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
Back to feedPost
When people picture quoting, they picture the calculation. But the math was never the slow part here. The slow part was finding the right items in a catalog of 12,000 — scrolling, searching, second-guessing whether this is the current price or last quarter's.
So that's what I automated first. The workflow takes the request, matches it against the live catalog, and pulls the exact items and current prices before any calculation happens. The math is the easy last step once the right data is in front of you.
This is the part clients often get wrong when they ask for automation: they want to speed up the visible task, when the time is actually lost in the quiet lookup before it.
Find where the minutes actually go. It's rarely the step everyone's looking at.
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
Challenges
View all
Related posts
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
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.
Business owners: you get one AI automation free this month. Which one do you take?
A. AI support inbox. Every customer email gets a drafted reply in your tone. You read it, click approve, done. Nothing goes out without you.
B. AI lead qualifier. Every new lead is scored, summarised and dropped into your CRM with the next step, so you only call the ones worth calling.
Both run on n8n + OpenAI, connect to the tools you already use (Gmail, HubSpot, Slack, Sheets) and take me about a day to set up.
Vote below. Then comment with what is eating most of your week right now. The 3 most painful answers get it built for free and I will post the before and after here.
0 voted
1%
0 voted
1%
0 votes
Closed
The one day setup estimate holds only if your existing Gmail and HubSpot data is already clean. Most businesses spend longer fixing inconsistent tags or messy history before the AI can actually qualify leads accurately.