Souvik Dutta
Business Operations & Workflow Automation
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Just finished the final version of my ๐๐๐ ๐๐ฉ๐๐ซ๐๐ญ๐ข๐จ๐ง๐ฌ & ๐๐ฎ๐ฌ๐ญ๐จ๐ฆ๐๐ซ ๐๐ข๐๐๐๐ฒ๐๐ฅ๐ ๐๐ฎ๐ญ๐จ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐ฒ๐ฌ๐ญ๐๐ฆ.
The goal was to automate everything that happens after a lead enters the systemโfrom lead capture and qualification to follow-ups, customer segmentation, and reportingโwhile keeping all customer data synchronized in one place.
๐๐ก๐๐ญ ๐ข๐ญ ๐๐จ๐๐ฌ:
Captures leads from multiple sources
Enriches and validates customer data
Segments leads based on business rules
Automates personalized email sequences
Sends Slack notifications for important events
Tracks customer engagement automatically
Maintains a centralized PostgreSQL CRM database
Provides operational dashboards through Metabase
Built using n8n, PostgreSQL, Metabase, REST APIs, AI, and Docker on a self-hosted cloud VPS.
The focus wasn't just building the automation. It was designing a CRM that keeps customer data synchronized, reduces manual work, and gives teams complete visibility into their sales operations.
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Just wrapped up the final version of my ๐๐ฎ๐ฅ๐ญ๐ข-๐๐ก๐๐ง๐ง๐๐ฅ ๐๐ง๐ฏ๐๐ง๐ญ๐จ๐ซ๐ฒ ๐๐๐๐จ๐ง๐๐ข๐ฅ๐ข๐๐ญ๐ข๐จ๐ง ๐๐ฒ๐ฌ๐ญ๐๐ฆ.
The automation was done, but the dashboard didn't meet the standard I had in mind, so I redesigned it from scratch. The second iteration is much cleaner and focuses on what actually matters: visibility into inventory mismatches across sales channels.
๐๐ก๐๐ญ ๐ข๐ญ ๐๐จ๐๐ฌ:
Synchronizes inventory across multiple marketplaces
Compares channel inventory against the master inventory
Detects discrepancies automatically
Logs every reconciliation for auditing
Sends Slack alerts when mismatches are detected
Provides a centralized audit dashboard for monitoring
Built using n8n, PostgreSQL, and Docker on a self-hosted cloud VPS.
Sometimes the automation is the easy part. Building a dashboard people can understand at a glance takes even more iteration.
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Just finished building an ๐๐๐๐ผ๐บ๐ฎ๐๐ฒ๐ฑ ๐๐จ๐ฌ๐ญ-๐๐ฎ๐ซ๐๐ก๐๐ฌ๐ ๐๐๐๐ซ๐๐ฌ๐ฌ ๐๐๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐ฒ๐ฌ๐ญ๐๐ฆ for eCommerce.
A surprising number of shipping issues start with incomplete or inaccurate customer addresses. Instead of letting bad orders reach the warehouse, this workflow validates every shipping address immediately after payment and decides whether the order is ready for fulfillment or needs manual review.
I built this workflow using the Shopify API, but the workflow architecture can be adapted to virtually any eCommerce platform. My goal is to make the concepts and implementation accessible so anyone can learn from them and build their own version.
๐๐ก๐๐ญ ๐ข๐ญ ๐๐จ๐๐ฌ:
ย โข Triggers automatically when a Shopify order is paid.
ย โข Validates shipping addresses using the Google Maps Address Validation API.
ย โข Assigns a confidence score and validation status.
ย โข Stores validation logs and address history in PostgreSQL.
ย โข Routes valid orders directly to the warehouse.
ย โข Sends Slack alerts for orders requiring manual review.
ย โข Provides a centralized dashboard for tracking validation results.
Built using n8n, Shopify, Google Maps API, PostgreSQL, Slack, and Docker on a self-hosted VPS.
One part I particularly enjoyed building was the decision logic. Not every address should be handled the same wayโsome can be corrected automatically, while others genuinely require human review. Finding the right balance between automation and manual intervention makes the workflow far more reliable.
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