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Internal ops automation for a US company — 90-person remote team, 10 locations (name withheld). Replaced manual reporting and status-chasing with automated, AI-summarized workflows.
I built: automated performance reports + overdue alerts from Asana; an AI bot that turns 90+ min/week of reading into a 3–5 min summary; and a dashboard-query bot answering in under 45 sec (was 2–4 min).
Results: ~13 hrs/week recovered · ~$30K/year in ops overhead eliminated · issues caught before they reach clients.
Stack: n8n · Asana API · Claude / OpenAI · Telegram Bot API
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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For this project, I was completely exhausted, my eyes hurting badly.
But I refused to leave it halfway.
The more I learn, the more I realize businesses don’t actually have an information problem.
They have a retrieval problem.
That’s the problem I stayed up all night thinking about in #n8n. 😪
I’m interested in building systems that actually make things run better with my automation engineering skills.
If you're interested in #AI, #automation, or just want to be part of my journey, hit the follow button and send a DM.
How AI Agents Help Business Operations
AI agents can act like digital teammates for repeatable workflows.
They can read documents, extract data, check missing details, update CRMs, send notifications, and prepare tasks for human review.
For example, when a client submits a form or document, an AI workflow can extract the key information, validate it, update the right tools, and notify the team automatically.
The goal is not to replace people. It is to remove repetitive work so teams can focus on better decisions.
The best AI automation keeps humans in the loop, adds error handling, and connects tools through APIs, webhooks, Slack, Google Sheets, CRMs, and workflow platforms like n8n.
Less manual work. Faster handoffs. Better operations.
AI is a great copilot, but it’s a terrible captain.
With AI coding assistants, it’s easy to fall into the trap of letting the LLM drive the entire development workflow. You prompt, copy, paste, and pray.
But our value as software engineers has never been about how fast we type syntax. It’s about system design, critical thinking, and code ownership.
Our role is evolving from builders to technical pilots. If you completely delegate the workflow, you lose control of the project.
Here is a quick framework to keep ownership of your features while leveraging AI:
🛠️ The 4-Step Workflow Guide
Design First, Prompt Second: Define your architecture, data flow, and edge cases before touching an LLM. Use AI to implement, not to invent.
Deconstruct: Break features into micro-steps. Guide the AI through isolated, high-context tasks (schema ➡️ logic ➡️ error handling).
Radical Skepticism: Treat AI code like a PR from an intern who is incredibly fast but occasionally hallucinates. Test and review every single line.
Own the "Why": If a bug hits production, "the AI wrote it" is not an option. You are responsible for the code you ship.
📌 To Developers: The future belongs to those who use AI to accelerate execution, but keep their eyes on system architecture and product sense.
📌 To Tech Recruiters: Stop looking for syntax experts. Look for developers with strong debugging frameworks, system-level thinking, and true project ownership.
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