Working of Qubitz AI

This comprehensive guide walks you through the complete workflow of using Qubitz AI, from initial use case selection to final deployment and operations.

Overview

Qubitz AI provides a complete workflow for building, deploying, and operating enterprise-grade AI applications. This guide covers the entire process from start to finish.

Key Workflow Steps

1. Use Case Selection - Choose from pre-built templates or get AI-driven recommendations
2. Data Integration - Connect your data sources and configure datasets
3. RAG Configuration - Set up knowledge bases and embeddings
4. Agent Configuration - Create and configure AI agents
5. Security Setup - Configure guardrails and responsible AI
6. Deployment - Deploy your application and configure APIs
7. Operations - Monitor, evaluate, and optimize your solution

1. Use Case Selection

Qubitz AI simplifies the process of building AI-powered solutions by providing users with guided steps to select the most relevant use cases for their projects.

Dashboard Navigation (Home Screen)

Options Available:

• My Projects: Where you can view or manage your active projects
• Use Case Catalogs: A library of ready-to-use categorized AI use cases
• Use Case Tool: A guided assistant to help users find the best use case through forms and recommendations

1.1 My Projects Page

What You See:

• A search bar to look for existing projects
• A message saying "No projects created yet" for new users
• Two primary action buttons: Create Project and Choose Use Case Template

1.2 Choose a Use Case Blueprint

What You See:

• Categories: All, Standard, Community, My Templates
• Buttons: Find My Use Case (opens intelligent assistant form) and Proceed without blueprint

1.3 Deep Analysis: Intelligent Use Case Discovery

Qubitz AI's Deep Analysis workflow guides users through a structured, multi-step journey that culminates in personalized use case suggestions.

Step 1: Provide Basic Business Information

Users fill out a structured intake form capturing:

• Contact Name, Email Address, Company Name
• Country (dropdown selection)
• Company Website(s) (supports multiple entries)

Step 2: Upload Supporting Documents

Users upload internal documentation:

• Accepted file types: PDF, DOCX, XLSX, PPT, PNG, JPG
• Limits: Up to 5 documents, maximum 10 MB each

Step 3: AI Deep Analysis & Recommendation Generation

Behind the scenes, the system performs:

• Website Analysis: AI crawls submitted websites to extract industry-specific content
• Document Parsing: NLP pipelines detect themes, goals, pain points, and strategic priorities
• Semantic Matching: Vector embeddings match business context against use case catalog
• Recommendation Generation: Ranked list with relevance scores, expected impact, and effort levels

2. Add a Data Source / Dataset Connector

The source/dataset connector links your data to the platform for further processing and analysis.

2.1 Basic Configuration

App Name: Enter a unique and descriptive name for your application Description: Provide a comprehensive description outlining the purpose and scope

2.2 Data Integration

Datasets

Import Options:

• Select an existing dataset
• Upload your own files
• Import dataset from external storage

Dataset Details: Name, Type, Size, and Action options

Data Sources

Connector Types Supported:

• SharePoint (client id/secret id)
• Confluence
• Web Crawler
• Google Drive
• GraphQL API
• REST API
• Custom Connection

3. Configure Context Management (RAG)

3.1 RAG Configuration Interface

Setup Process:

• Knowledge base name: Unique identifier for your repository
• Description: Comprehensive explanation of purpose and scope

Embeddings Model Selection

Amazon OpenSearch Serverless:

• Ideal for scalable, real-time search and analytics
• High-throughput data ingestion and rapid search responses

Amazon Aurora Postgres Serverless:

• Highly available, scalable relational database solution
• Strong transactional consistency and automated scaling

Amazon Neptune Analytics (GraphRAG):

• Optimized for graph-based data structures
• Complex relationship queries and pattern matching

Source Data Integration

Dataset Association: Link to pre-configured datasets Datasource Configuration: Connect using suitable connectors

Pipeline Activation

Add RAG Button: Initiates integration of knowledge base, embeddings model, and source data

4. Agents Configuration

The Qubitz Control Hub serves as the central interface for managing and configuring your AI agents.

4.1 Agents Configuration Overview

• Refresh Button: Updates agent list
• Add Agent Button: Creates new AI agent
• No Agents Configured Yet: Prompts new users to begin

4.2 The Agent Creation Bot

Functionality:

• Intelligent assistant leveraging large language models
• Natural language description of desired application
• Automatic suggestion of Type, Model, Instructions, and Tools

Features:

• Welcome message and input field (0/500 character limit)
• Quick examples: "Build a learning management system", "Create a fraud detection system"
• Send icon for processing descriptions

4.3 Adding a New Agent: Custom Configuration

Agent Details

• Name*: Mandatory descriptive name
• Type*: Primary function category (AI Text Generator, Image Recognition, etc.)
• Model*: Underlying LLM selection
• Region*: Geographical hosting location
• Description (Optional): Role and purpose explanation

Model Parameters

Temperature: Controls randomness (0.1-0.3 deterministic, 0.7-1.0 creative) Max Tokens: Maximum response length (default: 2000) Top P: Sampling diversity control (default: 0.9)

Agent Instructions and Tools

Instructions*: Detailed system prompt defining persona, task, constraints Memory Enabled: Toggle for context retention Tools: External tool/API integration capabilities Add Tool Button: Configure specific tools (search API, database queries, etc.)

5. Security Overview

Robust controls for safe and responsible AI application operation.

5.1 Guardrails

Harmful: Control AI sensitivity to harmful content (none to high) Prompt Attack: Protection against malicious prompts and adversarial attacks

5.2 Responsible AI

Denied Topics: Explicitly define and prevent discussion of specific subjects PII Data: Configure handling of sensitive personal data (Redact/Block/Warn) Grounded Score: Verify response accuracy against source data (0.0-1.0) Relevance Score: Ensure retrieved information relevance (0.0-1.0)

6. App Overview

High-level overview of deployed Qubitz application status and management options.

6.1 Application Name

Deployment Status: Green Deploy button indicating successful deployment Resave as Template: Save current configuration as reusable template App ID: Unique identifier for programmatic access Deployed Version: Current application version

6.2 Agentic Config

Version: Agent configuration version Refresh Icon: Update agent configuration view

7. Deploy an Agent

7.1 Custom URL Configuration

Deployed URL: Custom URL for application access with copy functionality

7.2 API

API Endpoint Tab

API Endpoint: Unique endpoint URL with visibility toggle and copy functionality Regenerate: Create new API endpoint (invalidates previous) Example API Request: Sample curl command with headers and JSON payload

7.3 Documentation

API Documentation: Reference documentation for endpoints and formats Endpoints: Available API endpoints with methods and descriptions Response Format: Sample JSON response structure with fields:

• id, object, created, model
• result, answer, confidence

8. Operate

Operations phase for monitoring, evaluating, and optimizing deployed AI solutions.

Evaluate

Assessment tools for AI agent performance and effectiveness

Trace

Execution flow and decision-making process tracking

Metric

Comprehensive system performance and business outcome insights

9. FinOps Dashboard

Detailed cost analysis and optimization recommendations for cloud spending management.

10. Sustainability Metrics

Environmental impact tracking including energy consumption and carbon footprint.

This comprehensive workflow guide covers the entire lifecycle of building, deploying, and operating AI solutions with Qubitz AI. Each step is designed to be intuitive and powerful, enabling both technical and non-technical users to create enterprise-grade AI applications.