Quick Start Guide

Get up and running with Qubitz AI in minutes. This guide will walk you through deploying your first AI solution using our pre-built templates.

Prerequisites

Before you begin, ensure you have:

• An AWS account with appropriate permissions
• Access to the Qubitz AI platform
• Basic understanding of your use case requirements

Step 1: Access the Platform

1. Navigate to the Qubitz AI Platform
2. Sign in with your credentials
3. You'll be greeted with the main dashboard

Step 2: Choose a Template

Qubitz AI offers pre-built templates for common use cases:

Step 3: Configure Your Solution

Using the Visual Builder

1. Select Your Template

   • Choose from industry-specific templates
   • Preview the solution before deployment

2. Customize Settings

   • Configure model selection (Claude, Titan, Llama)
   • Set up data sources and integrations
   • Define custom prompts and responses

3. Configure Data Sources

   • Connect to your existing data repositories
   • Set up RAG (Retrieval-Augmented Generation) if needed
   • Configure vector database settings

Example Configuration

# Sample configuration for a customer service bot
solution:
  name: "Customer Support Assistant"
  template: "support-bot"
  
models:
  primary: "claude-3-sonnet"
  fallback: "titan-text-express"
  
data_sources:
  - type: "knowledge_base"
    source: "company_docs"
    vector_db: "aurora_pgvector"
  
agents:
  - name: "query_classifier"
    role: "classify customer intent"
  - name: "response_generator"
    role: "generate helpful responses"
  - name: "escalation_handler"
    role: "escalate complex issues"

Step 4: Deploy Your Solution

One-Click Deployment

1. Review Configuration

   • Verify all settings are correct
   • Check data source connections
   • Validate agent configurations

2. Deploy to AWS

   • Click "Deploy" to launch your solution
   • Qubitz AI automatically provisions AWS resources
   • Monitor deployment progress in real-time

3. Access Your Solution

   • Receive deployment confirmation
   • Access your AI solution via provided URL
   • Test functionality with sample queries

Step 5: Monitor and Optimize

Real-Time Monitoring

Once deployed, monitor your solution through the Qubitz AI dashboard:

• Performance Metrics - Response times, accuracy, user satisfaction
• Usage Analytics - Query patterns, popular features, peak usage times
• Cost Tracking - AWS resource utilization and cost optimization

Continuous Improvement

• A/B Testing - Test different prompts and configurations
• Feedback Integration - Incorporate user feedback to improve responses
• Model Optimization - Switch between models based on performance

Example: Deploying a Customer Service Bot

Let's walk through deploying a customer service bot:

1. Template Selection

Choose the "Customer Support Bot" template from the retail/consumer section.

2. Configuration

• Model: Claude 3 Sonnet for natural conversation
• Knowledge Base: Connect to your company documentation
• Agents:
  • Query classifier for intent recognition
  • Response generator for helpful answers
  • Escalation handler for complex issues

3. Customization

• Upload your company's FAQ and support documents
• Configure escalation rules and human handoff points
• Set up integration with your existing support system

4. Deployment

• Deploy to your AWS account
• Receive a web interface URL
• Test with sample customer queries

5. Go Live

• Integrate the bot into your website or support portal
• Monitor performance and user satisfaction
• Continuously improve based on feedback

Next Steps

After deploying your first solution:

1. Explore Capabilities - Learn about advanced features
2. Multi-Agent Orchestration - Build complex workflows
3. AWS Architecture - Understand the technical foundation
4. Custom Development - Build custom solutions

Support

Need help with your deployment?

• Documentation - Comprehensive guides and tutorials
• Community Forum - Connect with other users
• Support Team - Get expert assistance
• Training - Attend workshops and webinars

Qubitz AI makes it easy to go from concept to production-ready AI solutions. Start with a template, customize to your needs, and deploy with confidence.