OpenAI Connector

Connect OpenAI to Brevo through Tajo to leverage AI-powered content generation, customer sentiment analysis, intelligent segmentation, and predictive analytics for your marketing automation workflows.

Overview

Features

• AI content generation - Generate email subject lines, body copy, and CTAs with GPT models
• Customer sentiment analysis - Analyze support tickets and feedback for sentiment scoring
• Smart segmentation - Use embeddings to cluster customers by behavior patterns
• Predictive analytics - Forecast churn, LTV, and purchase propensity
• Multi-language content - Generate marketing content in any supported language
• Image generation - Create campaign visuals with DALL-E integration

Prerequisites

Before you begin, ensure you have:

1. An OpenAI account with API access
2. An API key from the OpenAI dashboard
3. A Brevo account with API access
4. A Tajo account with connector permissions
5. Sufficient OpenAI API credits for your expected usage

Authentication

API Key Authentication

OpenAI uses Bearer token authentication for all API requests:

# Set your API keys
export OPENAI_API_KEY=sk-your-api-key
export TAJO_API_KEY=your_tajo_api_key
export BREVO_API_KEY=your_brevo_api_key

// All requests require the Authorization header
const headers = {
  'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`,
  'Content-Type': 'application/json'
};

// For organization-scoped access
const orgHeaders = {
  ...headers,
  'OpenAI-Organization': process.env.OPENAI_ORG_ID,
  'OpenAI-Project': process.env.OPENAI_PROJECT_ID
};

Configuration

Basic Setup

connectors:
  openai:
    enabled: true
    model: "gpt-4o"
    embedding_model: "text-embedding-3-small"
    image_model: "dall-e-3"

    features:
      content_generation: true
      sentiment_analysis: true
      smart_segmentation: true
      predictive_analytics: true

    limits:
      max_tokens_per_request: 4096
      max_requests_per_minute: 60
      temperature: 0.7

Content Generation Templates

templates:
  email_subject:
    model: "gpt-4o"
    system_prompt: |
      You are an expert email marketer. Generate compelling
      subject lines that drive open rates.
    max_tokens: 100
    temperature: 0.8

  email_body:
    model: "gpt-4o"
    system_prompt: |
      Generate personalized email content based on customer
      data and campaign objectives.
    max_tokens: 2048
    temperature: 0.7

API Endpoints

Code Examples

Initialize Connector

import { TajoClient } from '@tajo/sdk';

const tajo = new TajoClient({
  apiKey: process.env.TAJO_API_KEY,
  brevoApiKey: process.env.BREVO_API_KEY
});

await tajo.connectors.connect('openai', {
  apiKey: process.env.OPENAI_API_KEY,
  defaultModel: 'gpt-4o'
});

Generate Email Content

// Generate personalized email subject lines
const response = await fetch('https://api.openai.com/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'gpt-4o',
    messages: [
      {
        role: 'system',
        content: 'Generate 5 compelling email subject lines for a product launch.'
      },
      {
        role: 'user',
        content: `Product: ${product.name}. Target: ${segment.description}.`
      }
    ],
    max_tokens: 200,
    temperature: 0.8
  })
});

const result = await response.json();
const subjectLines = result.choices[0].message.content;

Customer Sentiment Analysis

// Analyze customer feedback sentiment
const sentimentAnalysis = await fetch(
  'https://api.openai.com/v1/chat/completions',
  {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'gpt-4o',
      messages: [
        {
          role: 'system',
          content: 'Analyze sentiment. Return JSON: {score: -1 to 1, label: string, topics: string[]}'
        },
        { role: 'user', content: customerFeedback }
      ],
      response_format: { type: 'json_object' },
      max_tokens: 150
    })
  }
);

const sentiment = await sentimentAnalysis.json();
await tajo.contacts.update(email, {
  attributes: { SENTIMENT_SCORE: JSON.parse(sentiment.choices[0].message.content).score }
});

Smart Segmentation with Embeddings

// Generate embeddings for customer clustering
const embeddingResponse = await fetch(
  'https://api.openai.com/v1/embeddings',
  {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'text-embedding-3-small',
      input: customerDescriptions,
      dimensions: 256
    })
  }
);

const embeddings = await embeddingResponse.json();
// Use embeddings for similarity-based customer clustering

Rate Limits

Troubleshooting

Debug Mode

connectors:
  openai:
    debug: true
    log_level: verbose
    log_prompts: false  # Don't log prompts in production
    log_usage: true

Best Practices

1. Cache responses - Store generated content to reduce API calls and costs
2. Use structured outputs - Request JSON responses for reliable parsing
3. Implement retry logic - Handle rate limits with exponential backoff
4. Monitor token usage - Track consumption to control costs
5. Use appropriate models - Use gpt-4o-mini for simple tasks, gpt-4o for complex ones
6. Validate outputs - Always validate AI-generated content before sending to customers

Security

• Bearer token auth - API keys transmitted via Authorization header
• Server-side only - Never expose API keys in client-side code
• Key rotation - Rotate API keys regularly via OpenAI dashboard
• Usage monitoring - Set spending limits in OpenAI billing settings
• Content moderation - Use the Moderations API to filter unsafe content
• Data privacy - Review OpenAI’s data usage policies for your use case

Related Resources

• Brevo Connector
• Customer Sync Skill
• Email Marketing Skills
• OpenAI API Reference

Open-Source Implementation Map

This section is derived from official or public repository material discovered for the OpenAI connector. Use it as the engineering companion to the setup guide above: it shows where the API surface lives, what implementation assets exist, and how Tajo should translate them into reliable Brevo sync behavior.

Repository Snapshot

Integration Shape

graph LR
  Source["OpenAI API / repository"] --> Auth["Auth and scopes"]
  Source --> Objects["Objects, events, and schemas"]
  Auth --> Tajo["Tajo connector runtime"]
  Objects --> Tajo
  Tajo --> Brevo["Brevo contacts, attributes, lists, campaigns"]
  Tajo --> Ops["Backfill, cursor, retries, logs"]

What To Reuse

• OpenAPI spec for the OpenAI API
• The most recent OpenAPI specification for the OpenAI API can be found here:
• https://app.stainless.com/api/spec/documented/openai/openapi.documented.yml
• To see the most recent manually updated version of the OpenAPI spec, refer to
• this branch:

Tajo Revamp Checklist

• Keep authentication setup aligned with the vendor docs and the public repository’s current API shape.
• Map primary resources into explicit Tajo sync objects with stable external IDs.
• Prefer cursor-based or updated-at incremental sync where the API exposes it; otherwise document the fallback.
• Treat webhook handlers as idempotent and replay-safe, especially for order, contact, ticket, and campaign events.
• Capture pagination, rate limits, retry headers, and partial-failure behavior in connector smoke tests.
• Keep examples small and runnable against sandbox or test-mode accounts.

Sources

• openai/openai-openapi
• Vendor documentation