Make.com Automations

Running a high-volume AI content pipeline generates a constant stream of operational demands: documents need converting between formats, API usage needs tracking for cost control, server health needs monitoring, and design requests need routing — all without slowing down the editorial team. I built a set of Make.com automation scenarios that served as the backend infrastructure handling this workload, processing requests continuously with zero manual intervention.

Each scenario was webhook-driven and integrated with multiple external services, built to handle conditional routing, error states, and structured data logging autonomously. The result was a reliable operations layer that kept the content team focused on editorial work while infrastructure tasks ran silently in the background.

A key aspect of these scenarios was their seamless integration with HARPA AI, the primary automation platform used by the content team. It allowed us to expand on HARPA's capabilities beyond what was possible natively, by integrating it seamlessly with external services like Google Docs and Sheets via webhooks and APIs.

• Markdown ↔ Google Docs Pipeline — Two-way document conversion: receive Markdown via webhook and export to Google Docs (with branching router for different output formats), and convert GDoc URLs back to Markdown via Google Drive API.
• Anthropic API Logger — Webhook-triggered scenario logging structured Claude API usage data across multiple Google Sheets tables for usage tracking and cost analysis.
• Railway Server Monitor — Logged server request data from the Railway deployment to Google Sheets in real time.
• Image Design Prompt → Email — Received design prompts via webhook, dispatched via Gmail, polled for reply, and returned the email response to the caller.
• All scenarios ran live in production around the clock, processing real workflow data for the AI Research team.