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

Building AI applications doesn't have to be complicated. With Cuadra AI, you can go from idea to production in hours, not weeks. Our three-phase approach—Connect, Train, Launch—makes it simple to build custom AI applications from your own data.

The Three-Phase Framework

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

Connect your data and knowledge
Train your AI model with custom behavior
Launch your AI via production-ready 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

Create a Dataset - Give it a name and description
Upload Files - Select one or multiple files (up to 50MB each)
Automatic Processing - We extract text, chunk it into semantic segments, and generate embeddings
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)

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:

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

Go to the Train section of your model
Click "Attach Dataset"
Select the datasets you want to use
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.

Fine-Tuning (Optional)

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

Attach your training dataset to the model
Start a training job from the dashboard
Monitor training progress
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:

Navigate to the Deploy section of your model
View your API endpoint and authentication key
Copy the endpoint URL and API key
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:

javascript

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.

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 in hours, not weeks.

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

Connect, Train, Launch

Your Complete Guide

To Building AI