Replacing 24+ fragile Zapier automations with one LLM-driven pipeline.
A multi-partner Amazon Vendor Central operation was held together by 24+ separate Zapier automations. We rebuilt the whole pipeline on AWS Lambda with an LLM handling classification and exception routing.
Client
Amazon Vendor Central operation
Partners synced
Wayfair, Walmart, Target, Faire, CommerceHub
Role
AI Engineer & Solution Architect
The operation was running 24+ separate Zapier automations just to keep compliance data, orders, and catalog information in sync across retail partners — Wayfair, Walmart, Target, Faire, CommerceHub, and direct SFTP feeds. Each automation was its own point of failure, and no one had a single place to see what needed attention when something broke.
How we approached it
Audit the opportunity
We mapped all 24+ existing automations, their data sources, and where they were failing or silently dropping exceptions.
Design the operating model
We designed a single pipeline architecture with an LLM sitting in the decisioning layer for classification and exception handling.
Ship fast, validate early
We migrated the pipeline off Zapier and Google Apps Script onto AWS Lambda, pulling compliance, order, and catalog data directly through Amazon's SP-API.
Launch with confidence
We put the system into production across multiple retail partners with dual sync to Airtable and BigQuery for visibility and reporting.
- 0124+ fragile automations rebuilt as a single Lambda-based pipeline
- 02LLM decisioning layer for classification, extraction, and exception routing
- 03Direct data pulls through Amazon SP-API
- 04Dual sync to Airtable (day-to-day visibility) and BigQuery (reporting and trend analysis)
Built on AWS Lambda with an LLM in the decisioning layer, Airtable, and BigQuery.
The pipeline now runs in production across multiple retail partners, replacing a patchwork of no-code automations with a system the team can actually debug and extend — ambiguous cases get routed for review instead of failing silently.
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