Multi-Channel AI Workflow Automation System
Event-driven business pipeline uniting webhooks, AI document parsing, and instant notifications
Interactive Architecture & Pipeline Inspector
Event-Driven n8n Multi-Channel Pipeline
Resilient asynchronous webhook ingestion, LLM document extraction, and transactional dispatch
Inbound Webhook Receiver
Role: Multi-Source IntakeAccepts incoming form leads, document uploads, and billing events from HubSpot, Typeform, and Stripe.
Operations teams spend dozens of hours weekly manually copying lead inquiries, sorting attachments, extracting invoice parameters, and manually notifying account executives.
Architected an end-to-end event-driven n8n automation pipeline that captures multi-source webhooks, parses unstructured incoming emails and PDF attachments via LLM function calling, writes structured records to PostgreSQL, and dispatches role-tailored alerts.
Mapped webhook payload normalization logic, built custom JavaScript processing nodes in n8n, configured retries with exponential backoff, and implemented secure credential isolation.
Architectural Breakdown
- 1Inbound webhook gateway receiving payload events from CRM forms and email forwarding hooks
- 2Data sanitation and deduplication stage with Redis/PostgreSQL idempotency checks
- 3LLM extraction node using structured JSON schemas to parse document line items
- 4Conditional routing switch delivering high-priority leads to Slack and archiving routine data
- 5Automated DLQ (Dead Letter Queue) alerting on webhook schema drift or upstream API failures
Key Engineering Decisions & Rationale
Self-hosted n8n instance
Ensured full data residency, zero per-execution pricing penalties, and complete control over execution logs and custom Node.js script nodes.
Idempotency keys on incoming payloads
Webhooks frequently deliver duplicate deliveries on network timeouts. Hashing headers and timestamps prevented duplicate database insertions.
Challenges & Trade-offs
- •Accepted higher upfront workflow design overhead for strict error handling and DLQs to prevent silent pipeline drops.
What I Learned
- ✓Never rely on LLM outputs without a downstream schema validator (like Zod or JSON Schema) before database writes.
- ✓Webhook consumers must respond with 200 OK immediately and process heavy AI tasks asynchronously.
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