AI automation engineering showcase demonstrating a practical workflow pattern using APIs, webhooks, Python, validation, logging, and reliable integrations. Designed to turn repetitive processes into traceable, maintainable automation with error handling and human oversight.
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I built this owner gateway to handle event-driven notifications in a more controlled way.
Incoming events are validated, checked for duplicates, and logged before they are routed to the appropriate notification path. I also kept the Telegram and n8n integration behind a clear boundary so the workflow stays easier to review, test, and control.
The focus here is reliability and traceability — making sure an event is handled once, important actions can be audited, and automation does not bypass human oversight.
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I built this research lab to help me evaluate new tools and technical opportunities without jumping straight into adoption.
Each candidate goes through a simple process: record the source, review what it could be useful for, check the risks and constraints, and keep evidence for the decision. I also built a small Python policy gate and offline test fixtures so some checks can be repeated without relying on live services.
The goal is simple: experiment freely, but keep important decisions traceable and under human control.
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I built this operational workflow as part of INAPRA PMS, a system designed around the everyday work of running a small hospitality property.
This example follows the flow from a confirmed reservation through housekeeping operations and folio handling, with an operational audit trail to make important actions easier to trace.
I developed and tested this in a local UAT environment using fictional hotel data. My focus was not just making the screens work, but making the workflow clear, dependable, and practical for day-to-day operations.
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