Make.com

Integrate TensorX AI models with Make.com (formerly Integromat) to build powerful automated scenarios.

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Overview

Make.com is a visual automation platform that connects thousands of apps. With TensorX's OpenAI-compatible API, you can add AI capabilities to any Make scenario.

What You Can Build

• 📧 Email Automation - Auto-reply, summarize, and classify emails
• 🔄 Data Enrichment - Enhance records with AI-generated insights
• 📱 Social Media Bots - Generate and schedule content
• 📊 Report Generation - Create summaries from data
• 🛒 E-commerce - Product descriptions, review analysis
• 📞 Customer Support - Intelligent ticket routing and responses

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Prerequisites

• Make.com account (Free tier works)
• TensorX API key from app.tensorx.ai

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Method 1: OpenAI Module with Custom Endpoint

Make's OpenAI module supports custom API endpoints, making TensorX integration simple.

Step 1: Create OpenAI Connection

1. In Make, go to Connections
2. Click Create a connection
3. Search for OpenAI
4. Configure the connection:

Field	Value
Connection Name	TensorX AI
API Key	Your TensorX API key
Organization ID	Leave empty

Step 2: Override the Base URL

When configuring an OpenAI module, expand Show advanced settings:

Setting	Value
Override Base URL	✅ Enabled
Base URL	https://api.tensorx.ai/v1

Step 3: Configure the Request

For a Create a Chat Completion action:

Field	Value
Model	deepseek/deepseek-chat-v3.1
Messages	Add your conversation
Max Tokens	1000 (adjust as needed)
Temperature	0.7

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Method 2: HTTP Module (Full Control)

For complete flexibility, use Make's HTTP module.

Create Chat Completion

Add an HTTP > Make a request module:

Configuration:

Field	Value
URL	https://api.tensorx.ai/v1/chat/completions
Method	POST

Headers:

Key	Value
Authorization	Bearer YOUR_API_KEY
Content-Type	application/json

Body Type: Raw

Request Content:

{
  "model": "deepseek/deepseek-chat-v3.1",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "{{1.userInput}}"
    }
  ],
  "max_tokens": 1000,
  "temperature": 0.7
}

Parse Response: ✅ Yes

Extract the AI Response

Access the response in subsequent modules:

{{2.choices[1].message.content}}

Or use a JSON > Parse JSON module for structured access.

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Method 3: Reusable HTTP Connection

Create a connection you can reuse across scenarios:

1. Go to Connections → Create a connection
2. Select HTTP → Make a request
3. Choose API Key authentication:

Field	Value
API Key	Your TensorX API key
Add to	Header
Key Name	Authorization
Key Value Prefix	Bearer (with space)

1. Save as TensorX API

Now use this connection in any HTTP module targeting https://api.tensorx.ai.

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Example Scenarios

1. Email Auto-Responder

Automatically draft responses to incoming emails:

Gmail (Watch Emails)
    ↓
OpenAI (Create Completion)
    ↓
Gmail (Create Draft)

Prompt Template:

You are an email assistant. Draft a professional response to this email:

From: {{1.from.text}}
Subject: {{1.subject}}
Body: {{1.text}}

Draft a helpful, concise response.

2. Shopify Product Description Generator

Generate SEO-optimized descriptions for new products:

Shopify (Watch Products)
    ↓
OpenAI (Create Completion)
    ↓
Shopify (Update Product)

Prompt:

Write an engaging product description for:
Product: {{1.title}}
Vendor: {{1.vendor}}
Tags: {{1.tags}}

Include benefits, features, and a call-to-action. Keep it under 200 words. Use SEO-friendly language.

3. Slack Support Bot

Respond to Slack messages with AI:

Slack (Watch Messages)
    ↓
Filter (exclude bot messages)
    ↓
OpenAI (Create Completion)
    ↓
Slack (Create a Message)

4. Lead Enrichment

Enrich CRM leads with AI-generated insights:

CRM (New Lead)
    ↓
HTTP (Company lookup)
    ↓
OpenAI (Analyze company)
    ↓
CRM (Update Lead)

Prompt:

Based on this company information, provide:
1. Industry classification
2. Company size estimate
3. Potential use cases for our product
4. Recommended approach

Company: {{1.company_name}}
Website: {{2.website_content}}

5. Content Calendar Automation

Generate a week's worth of social content:

Schedule (Weekly)
    ↓
Iterator (7 days)
    ↓
OpenAI (Generate post)
    ↓
Array Aggregator
    ↓
Google Sheets (Add rows)

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Working with Responses

Parse the JSON Response

Make automatically parses JSON responses. Access fields like:

Field	Make Syntax
AI Response	{{X.choices[1].message.content}}
Model Used	{{X.model}}
Total Tokens	{{X.usage.total_tokens}}
Finish Reason	{{X.choices[1].finish_reason}}

Handle Multiple Messages

For multi-turn conversations, use an Array Aggregator to build the messages array:

{
  "messages": {{arrayAggregator.messages}}
}

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Recommended Models

Scenario Type	Recommended Model
General Text	deepseek/deepseek-chat-v3.1
Analysis/Reasoning	deepseek/deepseek-r1-0528
Code Generation	z-ai/glm-5.1
Long Documents	meta-llama/llama-4-maverick
Quick Responses	meta-llama/llama-3.3-70b-instruct

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Error Handling

Add Error Handlers

Right-click any module → Add error handler

Recommended handlers:

• Resume - Continue with default value
• Rollback - Undo changes and retry
• Break - Stop and notify

Common Errors

Error	Solution
401 Unauthorized	Check API key is correct
404 Not Found	Verify model name exactly
429 Rate Limited	Add delay between operations
500 Server Error	Retry with exponential backoff

Rate Limit Handling

Use the Sleep module between API calls:

API Call → Sleep (1 second) → Next API Call

For batch processing, use Iterator with built-in delay.

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Best Practices

1. Use Variables for API Key

Store your API key in a scenario variable:

• Go to Scenario settings → Variables
• Add tensorx_api_key
• Reference as {{tensorx_api_key}}

2. Optimize Token Usage

• Set appropriate max_tokens limits
• Use concise system prompts
• Summarize long inputs before processing

3. Structure Your Prompts

[ROLE]
You are a [specific role].

[TASK]
[Clear instruction]

[FORMAT]
Respond in [format].

[INPUT]
{{input_data}}

4. Monitor Usage

Add a Webhook or Google Sheets module to log:

• Token usage per request
• Response times
• Error rates

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Pricing Considerations

TensorX charges per token. Optimize costs by:

• Using smaller context when possible
• Caching repeated queries
• Choosing appropriate models for each task
• Setting reasonable max_tokens limits

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See Also

• API Reference - Full API documentation
• Models - Model capabilities and pricing
• Make.com Documentation - Official Make guides