adding new feature"
- Update tasks: "Update task #123 with title: Improved API integration"
- Change task status: "Move task #123 to in-progress"
- Delete tasks: "Delete task #123"
- List tasks with filtering: "Show me my in-progress tasks"
Kanban Board Visualization
- Visual task board with draggable cards
- Columns for Todo, In Progress, Review, and Done
- Status updates sync between chat and board interfaces
Fine-Grained Permissions with Permit.io
- Role-based access control (Admin, Employee, Guest)
- Attribute-based permissions (task ownership, assignment)
- LLM operation authorization
Chat Interface Integration
- Conversational AI powered by OpenAI
- Context-aware responses
- Permission-aware task operations

Technical Architecture

Frontend (React + TypeScript)

React-based SPA with TypeScript
Zustand for state management
Tailwind CSS for styling
Integrated chat interface and kanban board

Backend (Node.js + Express)

Express-based REST API
OpenAI integration for natural language processing
Permit.io SDK for authorization
In-memory data store (could be replaced with a database)

Permit.io Integration

Permitron leverages Permit.io's powerful authorization system to implement fine-grained access controls for LLM-based task operations. Here's how Permit.io is integrated:

1. Resource-Based Authorization

We've defined two key resources in our Permit.io configuration:

resources:
  - key: task
    name: Task
    description: A task in the system
    attributes:
      - key: status
        type: string
      - key: assignedTo
        type: string
      - key: createdBy
        type: string
    actions:
      - key: create
        name: Create
      - key: read
        name: Read
      - key: update
        name: Update
      - key: delete
        name: Delete
      - key: change_status
        name: Change Status

  - key: llm
    name: LLM
    description: LLM functions
    actions:
      - key: manage_tasks
        name: Manage Tasks

2. Role-Based Permissions

We've implemented different roles with varying levels of access:

roles:
  - key: admin
    name: Admin
    permissions:
      - resource: task
        actions: ["create", "read", "update", "delete", "change_status"]
      - resource: llm
        actions: ["manage_tasks"]

  - key: employee
    name: Employee
    permissions:
      - resource: task
        actions: ["create", "read"]
        condition: "{ assignedTo: user.id } OR { createdBy: user.id }"
      - resource: task
        actions: ["update", "delete", "change_status"]
        condition: "{ createdBy: user.id }"
      - resource: llm
        actions: ["manage_tasks"]

  - key: guest
    name: Guest
    permissions:
      - resource: task
        actions: ["read"]

3. LLM Assistant Integration

A unique aspect of our implementation is the integration of Permit.io with the LLM task processor. When a user asks the chatbot to perform a task operation, the system:

Identifies the user's intent (create/update/delete/etc.)
Extracts the task details from natural language
Checks permission with Permit.io before execution
Executes the task operation only if permitted
Provides clear feedback about permission status

Example code snippets showing this integration:

// Check permission for task creation
try {
  const permitted = permit ? 
    await permit.check({
      user: { id: userId, key: userId },
      action: "create",
      resource: "task"
    }) : true;

  if (!permitted) {
    return {
      actionTaken: true,
      action: 'create_task',
      success: false,
      response: "❌ You don't have permission to create tasks."
    };
  }
} catch (error) {
  console.error('Permit.io check error:', error);
}

4. Attribute-Based Access Control (ABAC)

For operations on existing tasks, we use attribute-based permissions to check if the user is the creator or is assigned to the task:

// For updating tasks
permitted = await permit.check({
  user: { id: userId, key: userId },
  action: "update",
  resource: "task",
  context: {
    task: {
      createdBy: task.createdBy,
      assignedTo: task.assignedTo
    }
  }
});

This enables contextual permission checks based on the task's properties and the user's relationship to it.

5. LLM-Specific Permissions

We've introduced a specific permission for using the LLM to manage tasks:

// Check if LLM can manage tasks on user's behalf
if (permit && !actionTaken) {
  const permitted = await permit.check({
    user: { id: userId, key: userId },
    action: "manage_tasks",
    resource: "llm"
  });

  if (!permitted) {
    return {
      actionTaken: true,
      success: false,
      response: "❌ You don't have permission to use the LLM for task management operations."
    };
  }
}

This adds an additional layer of control over which users can leverage AI for task management.

Benefits of Using Permit.io

Separation of Concerns: Authorization logic is cleanly separated from application code
Fine-Grained Control: Permissions based on roles, attributes, and context
Natural Language Understanding with Authorization: LLM understands not just what the user wants to do, but whether they're allowed to do it
User-Friendly Feedback: Clear permission-related responses from the chatbot
Scalable Permission Model: Easy to add new roles, resources, and permissions as the application grows

Future Enhancements

Implement user authentication with JWT integration
Add team-based permissions using Permit.io's tenancy model
Create an audit log of all permission decisions
Implement permission caching for performance optimization
Add permission-aware UI elements that hide/show based on user permissions

Conclusion

Permitron demonstrates how combining Permit.io's powerful authorization capabilities with AI-powered interfaces creates a secure, user-friendly task management system. By ensuring that the LLM understands and respects permission boundaries, we've created an application that leverages the power of AI while maintaining robust security controls.

Application Screenshots

Chatbot View

Board View

Dynamic Healthcare AI chat widget's with Kendo React Components

Shaik Mohammed Huzaifa — Mon, 24 Mar 2025 06:59:13 +0000

This is a submission for the KendoReact Free Components Challenge.

What I Built

I developed a comprehensive Healthcare AI Assistant that leverages KendoReact components to create an intuitive and efficient healthcare management system. The application features role-based interfaces for patients, doctors, and receptionists, providing specialized functionalities for each user type.

Key Features:

Role-based access control (Patient, Doctor, Receptionist)
Real-time appointment management
ICU patient monitoring with progress tracking
Surgery scheduling and progress tracking
Patient discharge management
Interactive data grids with filtering and sorting
Dynamic progress bars for time-sensitive operations
Responsive design for all devices

Demo

[Live Demo Link] - Add your deployed application link here
[GitHub Repository] - Github Repo

Screenshots:

Main Interface

Shows the chat interface with role selection
Displays the conversation history
Features the message input area

Doctor Dashboard ## Healthcare AI Assistant with KendoReact Components
Shows upcoming appointments
Displays ICU patient monitoring
Features surgery progress tracking
Receptionist Interface
Shows appointment management
Displays billing information
Features doctor availability

KendoReact Experience

I extensively utilized KendoReact's free components to build a professional and user-friendly interface:

Layout Components:
- Card, CardBody, CardTitle for organized content sections
- Responsive grid layouts for data presentation
Data Grid Components:
- Grid and GridColumn for displaying appointments, patients, and doctor lists
- Implemented sorting, filtering, and pagination
- Added custom cell renderers for progress bars
Input Components:
- TextBox for text input fields
- DatePicker for date selection
- DropDownList for role selection and status updates
Progress Components:
- ProgressBar for surgery and discharge progress tracking
- Custom styling for different states (success, info, error)
Dialog Components:
- Dialog and DialogActionsBar for clearing the Chat history
- Custom styling for better user experience ### AI to Impress The application integrates OpenAI's GPT model through LangChain to provide intelligent responses and dynamic widget rendering:
Smart Response System:
- Analyzes user queries to determine appropriate responses
- Dynamically renders relevant widgets based on context
- Maintains conversation history for context-aware responses
Role-Based Intelligence:
- Custom prompts for each user role (Patient, Doctor, Receptionist)
- Contextual understanding of healthcare terminology
- Intelligent widget selection based on user needs
Natural Language Processing:
- Understands complex healthcare queries
- Provides relevant information and actions
- Maintains professional healthcare communication

Delightfully Designed

The application features a modern, clean design using KendoReact's built-in theming and custom styling:

Visual Hierarchy:
- Clear section separation using cards
- Consistent color scheme for different states
- Intuitive progress indicators
Responsive Design:
- Adapts to different screen sizes
- Mobile-friendly interface
- Optimized grid layouts for smaller screens
Interactive Elements:
- Hover effects on cards and grid rows
- Smooth transitions for progress bars
- Clear visual feedback for user actions
Accessibility:
- High contrast text
- Clear visual hierarchy
- Keyboard navigation support

The application demonstrates the power of KendoReact's free components in creating a professional healthcare management system while maintaining excellent user experience and visual appeal.

Most Convenient way to create a new file

Shaik Mohammed Huzaifa — Mon, 07 Nov 2022 14:32:37 +0000

However, many ways exist to create a new file in Linux and Other CLIs.
But this one is the most convenient way with very minimal code

First, navigate the terminal to your desired location and then use this command

Here the Command

Username@deviceName ~ root directory
$ > filename.extension

Here, the Greater than '>' symbol is used to create new files

NOTE: You can create as many files as possible at once

Example:

Here I am creating files starting from No.1 to No.10 with a .js extention

Username@deviceName ~ root directory
$ > file1.js file2.js file3.js file4.js file5.js file6.js file7.js file8.js file9.js file10.js

The intention is to make the users work more convenient and faster