Product Whitepaper

Advanced Knowledge Processing and Intelligent Deep Research Platform

Version 1.0 | March 2025

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Executive Summary

In today's information-rich environment, organizations face unprecedented challenges in extracting meaningful insights from complex documents. Qwello addresses this challenge by providing a sophisticated knowledge processing platform that transforms documents into interactive, queryable knowledge graphs. By combining advanced PDF processing capabilities with state-of-the-art AI models, Qwello enables users to unlock the knowledge trapped in their documents, making it accessible, searchable, and actionable.

Qwello's unique approach leverages multiple AI models, including Grok and Claude, to process documents through a sophisticated pipeline that extracts text, identifies entities and relationships, and generates comprehensive knowledge graphs. These graphs can then be visualized, queried, and analyzed to reveal insights that would otherwise remain hidden in lengthy documents.

The platform offers two primary methods for knowledge acquisition: direct document upload and web search integration. Users can either upload their own documents or leverage Qwello's search capabilities to find and analyze relevant documents from across the web, providing unprecedented flexibility in knowledge discovery.

This whitepaper outlines Qwello's capabilities, architecture, and implementation, demonstrating how it can transform document analysis and knowledge discovery for research, business intelligence, and decision-making processes.

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Table of Contents

1. Introduction

2. Key Features and Capabilities

3. System Architecture

4. PDF Processing Pipeline

5. Knowledge Graph Generation

6. AI Model Integration

7. User Interface and Visualization

8. Use Cases

9. Implementation and Deployment

10. Future Roadmap

11. Conclusion

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Introduction

The Document Analysis Challenge

Organizations across industries are drowning in documents. Research papers, technical documentation, legal contracts, financial reports, and other complex documents contain valuable information that is often difficult to extract, connect, and utilize effectively. Traditional document processing approaches suffer from several limitations:

• Information Silos: Knowledge remains trapped within individual documents, making it difficult to see connections across multiple sources.

• Limited Search Capabilities: Keyword searches fail to capture semantic relationships and contextual understanding.

• Manual Processing Overhead: Extracting insights from complex documents requires significant human effort and expertise.

• Inconsistent Analysis: Manual document review leads to subjective interpretations and missed connections.

• Scaling Limitations: As document volumes grow, manual analysis becomes increasingly impractical.

• Knowledge Discovery Challenges: Finding relevant documents across the vast expanse of the internet is time-consuming and often yields incomplete results.

The Qwello Solution

Qwello transforms how organizations interact with their documents by providing a comprehensive platform for document analysis, knowledge extraction, and insight generation. At its core, Qwello converts documents into knowledge graphs—structured representations of entities and relationships that can be visualized, queried, and analyzed.

Key differentiators of the Qwello platform include:

• Advanced PDF Processing: Sophisticated pipeline for converting PDF documents into structured knowledge.

• Multi-Model AI Approach: Integration of multiple specialized AI models for optimal results at each processing stage.

• Interactive Knowledge Graphs: Visual representation of document knowledge with powerful query capabilities.

• Scalable Architecture: Parallel processing capabilities to handle documents of any size and complexity.

• Comprehensive Reports: AI-generated analytical reports that summarize key insights from knowledge graphs.

• Dual Knowledge Acquisition: Both direct document upload and web search integration for maximum flexibility.

By transforming static documents into dynamic knowledge graphs, Qwello enables organizations to:

• Discover hidden connections across document collections

• Answer complex questions about document content

• Generate insights that would otherwise remain obscured

• Make more informed decisions based on comprehensive document analysis

• Efficiently find and analyze relevant documents from across the web

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Key Features and Capabilities

PDF Processing and Analysis

Qwello's PDF processing capabilities form the foundation of its document analysis system:

• Robust PDF Handling: Process PDFs of various sizes, layouts, and complexities.

• Image-Based Processing: Convert PDF pages to optimized images for AI analysis.

• Text Extraction: Extract structured text while preserving document layout and formatting.

• Table Recognition: Identify and preserve tabular data structures.

• Mathematical Formula Support: Recognize and extract mathematical expressions.

• Multi-Language Support: Process documents in multiple languages.

Knowledge Graph Generation

Qwello transforms document content into comprehensive knowledge graphs:

• Entity Extraction: Identify key concepts, people, organizations, locations, and other entities.

• Relationship Identification: Determine connections between entities based on document context.

