Connect, Train, Launch: Your Complete Guide to Building AI Applications

Build an AI application from your proprietary data in three simple phases: Connect (upload and process your data), Train (configure your model's behavior), and Launch (deploy via enterprise-grade API). Cuadra AI automates the entire workflow—data processing, embedding generation, RAG implementation, and API infrastructure—so you can focus on building AI that helps your business, not managing infrastructure.

The Three-Phase Framework

Every AI application built with Cuadra AI follows the same straightforward process:

1. Connect your data and knowledge
2. Train your AI model with custom behavior
3. Launch your AI via enterprise-grade API

Let's dive into each phase.

Phase 1: Connect

The Connect phase is where you bring your data into Cuadra AI. This data becomes the foundation of your AI's knowledge.

What You Can Connect

You can upload various file types to create knowledge bases called "Datasets":

• PDFs - Documentation, reports, manuals
• Microsoft Word (.docx) - Documents, guides, content
• Plain Text (.txt) - Notes, scripts, data
• Markdown (.md) - Documentation, README files
• CSV (.csv) - Structured data, lists
• JSON (.json) - Structured data, configurations

How It Works

1. Create a Dataset - Give it a name and description
2. Upload Files - Select one or multiple files (up to 50MB each)
3. Automatic Processing - We extract text, chunk it into semantic segments, and generate embeddings
4. Ready to Use - Your files become searchable and ready to enhance AI responses

What Happens Behind the Scenes

When you upload files, Cuadra AI automatically:

• Extracts text from documents
• Chunks content into semantic segments
• Generates embeddings for semantic search
• Makes your content searchable via RAG (Retrieval-Augmented Generation)

What happens automatically: This entire data processing pipeline runs automatically—extracting text, generating embeddings, and building your searchable knowledge base. Skip the weeks of infrastructure development—everything runs automatically.

You'll see the processing status update in real-time: uploaded → processing → ready.

Best Practices for Connect

• Organize by Purpose - Create separate datasets for different knowledge domains
• Keep Files Focused - Upload relevant, high-quality content
• Use Descriptive Names - Make it easy to identify datasets later
• Start Small - Begin with a few key files and expand as needed

Phase 2: Train

The Train phase is where you configure your AI model's behavior. This is where you make your AI uniquely yours.

Creating Your Model

Start by creating a new model in the dashboard:

1. Choose a Provider - Select from OpenAI, Anthropic, Cohere, or Mistral
2. Select a Model - Pick the specific model (e.g., gpt-4o, claude-3-5-sonnet)
3. Configure Settings - Set context window, token limits, and other parameters
4. Add Metadata - Include descriptions and custom metadata

Configuring Behavior

Each model has a profile where you define how it should behave:

• System Instructions - Define the AI's role, tone, and personality
• Response Guidelines - Set boundaries and rules for responses
• Output Format - Specify how responses should be structured
• Custom Directives - Add specific instructions for your use case

Attaching Datasets

Link your datasets to your model to give it access to your knowledge:

1. Go to the Train section of your model
2. Click "Attach Dataset"
3. Select the datasets you want to use
4. Your model can now reference this knowledge when responding

This enables RAG (Retrieval-Augmented Generation), allowing your AI to search your knowledge base and provide accurate, context-aware responses. Cuadra AI's no-code approach eliminates the need for manual RAG implementation—skip the weeks of infrastructure development and deploy quickly.

Fine-Tuning (Optional)

For advanced customization, you can fine-tune your model with your custom datasets:

1. Attach your training dataset to the model
2. Start a training job from the dashboard
3. Monitor training progress
4. Use your fine-tuned model once training completes

Best Practices for Train

• Be Specific - Clear instructions lead to better results
• Iterate - Test different configurations and refine
• Use Examples - Include examples in your instructions when helpful
• Attach Relevant Data - Connect datasets that match your use case
• Test in Chat - Use the chat playground to test before deploying

Phase 3: Launch

The Launch phase is where you deploy your AI model and integrate it into your application.

Getting API Access

Once your model is configured:

1. Navigate to the Deploy section of your model
2. View your API endpoint and authentication key
3. Copy the endpoint URL and API key
4. Start making API calls

API Features

Cuadra AI provides a production-ready REST API with:

• Standard REST Endpoints - Easy to integrate into any application
• Authentication - Secure API keys for each model
• Streaming Responses - Real-time token-by-token delivery
• Structured Outputs - JSON schema enforcement for consistent responses
• Usage Tracking - Monitor calls, tokens, and costs in real-time

Integration Examples

Web Application:

const response = await fetch('https://api.cuadra.ai/v1/models/your-model-id/chat', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    message: 'Your user input here'
  })
});

Mobile App:

• Integrate API calls in your backend
• Or call directly from app (with secure key storage)
• Use streaming for better user experience

Workflow Automation:

• Integrate into automated workflows
• Process data automatically
• Trigger based on events

Monitoring and Analytics

Track your AI's performance:

• Usage Dashboard - View API calls, token usage, and costs
• Real-Time Monitoring - See usage as it happens
• Cost Tracking - Monitor spending per model
• Performance Metrics - Track response times and success rates

Best Practices for Launch

• Start with Testing - Use the chat playground before deploying
• Monitor Usage - Keep an eye on costs and limits
• Handle Errors - Implement proper error handling in your integration
• Use Streaming - Enable streaming for better user experience
• Secure Your Keys - Keep API keys safe and rotate periodically

Real-World Example: Customer Support Chatbot

Let's see how the three phases work together:

Connect:

• Upload support documentation, FAQs, and product manuals
• Create a dataset called "Support Knowledge Base"

Train:

• Create a model with a helpful, professional personality
• Configure it to answer questions using the support documentation
• Attach the "Support Knowledge Base" dataset

Launch:

• Deploy via API
• Integrate into your website or support system
• Customers get instant, accurate answers 24/7

Common Questions

Q: How long does it take to go from Connect to Launch? A: You can complete all three phases in a few hours. Most of the time is spent configuring your model's behavior to match your needs. The platform automates infrastructure that typically takes weeks to build manually.

Q: Do I need to code? A: No coding required for Connect and Train phases. You'll use our intuitive dashboard. For Launch, you'll integrate via our standard REST API.

Q: Can I update my model after launching? A: Yes! You can update your model's configuration, instructions, and datasets anytime. Changes take effect immediately.

Q: What if I need help? A: Check our documentation, use the chat playground to test, or contact our support team.

Next Steps

Ready to build your AI application? Start with a free trial and experience the Connect → Train → Launch workflow yourself.

Whether you're building a customer support chatbot, a documentation assistant, or a custom AI for your product, Cuadra AI makes it simple to go from idea to production quickly—typically ready in 2-3 hours.

Have questions about the Connect → Train → Launch framework? Contact our team or explore our documentation.