Skip to content

GabrielAI Story

Emails are a fundamental part of every worker's day.

We receive too many emails each day!

I am building a new product to help people manage emails and communication better, saving time, effort, and stress!

GabrielAI allows creating a single summary of your unread emails so you can skim them all together instead of reading each one individually, saving hours each day.

It can help categorize your emails with the smartest email filters on the market, categorizing emails by their content, not by who sent them or by searching for specific words.

Finally, GabrielAI helps in replying to the most common emails, following your instructions with your data!

This is the story of how GabrielAI was born!

Helping my girlfriend with her students

My girlfriend is a university teacher, and as such, she receives a lot of emails every day.

Emails from students, emails from other researchers she collaborates with, and emails from the university staff she coordinates with.

Her email load is huge!

Despite the heavy load, she used to manage it rather well, until she got a new class of young students.

These new students started to bombard her with emails for the slightest issues. They kept asking the same questions over and over. Most annoyingly, the majority of their questions were already well explained in the public syllabus.

She started to burn out and, of course, she shared her challenges with me over one of our dates!

This was a problem I could help with! I was becoming quite proficient at using AI to accomplish interesting tasks. I immediately realized that I could use my skills to help her.

So GabrielAI was born to help her stay on top of her email game!

GabrielAI has three main features:

  1. Email summaries
  2. Smart filtering
  3. Automatically drafted replies

Email summaries

The idea behind email summaries is to save time on reading and absorbing information from your emails.

Instead of opening each email, she now gets a summary email every 6 hours.

In the summary, there is enough information to decide if it is worth opening and reading the email or not.

This saves her an hour each day. Instead of opening and reading 10 different emails every hour, she now reads one summary email and gets all the information she needs.

It is much faster to skim emails. She already knows what those emails are going to say, and she just doesn't waste time anymore.

It easily saves her one hour each day!

Smart filtering

Another innovation from GabrielAI is smart filtering.

Everyone who heavily uses email must eventually implement some filtering rules.

The problem is that those rules are very hard to get right, and they are very limited.

This is because most email rules are based on mechanical recognition of words.

Email filters usually check if the email contains a specific word or if the sender is a specific email address.

We can do better!

GabrielAI allows filtering emails based on their topics.

She now has a filter to put all the students' emails in a specific folder, all the emails related to a specific research project in another folder, and all the emails about her university duties in another folder.

Automatically drafted replies

The last innovation from GabrielAI is automatically drafting replies.

Remember when she was about to burn out because her students kept asking the same questions over and over again, and all those questions were already answered in the syllabus?

This is how she avoided burning out!

GabrielAI automatically detects the emails it can answer and creates a draft.

My girlfriend would just read the email, read the draft created by GabrielAI, sometimes adjust a few details, and simply send the email.

The amount of stress she saved was literally visible during our dates!

Help me help you

If you've read this far, you likely have the same issues as my girlfriend. Too many emails.

I want to help you!

But I need your help!

The help I need is for you to try out our app.

Just try it out and let me know what you think.

All you need to do is click on this link: