

How to Draft Email Responses from Slack with Make
Paste a client email into a Slack channel and automatically generate a professional response using OpenAI's GPT-4.
Steps and UI details are based on platform versions at time of writing β check each platform for the latest interface.
Best for
Teams that regularly draft email responses and want AI assistance with professional tone
Not ideal for
High-volume customer service (use dedicated tools like Intercom) or personal email management
Sync type
real-timeUse case type
notificationReal-World Example
A 12-person B2B consulting firm uses this to draft client email responses in their #client-emails Slack channel. Account managers paste incoming client emails and get professional AI-drafted responses within 30 seconds. Before automation, junior staff spent 15-20 minutes crafting each response and senior partners had to review every outbound email for tone and accuracy.
What Will This Cost?
Drag the slider to your expected monthly volume.
Each platform counts differently β Zapier: 1 task per trigger. Make: 1 operation per module per record. n8n: 1 execution per run.





Prices shown for annual billing. Based on published pricing as of April 2026.
Estimated ROI
1000
min saved/mo
$583
labor value/mo
Free
no platform cost
Based on ~2 min manual effort per operation at $35/hr fully loaded labor cost.
Implementation
Import this workflow directly into Make
Copy the pre-built Make blueprint and paste it straight into Make. All modules, filters, and field mappings are already configured β you just need to connect your accounts.
Before You Start
Make sure you have everything ready.
Field Mapping
Map these fields between your apps.
| Field | API Name | |
|---|---|---|
| Required | ||
| Message Text | text | |
| Channel ID | channel | |
| Message Timestamp | ts | |
| AI Response Text | choices[0].text | |
| Thread Timestamp | thread_ts | |
1 optional fieldβΈ show
| User ID | user |
Step-by-Step Setup
Scenarios > Create new scenario > Slack
Create New Scenario
Start a new Make scenario to connect Slack message triggers with OpenAI response generation. This scenario will watch for new messages in a specific Slack channel.
- 1Log into Make and click 'Create a new scenario'
- 2Click the big + button in the center
- 3Search for 'Slack' and select it from the app list
- 4Choose 'Watch Messages' as your trigger
Slack module > Connection > Add
Connect Slack Account
Link your Slack workspace to Make so the scenario can access your channels and messages. You'll need admin permissions or pre-approval from your workspace admin.
- 1Click 'Add' next to the Connection field
- 2Select 'OAuth 2.0' connection type
- 3Click 'Continue' and authorize Make to access Slack
- 4Choose your workspace from the dropdown
Slack module > Configuration
Configure Channel Monitoring
Set which Slack channel to monitor for email content. Pick a dedicated channel like #email-drafts to avoid triggering on unrelated messages.
- 1Select your workspace from the Team dropdown
- 2Choose the target channel from the Channel dropdown
- 3Set 'Limit' to 1 to process one message at a time
- 4Leave 'Only messages from bots' unchecked
Add module > OpenAI > Create a Completion
Add OpenAI Module
Connect OpenAI's API to generate the email response. The GPT-4 model works best for professional email drafting compared to GPT-3.5.
- 1Click the + button to the right of your Slack module
- 2Search for 'OpenAI' and select it
- 3Choose 'Create a Completion' action
- 4Select 'GPT-4' from the Model dropdown
OpenAI module > Connection > Add
Connect OpenAI API
Add your OpenAI API key to authenticate requests. You'll need a paid OpenAI account since the free tier has strict rate limits.
- 1Click 'Add' next to the Connection field
- 2Paste your OpenAI API key in the API Key field
- 3Click 'Save' to test the connection
- 4Verify the green connection status appears
OpenAI module > Prompt field
Build the Prompt Template
Create a structured prompt that tells GPT-4 to draft a professional email response. The prompt should include the original email content from Slack.
- 1Click in the Prompt field to open the mapping panel
- 2Type: 'Draft a professional email response to the following:'
- 3Press Enter twice and click 'Text' under Slack data
- 4Select the message text field to insert the Slack message content
π¬ New entry: {{1.name}}
Email: {{1.email}}
Details: {{1.description}}usage.prompt_tokens: {{usage.prompt_tokens}}
usage.completion_tokens: {{usage.completion_tokens}}
OpenAI module > Parameters section
Configure Response Parameters
Set OpenAI parameters to generate appropriate email responses. Lower temperature gives more consistent professional tone.
- 1Set Max Tokens to 300 for typical email length
- 2Set Temperature to 0.3 for consistent professional responses
- 3Leave Top P at default value of 1
- 4Set Frequency Penalty to 0.2 to avoid repetition
Add module > Slack > Create a Message
Add Slack Response Module
Send the AI-generated response back to Slack so your team can review and use it. This creates a thread reply under the original message.