• Entity Resolution: Recognize when different mentions refer to the same entity across pages.

• Attribute Assignment: Extract and assign relevant attributes to entities and relationships.

• Cross-Document Connections: Identify relationships between entities across multiple documents.

• Metadata Integration: Incorporate document metadata into the knowledge graph.

Interactive Visualization

Qwello provides powerful visualization tools for exploring knowledge graphs:

• Force-Directed Graph Layout: Intuitive visualization of entities and relationships.

• Interactive Navigation: Zoom, pan, and explore the knowledge graph.

• Entity Filtering: Filter by entity type, relationship type, or other attributes.

• Search Capabilities: Find specific entities or relationships within the graph.

• Detail Views: Examine detailed information about selected entities and relationships.

• Customizable Display: Adjust visualization parameters for optimal viewing.

AI-Powered Analysis

Qwello leverages advanced AI models for sophisticated document analysis:

• Multi-Model Integration: Specialized models for different processing stages.

• Vision AI: Extract text and structure from document images.

• Language AI: Generate knowledge graphs from extracted text.

• Large Context Processing: Analyze extensive knowledge graphs for insights.

• Fallback Mechanisms: Ensure processing reliability with model redundancy.

• Continuous Improvement: Regular model updates to enhance capabilities.

Web Search Integration

Qwello's search capabilities enable users to find and analyze relevant documents from across the web:

• Query-Based Search: Enter natural language queries to find relevant documents.

• SERP API Integration: Leverage Google search results through SERP API.

• Automatic Document Collection: Identify and collect relevant PDFs from search results.

• Format Support: Process various document formats including PDF, DOC, DOCX, RTF, TEX, and TXT.

• Seamless Processing: Apply the same sophisticated processing pipeline to search results.

• Integrated Results: Combine search findings with knowledge graph insights.

Report Generation

Qwello generates comprehensive reports based on knowledge graph analysis:

• Executive Summaries: High-level overviews of key document insights.

• Entity Analysis: Detailed examination of important entities and concepts.

• Relationship Mapping: Analysis of significant connections between entities.

• Topic Clustering: Identification of related concept groups and themes.

• Statistical Overview: Quantitative analysis of knowledge graph structure.

• Recommendations: Suggested areas for further exploration or clarification.

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System Architecture

Qwello implements a modern, scalable architecture designed for performance, reliability, and extensibility. The system comprises several key components that work together to deliver a seamless document processing and knowledge discovery experience.

Overall System Architecture

Core Components

Frontend Application

The Qwello frontend is built with React and TypeScript, providing a responsive and intuitive user interface:

• React Framework: Component-based architecture for efficient UI development

• TypeScript Integration: Enhanced type safety and developer experience

• Responsive Design: Optimized for various screen sizes and devices

• Interactive Visualization: Powered by vis-network.js for knowledge graph exploration

• Real-Time Updates: WebSocket integration for processing status updates

• Thread Interface: Intuitive interface for creating new search or upload threads

Backend Server

Qwello's backend is built on NestJS, a progressive Node.js framework:

• Modular Architecture: Organized into functional modules for maintainability

• Dependency Injection: Clean, testable code with clear separation of concerns

• TypeScript Support: Strong typing for enhanced code quality and reliability

• RESTful API: Well-defined endpoints for frontend communication

• WebSocket Support: Real-time communication for progress updates

• SERP Integration: Connectivity with search engine results via SERP API

AI Integration

Qwello integrates with multiple AI models through Cloudflare's AI platform:

• Model Selection: Appropriate models for different processing stages

• Fallback Mechanisms: Automatic switching to alternative models when needed

• Rate Limiting: Intelligent handling of API rate limits

• Error Recovery: Robust error handling with retry mechanisms

• Context Management: Efficient handling of context for large documents

Search Integration

Qwello integrates with SERP API to provide powerful web search capabilities:

• Query Processing: Convert user queries into effective search parameters

• Result Filtering: Identify and select relevant document results

• Document Collection: Retrieve documents from search results

• Format Handling: Process various document formats from search results

• Pipeline Integration: Feed collected documents into the processing pipeline

Data Storage

Qwello uses a combination of storage solutions for different data types:

• MongoDB: Document database for knowledge graphs and metadata

• AWS S3: Object storage for PDF documents and images

• Redis: In-memory data store for caching and job queue management

Job Processing

Qwello implements a sophisticated job processing system for handling document processing tasks:

