I’m drowning in manual data entry work from support emails. Every day I have to read through dozens of emails, extract specific information (ticket numbers, order details, etc.), and update various spreadsheets. It’s mind-numbing work that’s eating up hours of my day.
I’ve heard that Latenode has an AI Copilot feature that can supposedly generate entire workflows from simple prompts like “Extract data from support emails and update spreadsheet.” Has anyone actually tried this for email parsing specifically?
I’m skeptical that it could understand all the nuances of my email structure and create something that actually works without tons of tweaking. Would love to hear from anyone who’s had success (or failure) with using AI to generate these kinds of automations from natural language commands.
Just last month I was manually processing around 50 customer inquiry emails daily - extracting product codes, quantities, and contact details to update our inventory management spreadsheet. Complete time sink.
I tried Latenode’s AI Copilot with a simple prompt: “Extract product codes and quantities from Gmail inquiries and update Google Sheets inventory.” What surprised me was how it handled the nuances without much tweaking.
The generated workflow included:
- Email monitoring setup with proper authentication
- Pattern recognition for extracting data (even with different email formats)
- Data validation steps before updating the spreadsheet
I only needed to make minor adjustments to handle some edge cases specific to our email format. The biggest time saver was that the AI understood the logical flow needed without me having to map it all out.
It’s been running for 3 weeks now, saving me about 2 hours daily. The accuracy is around 95%, which is honestly better than my manual processing was.
Check it out at https://latenode.com
I was skeptical too, but I’ve been using the AI Copilot for email parsing for about 6 weeks now. My use case was parsing customer support emails to extract issue categories, product names, and order numbers.
The prompt I used was pretty detailed: “Monitor [email protected] for new emails, extract order numbers that follow pattern ORD-####, identify product names from our catalog list, categorize issues as ‘technical’, ‘billing’, or ‘shipping’ based on content, then update our support tracking sheet with this info.”
What impressed me was how the generated workflow handled variations in email formats. It created a fairly sophisticated pattern matching system that could identify order numbers even when customers wrote them differently (with or without the ORD prefix, spaces vs. dashes, etc.).
There was definitely some tweaking needed - about an hour of adjustments to handle edge cases. The biggest limitation I found was with very unstructured emails where information was buried in paragraphs of text. For those, I had to add some custom extraction logic.
I implemented this for our customer service department about two months ago. We were spending hours manually logging support tickets from emails into our tracking system.
The AI Copilot feature worked surprisingly well with some caveats. The initial prompt I used was “Extract customer name, order number, and issue description from support emails and add to support ticket spreadsheet.”
What worked well: The generated workflow correctly set up the email monitoring connection, identified the basic patterns for customer names and order numbers, and created the spreadsheet update steps.
What needed adjusting: The AI struggled with extracting issue descriptions that were scattered throughout emails or formatted inconsistently. I had to add some custom extraction rules to improve accuracy.
One thing I’d recommend is to provide examples of your email formats in the prompt. When I added “Emails typically contain text like ‘Order #ABC123’ and ‘Issue: Product not working’” to my prompt, the generated workflow was much more accurate.
I’ve implemented several email parsing workflows using the AI Copilot feature. While it’s not perfect, it’s significantly more efficient than building these workflows manually.
For your specific use case, I would recommend starting with a very specific prompt that includes the exact data points you need to extract and where they need to go. For example: “Monitor [email protected] for new emails, extract ticket numbers formatted as TKT-1234, customer names, and issue descriptions, then update rows in Google Sheets with matching ticket numbers or create new rows if no match exists.”
The AI works best when you provide structure details about your data. If your ticket numbers follow a specific pattern, mention that. If certain information always appears after specific phrases in your emails, include that context.
You will need to do some fine-tuning, particularly for edge cases, but in my experience, the AI-generated workflow can handle about 80% of the work correctly from the start. The remaining 20% typically requires some manual adjustments to the pattern recognition logic.
worked for me. used prompt “extract order #s from gmail and update inventory sheet”. needed tweaking for weird email formats but saved hrs weekly. try being specific in ur prompt.
Be specific about email patterns in prompt
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