- 1Click + after the OpenAI module
- 2Search for 'Slack' and add another Slack module
- 3Choose 'Create a Message' action
- 4Use the same connection from step 2
Slack module > Message configuration
Configure Response Message
Set up the Slack message to post the generated response. Using a thread keeps the original email and response together.
- 1Select the same channel as your trigger
- 2Click in the Text field and select the OpenAI 'Text' output
- 3Enable 'Reply in thread' toggle
- 4Map the Thread TS field to the original message timestamp
π¬ New entry: {{1.name}}
Email: {{1.email}}
Details: {{1.description}}Scenario controls > Run once
Test the Scenario
Run a test to verify the complete workflow processes a message correctly. This checks all connections and data mapping.
- 1Click 'Run once' at the bottom of the scenario
- 2Go to your Slack channel and post a sample email
- 3Return to Make and wait for the execution to complete
- 4Check that a response appeared as a thread reply in Slack
OpenAI module > Right-click > Add error handler
Set Up Error Handling
Configure what happens when OpenAI is down or rate-limited. This prevents the scenario from breaking on temporary issues.
- 1Right-click the OpenAI module and select 'Add error handler'
- 2Choose 'Break' as the error handling directive
- 3Add a filter to catch HTTP 429 (rate limit) errors specifically
- 4Set a 2-minute delay before retry for rate limit errors
Scenario controls > Activate toggle
Activate the Scenario
Turn on the scenario to start monitoring your Slack channel continuously. Make will check for new messages every few minutes.
- 1Click the toggle switch in the bottom left corner
- 2Confirm activation in the popup dialog
- 3Set the scheduling to run every 5 minutes
- 4Save the scenario with a descriptive name like 'Email Response Drafter'
Drop this into a Make custom function.
Copy this templateAdd this filter between Slack and OpenAI modules to only process messages that look like emails:βΈ Show code
Add this filter between Slack and OpenAI modules to only process messages that look like emails:
{{contains(1.text; "@")}} AND {{contains(1.text; "Subject:")}} OR {{contains(1.text; "From:")}}... expand to see full code
Add this filter between Slack and OpenAI modules to only process messages that look like emails:
{{contains(1.text; "@")}} AND {{contains(1.text; "Subject:")}} OR {{contains(1.text; "From:")}}Scaling Beyond 50+ emails/day+ Records
If your volume exceeds 50+ emails/day records, apply these adjustments.
Switch to N8n Self-Hosted
At 1,500+ operations monthly, N8n's $50 cloud plan or $0 self-hosted option beats Make's $29 Pro plan. You'll need Docker knowledge for self-hosting.
Implement Response Caching
Add a Google Sheets module to cache similar email patterns and responses. Check for existing responses before calling OpenAI to reduce API costs by 30-40%.
Use GPT-3.5-Turbo Instead
Switch from GPT-4 to GPT-3.5-turbo to cut API costs from $0.03 to $0.002 per 1K tokens. Response quality drops slightly but cost savings are massive at scale.
Going live
Production Checklist
Before you turn this on for real, confirm each item.
Troubleshooting
Common errors and how to fix them.
Frequently Asked Questions
Common questions about this workflow.
Analysis
Use Make for this if you want visual workflow building without coding and need reliable OpenAI API handling. Make's built-in error handling and retry logic works better than Zapier for API-heavy workflows like this one. The visual interface makes it easy to add filters and conditions later. Skip Make if you're processing 50+ emails daily β N8n's self-hosted option becomes more cost-effective at that volume.
This workflow uses 3 operations per email: Slack trigger, OpenAI completion, and Slack response. At 100 emails monthly, that's 300 operations total. Make's Core plan at $9/month includes 10,000 operations, so you're well covered. Zapier would cost $20/month for their Professional plan to get the same OpenAI integration features. N8n cloud costs $50/month but includes unlimited operations.
Zapier's OpenAI integration includes built-in prompt templates for common email scenarios, which saves setup time compared to Make's blank prompt field. N8n offers better debugging with detailed execution logs and the ability to replay failed runs with modified data. But Make's error handling is more sophisticated β you can set different retry strategies for different error types, while Zapier just retries everything the same way.
OpenAI's GPT-4 API costs $0.03 per 1K tokens, and typical email responses use 200-400 tokens including the prompt. At 100 emails monthly, expect $6-12 in OpenAI costs on top of Make's fees. The API occasionally returns rate limit errors during peak hours β Make's built-in 429 handling retries automatically, but you'll see 2-3 minute delays. Long email chains can hit the 4K token context limit, causing truncated responses that miss important context from the original message.
Ideas for what to build next
- β
- βBuild Client-Specific Tone Templates β Use Make's router module to detect client names or channel patterns and apply different response templates (formal, casual, technical) based on the recipient.
- βCreate Response Performance Tracking β Log all generated responses to Google Sheets with timestamps and user feedback to analyze which types of emails need the most human editing.
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