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I designed an automated client inquiry management system that streamlines the process of collecting, organizing, and tracking client requests.
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My recent client is an Australian printing firm.
They already had clients coming in.
The problem was what happened after the agreement.
When a client agrees to payment terms, every delay matters.
A payment process that sits waiting for someone to follow up can slow down the entire client journey. And when clients experience unnecessary delays, it can affect more than cash flow. It can affect their confidence in the business.
So I built an Agreed Payment System in Make.com designed around the client journey.
The goal wasn’t simply to automate emails.
It was to create a process that keeps agreed payments moving, keeps communication consistent, and reduces the amount of manual follow-up required from the team.
The workflow takes the agreed payment process and routes it through the appropriate steps, conditions, and Gmail communications.
The system helps keep the process moving.
And this is where my background in sales development and client retention comes into the automation work.
I don’t look at automation as:
Trigger → Action → Done.
I look at it as:
Lead → Sale → Client → Experience → Retention → Repeat business.
Because getting a client is only part of the job.
You also need systems that make it easy for that client to keep doing business with you.
Sales development + client retention + workflow automation.
That’s the intersection I build around.
The handoff after a client agrees to terms is where many service businesses leak cash flow. Automating follow-up there is a practical B2B wedge because it ties the workflow directly to revenue.
Built an AI-powered Log Analyzer using Python and FastAPI to automate log analysis and troubleshooting. The system processes application/system logs, performs rule-based analysis, and uses an LLM to identify problems, root causes, severity, possible causes, and recommended actions. Exposed the functionality through REST APIs with structured Pydantic responses for reliable and consistent results.
Tech: Python, FastAPI, LLM/Ollama, Pydantic, REST APIs, Log Parsing, Prompt Engineering, Git.
I built an AI-powered n8n automation that generates personalized ice breakers for cold emails.
It takes a company’s website URL, analyzes the business, extracts key information, and uses AI to create a unique opening line for outreach.
Instead of manually researching every prospect, this workflow helps personalize cold emails at scale while saving hours of repetitive work.
Built with n8n + AI + web data extraction.
Strong use case. The quality control matters as much as the generation: I’d keep the source snippet and URL beside every ice breaker, then use a fallback when the evidence is thin instead of letting the model infer. Have you added a similarity check so repeat prospects don’t receive nearly identical openings?