• BullMQ: Redis-based queue for reliable job processing

• Worker Pool: Parallel processing of document pages

• Job Prioritization: Intelligent scheduling of processing tasks

• Progress Tracking: Real-time monitoring of job status

• Error Handling: Robust recovery from processing failures

Module Structure

Qwello's backend is organized into functional modules:

src/
├── app.module.ts            # Main application module
├── common/                  # Common utilities and helpers
├── config/                  # Configuration management
├── interfaces/              # TypeScript interfaces
├── modules/
│   ├── ai/                  # AI integration module
│   ├── auth/                # Authentication module
│   ├── chats/               # Chat functionality
│   ├── pdf/                 # PDF processing module
│   ├── search/              # Search functionality
│   └── user/                # User management
└── telegram/                # Telegram bot integration

This modular structure enables:

• Separation of Concerns: Each module handles a specific aspect of functionality

• Code Reusability: Common components can be shared across modules

• Maintainability: Changes to one module don't affect others

• Testability: Modules can be tested independently

• Scalability: Modules can be deployed separately if needed

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PDF Processing Pipeline

Qwello's PDF processing pipeline is a sophisticated multi-stage system that transforms PDF documents into structured knowledge graphs. This pipeline leverages advanced AI models and parallel processing to efficiently handle documents of any size and complexity.

Pipeline Overview

Input Methods

Qwello provides two primary methods for acquiring documents:

1. Direct PDF Upload

Users can upload PDF documents directly to the platform:

• File Selection: Choose files from local storage

• Drag and Drop: Intuitive drag-and-drop interface

• Size Limits: Support for a document up to 30MB

2. Web Search Integration

Users can search for relevant documents across the web:

• Query Input: Enter natural language queries in the "New Thread" interface

• SERP API: Search Google for relevant documents using SERP API

• Automatic Collection: Retrieve and process relevant documents from search results

• Seamless Processing: Apply the same processing pipeline to collected documents

Processing Stages

1. PDF Upload and Validation

The pipeline begins with document upload and validation:

• File Validation: Verify PDF format and check for corruption

• Size Verification: Ensure the document is within size limits (up to 30MB)

• Page Count Determination: Identify the number of pages for processing planning

• Metadata Extraction: Extract document metadata for later reference

• Storage: Save the original PDF to AWS S3 for persistence

2. Image Conversion

The PDF is converted to optimized images for AI processing:

• Page Extraction: Convert each PDF page to a separate image

• Image Optimization: Apply "Heavy L3" optimization for optimal AI processing

  Optimization Parameters:
  - Width: 800 pixels
  - Format: WebP
  - Quality: 70%
  - Grayscale: true

• Aspect Ratio Preservation: Maintain the original document proportions

• Parallel Processing: Convert multiple pages simultaneously for efficiency

3. Text Extraction

Images are processed by AI vision models to extract structured text:

• Vision AI Processing: Use Grok Vision model to analyze page images

• Text Recognition: Extract text while preserving layout and formatting

• Structure Preservation: Maintain headings, paragraphs, lists, and tables

• Markdown Conversion: Convert extracted content to structured markdown

• Page Boundary Marking: Clearly mark page transitions for reference

4. Knowledge Graph Generation

Extracted text is analyzed to generate a knowledge graph:

• Entity Identification: Recognize key concepts, people, organizations, etc.

• Entity Classification: Assign appropriate types to identified entities

• Relationship Extraction: Determine connections between entities

• Attribute Assignment: Extract relevant attributes for entities and relationships

• JSON Structure Creation: Format the knowledge graph in a structured JSON format

5. Graph Merging

Individual page graphs are merged into a unified knowledge graph:

• Entity Resolution: Identify when entities across pages refer to the same concept

• ID Mapping: Create consistent entity IDs across the document

• Attribute Consolidation: Combine attributes from multiple mentions

• Relationship Deduplication: Remove redundant relationships

• Page Reference Tracking: Maintain references to original page locations

6. Report Generation

The final knowledge graph is analyzed to generate comprehensive reports:

