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A company kept losing deals to its own quoting speed. Every request meant a manager opening the catalog, hunting for the right items among 12,000 positions, calculating the quote by hand, and formatting it. Often that took most of a day — and by the time the quote arrived, some clients had already moved on.
I connected their catalog to an n8n workflow. Now one request runs the whole thing: it finds the right items, pulls current prices, does the math, and returns a formatted quote ready to send. The manager reviews and sends instead of building it from scratch.
Close to a day became under 15 minutes.
The quote didn't get faster because someone rushed. It got faster because the slow part — the manual lookup and math between a request and an answer — stopped being manual.
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Built an n8n automation that recovers missed customer calls, starts an AI conversation, checks CRM context, routes urgent requests, and tracks callback SLAs until the customer is handled.
nice that it tracks the callback until someone actually handles it. which businesses is this built for, trades or more like clinics
Attention, Contra people🚨
Only proceed if you're in the mood for a laugh😅
A while ago, I was working with a client who needed lots of video ads every month. And by "lots," I mean 36 videos, 3 hooks each, 2 formats.
That's 216 files. Every. Single. Month.
Sounds like a lot, but we were ready.
THEN... things got interesting.
40 videos. 5 hooks each. 3 formats. Yep, SIX HUNDRED.
In 2 weeks.
And what did we tell the client? "Pff... two weeks? Easy."
Turns out, I was a little too optimistic🙃
This took us over a month. Not exactly our proudest moment.
But hey, that little disaster pushed us to rethink our workflow.
And the system we built? It eventually allowed us to deliver
150 files in just 7 calendar days.
Have you ever underestimated a project's timeline and regretted it later?
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The one I experienced was when a client gave me a project and provided a reference she wanted me to use as inspiration to build her website. I quoted her 10-14 days, but she said she needed it done in 5 days. I accepted it because I was desperate to get the project and I needed...
From 2.5 hours of research a week to 30 minutes of review.
Finding funding opportunities was a repetitive process: open a link, find the deadline and funding amount, check eligibility, and decide whether it was worth pursuing.
Around 5 minutes per opportunity. Across 30 opportunities a week, that adds up.
I automated the process using Make + OpenAI.
Here’s how it works:
Trigger: A user submits a link or screenshot.
Extract: The workflow fetches web pages and PDFs, or uses OCR for screenshots.
Process: Rules route each input to the right path.
Evaluate: OpenAI extracts key details and scores the opportunity against the user's profile.
Validate: Unreadable pages trigger a screenshot fallback. Invalid opportunities are rejected, and missing details are marked “not specified.”
The key design decision? Keep the AI in its lane.
Rules handle routing. AI handles extraction and fit scoring. Missing information stays missing instead of being guessed.
It’s not fully hands-off. Funding pages vary, and some results still need human review.
But the time spent went from roughly 2.5 hours to 30 minutes per week.
That’s what good automation should do: remove repetitive work without removing human judgment.
Want to see how the workflow is structured? Comment “wired” and I’ll share the workflow map.
#Automation #Make #OpenAI #LowCode
Love the decision to keep rules in control and use AI only where it adds value. Especially the part about not guessing missing information. That’s what makes automation reliable.