Need a simple way to fill missing emails for leads at scale? This setup pulls people from your database and finds their emails in bulk using Dropcontact. It suits sales teams that want clean contact data without manual research.

A scheduled run checks PostgreSQL for profiles with no email and a valid company domain. Records are grouped in batches of 250, combined, and shaped by a code step into the exact format Dropcontact needs. The workflow sends a batch request, waits about ten minutes for processing, then downloads the results. It splits the returned data per person and updates your profiles table with email, phone, name, and a found flag. A Slack message can alert you if credits run low or a request fails.

You will need a PostgreSQL database, a Dropcontact API key, and a Slack workspace. Expect higher email coverage, faster list prep, and steady throughput of up to about 1500 requests per hour. This is ideal for outbound lists built from job titles or roles where emails are missing, and where you want data to stay in your own database.

• Scheduled runs fetch profiles without emails from PostgreSQL.
• Batching at 250 records to respect service limits and speed up processing.
• Code step formats names and domains into the exact Dropcontact payload.
• Bulk request to Dropcontact and timed wait for asynchronous processing.
• Result download and split so each person is handled cleanly.
• Database update writes email, phone, and a found flag to the profiles table.
• Slack alert to notify about credit issues or processing problems.
• Field mapping supports email arrays and avoids empty strings.

• Reduce manual email research from hours to minutes
• Automate up to 100 percent of enrichment work for missing emails
• Handle about 1500 requests per hour with batch processing
• Improve data quality by writing validated emails back to your database
• Connect PostgreSQL, Dropcontact, and Slack in one flow

1. Import the template into n8n: Create a new workflow in n8n > Click the three dots menu > Select 'Import from File' > Choose the downloaded JSON file.
2. You'll need accounts with PostgreSQL, Dropcontact and Slack. See the Tools Required section above for links to create accounts with these services.
3. In the n8n credentials manager, create a PostgreSQL credential. Enter host, port, database, user, and password. Enable SSL if your server requires it. Name it clearly such as PostgreSQL Production and test the connection.
4. Open the Dropcontact HTTP Request nodes and click Create new credential. Choose API Key. In your Dropcontact account, create an API key from the API page, paste it into n8n, name the credential, and save.
5. Open the Slack node, click Create new credential, and connect your Slack workspace. Approve the scopes and select the channel for alerts.
6. Open the Schedule Trigger and set how often it should run, for example hourly or daily based on your volume and credits.
7. Review the PROFILES QUERY node. Adjust the SQL to match your schema, table names, and the role or title you want to enrich. Make sure the filter selects records with no email and a valid domain.
8. Check the Split in Batches node and keep batch size at 250 unless your plan requires a smaller size.
9. Open the Code step and confirm the payload includes the fields you send to Dropcontact, such as first name, last name, and domain. Update field names if your query returns different keys.
10. In the Wait node, keep about ten minutes to allow processing. If your batches finish faster or slower, adjust this time after testing.
11. Open the Postgres Update node and map columns to your profiles table. For email, point to the first item in the email array. Confirm the schema and table names match your database.
12. Run a test with a small set. Temporarily change the batch size to 10 and execute. Verify that emails appear in PostgreSQL and that Slack posts any alert. If you see empty results, check domains in your source data and confirm your Dropcontact credits.