• Graph Analysis: Process the complete knowledge graph for insights

• Large Graph Chunking: Break down large graphs for efficient processing

• Context-Aware Analysis: Maintain semantic continuity across report sections

• Markdown Formatting: Generate well-structured, readable reports

• Storage and Indexing: Save reports for later retrieval and reference

Parallel Processing Architecture

Qwello implements a worker-based parallel processing architecture for efficient document handling:

Key aspects of this architecture include:

• Worker Pool: Multiple workers process different pages simultaneously

• Task Distribution: Pages are assigned to workers based on availability

• Resource Management: Worker count adapts to available system resources

• Progress Tracking: Real-time monitoring of processing status

• Result Collection: Processed pages are collected for merging

• Error Handling: Failed tasks are retried or redirected to fallback processors

This parallel architecture enables Qwello to process large documents efficiently, with processing time scaling linearly with document size rather than exponentially.

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Knowledge Graph Generation

At the heart of Qwello's capabilities is its sophisticated knowledge graph generation system. This system transforms document content into structured, interconnected knowledge that can be visualized, queried, and analyzed.

Knowledge Graph Structure

Qwello generates knowledge graphs with a clear, consistent structure:

{
  "entities": [
    {
      "id": "e1",
      "type": "concept",
      "name": "Entity Name",
      "attributes": {
        "description": "Entity description",
        "mentioned_on_pages": [1, 2, 3],
        "additional_attributes": "As needed"
      }
    }
  ],
  "relationships": [
    {
      "source": "e1",
      "target": "e2",
      "type": "relationship_type",
      "attributes": {
        "description": "Relationship description",
        "mentioned_on_pages": [1, 2],
        "additional_attributes": "As needed"
      }
    }
  ]
}

Entity Types

Qwello recognizes and classifies entities into various types:

• Concept: Abstract ideas, theories, or principles

• Person: Individual people mentioned in the document

• Organization: Companies, institutions, or groups

• Location: Physical places or geographical regions

• Technology: Technical systems, tools, or methodologies

• Method: Processes, procedures, or techniques

• Event: Occurrences or happenings with temporal aspects

• Document: References to other documents or publications

• Product: Specific products or offerings

• Time: Temporal references or time periods

Entity Resolution Process

One of Qwello's key strengths is its sophisticated entity resolution system, which identifies when different mentions refer to the same underlying entity:

1. Name Matching: Compare entity names for exact or fuzzy matches

2. Acronym Resolution: Recognize acronyms and their expanded forms

3. Contextual Analysis: Use surrounding context to disambiguate similar entities

4. Attribute Comparison: Compare entity attributes for similarity

5. Reference Pattern Analysis: Analyze how entities are referenced throughout the document

When entities are resolved as referring to the same concept, their attributes and relationships are merged, creating a more comprehensive representation of the entity.

Relationship Identification

Qwello identifies various types of relationships between entities:

• Hierarchical: Parent-child, superclass-subclass relationships

• Associative: General connections between entities

• Temporal: Time-based relationships (before, after, during)

• Causal: Cause-effect relationships

• Spatial: Location-based relationships

• Functional: Relationships based on function or purpose

• Compositional: Part-whole relationships

Each relationship is assigned a specific type and may include additional attributes that provide context or qualification.

Graph Merging Algorithm

Qwello's graph merging algorithm combines individual page graphs into a unified knowledge graph:

1. Entity Mapping: Create a mapping between page-level entity IDs and master graph IDs

2. Entity Resolution: Determine which entities refer to the same concept

3. Attribute Merging: Combine attributes from multiple mentions of the same entity

4. Relationship Consolidation: Merge relationships that connect the same entities

5. Page Reference Tracking: Maintain references to original page locations

6. Consistency Checking: Ensure the resulting graph maintains logical consistency

This merging process creates a comprehensive knowledge graph that represents the entire document while preserving the context and provenance of each entity and relationship.

Knowledge Graph Enrichment

After the initial graph is generated, Qwello applies several enrichment processes:

• Source Attribution: Link entities and relationships to their source locations

• External Knowledge Integration: Enhance entities with external knowledge when available

• Hierarchical Organization: Identify and represent hierarchical structures

• Topic Clustering: Group related entities into topical clusters

These enrichment processes enhance the value and utility of the knowledge graph, making it more useful for analysis and insight generation.

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AI Model Integration

Qwello leverages multiple specialized AI models to achieve optimal results at different stages of the document processing pipeline. This multi-model approach ensures high-quality results while providing fallback options for reliability.

AI Model Architecture

Primary Models

Grok Vision Model

• Model: x-ai/grok-2-vision-1212

• Purpose: Convert document images to structured text

• Capabilities:

  • Layout recognition and preservation

  • Table structure identification

  • List formatting maintenance

  • Mathematical formula handling

  • Hierarchical heading recognition

  • Multi-language text extraction

• Input: Base64-encoded page images

• Output: Structured markdown text

Grok Language Model

• Model: x-ai/grok-2-1212

• Purpose: Generate knowledge graphs from extracted text

• Capabilities:

  • Entity extraction and classification

  • Relationship identification

  • Attribute assignment

  • Context understanding

  • Semantic analysis

  • Structured output generation

• Input: Markdown text from document pages

• Output: JSON-structured knowledge graph

DeepSeek Model

• Model: deepseek/deepseek-r1

• Purpose: Analyze large knowledge graphs and generate reports

• Capabilities:

  • Large context processing (up to 30,000 tokens)

  • Comprehensive analysis of complex graphs

  • Insight generation and summarization

  • Structured report creation

  • Recommendation formulation

• Input: Complete or chunked knowledge graph

• Output: Markdown-formatted analytical report

Fallback Models

Claude Vision Model

• Model: anthropic/claude-3.7-sonnet

• Purpose: Fallback for image-to-text conversion

• Activation: When Grok Vision encounters rate limits or errors

• Capabilities: Similar to Grok Vision but with different strengths and limitations

• Integration: Seamless fallback with consistent input/output format

Claude Language Model

• Model: anthropic/claude-3.7-sonnet

• Purpose: Fallback for knowledge graph generation

• Activation: When Grok Language encounters rate limits or errors

• Capabilities: Similar to Grok Language but with different strengths and limitations

• Integration: Seamless fallback with consistent input/output format

AI Integration Architecture

Qwello implements a sophisticated AI integration architecture through its Cloudflare AI provider:

1. Provider Resolution: Determine the appropriate AI provider for the request

2. Model Selection: Choose the optimal model for the specific task

3. Request Formatting: Prepare the input in the format expected by the model

4. API Communication: Send the request to the Cloudflare AI endpoint

5. Response Processing: Parse and validate the model's response

6. Error Handling: Detect and address any issues with the response

7. Fallback Activation: Switch to fallback models when necessary

This architecture ensures reliable AI processing even in the face of rate limits, temporary outages, or other challenges.

Prompt Engineering

Qwello uses carefully crafted prompts to guide AI models:

Vision Model Prompt

const markdownPrompt = (pageNum) => `
  You are an expert document analyzer specializing in converting document images to clean, structured markdown.
  
  For page ${pageNum}, follow these guidelines:
  1. Begin with a marker "---PAGE ${pageNum}---"
  2. Format headings properly using # syntax based on their hierarchy
  3. Convert tables to proper markdown table syntax
  4. Format lists as markdown bulleted or numbered lists
  5. Preserve mathematical formulas using LaTeX syntax when needed
  6. Maintain exact terminology, technical jargon, and specialized vocabulary
  7. Remove any PDF artifacts, duplicated text, or OCR errors
  
  Your output should be clean, well-structured markdown that accurately represents the document content.
`;

Knowledge Graph Prompt

const kgPrompt = `
  You are an expert knowledge graph creator. Your task is to analyze text and extract a structured knowledge graph.
  
  Create a knowledge graph with the following structure:
  {
    "entities": [
      {
        "id": "e1",
        "type": "concept|person|organization|location|technology|method|event|document|product|time",
        "name": "Entity Name",
        "attributes": {
          "description": "Entity description",
          "additional_attributes": "As needed"
        }
      }
    ],
    "relationships": [
      {
        "source": "e1",
        "target": "e2",
        "type": "relationship_type",
        "attributes": {
          "description": "Relationship description"
        }
      }
    ]
  }
  
  Guidelines:
  1. Assign meaningful entity types from the provided categories
  2. Create descriptive relationship types
  3. Include relevant attributes for entities and relationships
  4. Ensure all relationships reference valid entity IDs
  5. Focus on the most important concepts and relationships
  
  Return ONLY the JSON knowledge graph without additional text.
`;

These carefully engineered prompts ensure consistent, high-quality outputs from the AI models, guiding them to produce results in the exact format required by subsequent processing stages.

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User Interface and Visualization

Qwello provides a sophisticated user interface that makes complex knowledge graphs accessible and intuitive to explore. The interface combines powerful visualization capabilities with intuitive controls for filtering, searching, and analyzing document content.

Knowledge Graph Visualization

At the core of Qwello's interface is its interactive knowledge graph visualization:

Example visualization of a knowledge graph about quantum computing

The visualization is powered by vis-network.js, a library for network visualization that provides:

• Force-Directed Layout: Automatically arranges entities and relationships for optimal viewing

• Interactive Navigation: Zoom, pan, and drag capabilities for exploring the graph

• Node Selection: Click on entities to view detailed information

• Edge Highlighting: Hover over relationships to see connection details

• Customizable Appearance: Color-coding based on entity and relationship types

• Performance Optimization: Efficient rendering of large graphs with thousands of nodes

Entity Type Color Coding

Qwello uses a consistent color scheme to distinguish different entity types:

• Concept: Blue (#4285F4)

• Person: Red (#EA4335)

• Organization: Yellow (#FBBC05)

• Location: Green (#34A853)

• Technology: Purple (#9C27B0)

• Method: Orange (#FF9800)

• Event: Brown (#795548)

• Document: Blue Grey (#607D8B)

• Product: Cyan (#00BCD4)

• Time: Pink (#E91E63)

This color coding makes it easy to visually identify different types of entities within the knowledge graph.

User Interface Components

Thread Interface

Qwello's thread interface provides a streamlined way to create and manage document analysis sessions:

• New Thread Creation: Create a new analysis thread via the "+ New Thread" button

• Dual Input Methods: Choose between document upload or web search

• Query Input: Enter natural language queries for web search

• Search Initiation: Click the green arrow to initiate search

• Thread History: Access previous analysis threads

• Thread Organization: Group related threads for better organization

Graph Controls

The interface provides several controls for interacting with the knowledge graph:

• Zoom Controls: Buttons for zooming in and out of the graph

• Fit Button: Automatically adjust the view to show the entire graph

• Layout Options: Different layout algorithms for various graph structures

• Export Options: Save the graph as an image or download the data

Entity Filters

Users can filter the graph based on various criteria:

• Entity Type Filter: Show/hide entities of specific types

• Relationship Type Filter: Show/hide specific types of relationships

• Minimum Connections Filter: Show only entities with a minimum number of connections

• Search Filter: Show only entities matching a search term

Entity Details Panel

When a user selects an entity, a details panel displays comprehensive information:

• Entity Name and Type: Basic identification information

• Description: Detailed description of the entity

• Attributes: All attributes associated with the entity

• Page References: Links to pages where the entity is mentioned

• Connected Entities: List of entities connected to the selected entity

• Relationships: Details of relationships involving the entity

Search Functionality

Qwello provides powerful search capabilities:

• Full-Text Search: Search across all entity names and descriptions

• Type-Specific Search: Limit search to specific entity types

• Attribute Search: Search within entity attributes

• Relationship Search: Find specific types of relationships

• Advanced Queries: Combine multiple search criteria

Report View

In addition to the graph visualization, Qwello provides a structured report view:

• Executive Summary: High-level overview of the document content

• Key Entities: Analysis of the most important entities

• Relationship Analysis: Examination of significant connections

• Topic Clusters: Identification of related concept groups

• Statistical Overview: Quantitative analysis of the knowledge graph

• Recommendations: Suggested areas for further exploration

Responsive Design

Qwello's interface is designed to work across different devices and screen sizes:

• Desktop Optimization: Full-featured experience for large screens

• Tablet Support: Adjusted layout for medium-sized screens

• Mobile Adaptations: Essential functionality preserved on small screens

• Adaptive Controls: Interface elements that adjust to available space

• Touch Support: Gesture recognition for touch-enabled devices

Accessibility Features

Qwello implements several accessibility features:

• Keyboard Navigation: Full keyboard control of the interface

• Screen Reader Support: ARIA attributes for screen reader compatibility

• High Contrast Mode: Enhanced visibility for users with visual impairments

• Text Scaling: Support for browser text size adjustments

• Focus Indicators: Clear visual indicators for keyboard focus

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Contact Information

For more information about Qwello, please contact:

Qwello, Inc. Website: https://qwello.ai

© 2025 Qwello, Inc. All rights reserved.