Event Research Assistant - Stop Searching Manually

Event Research Assistant - Stop Searching Manually

If you've ever researched online events to attend so you can network or potentially find clients and partners, you know it can be a super daunting and manual procedure that takes a lot of time. But I've just created a super cool automation using a custom GPT to handle this task. We pull all the details for each event using Make Automation and place them into a neatly designed Airtable database. Let me switch my screen and show you exactly how it's done.

We start with a custom GPT, instructing it to find conferences. Initially, I’ve fine-tuned it to find just two conferences for demonstration purposes. We ask for specific details: name of the conference, industry, date, link, city, and a description. If done in real-time, it starts automatically, but I’m going to manually prompt it this time.

In the meantime, I open my Make Automation tool and click on 'run once' to wait for new data. This ensures that the automation triggers as soon as I confirm the action. Now, let me show you how everything works first, and then I'll go through each module to explain the setup.

Clicking confirm, the details are sent through Chat GPT, and you’ll see the output. What we get is a neatly separated list of conference details: name, industry, date, link, city, and description. The automation ensures that text doesn't get clumped but is separated into appropriate cells in Airtable.

Here's how everything is set up for those interested in grabbing this blueprint. Make sure you follow the link under this video and join my school community for access to every single blueprint and template for custom GPTs and Airtables.

Steps:

1. Connect Custom GPT: Pass the information to Make Automation using a webhook set up in the back end of our GPT.
2. Text Parsing: Information initially comes as big chunks of text. Use a text parser with a specific pattern to separate text.
3. Iterator: Separate each entry individually.
4. Chat GPT Completion Module: Instruct GPT to separate text into individual elements using JSON format.
5. Delay Tool: Sometimes information may be large, so include a delay (e.g., two seconds) to manage the flow of data.
6. JSON Parsing: Continue passing JSON results to open AI and prepare them for Airtable.
7. Map to Airtable: Every column from the JSON parser is mapped to corresponding Airtable columns.

This is a fast and efficient way to find every event in your area, state, country, or internationally. For this and other blueprints, join my AI Business Builders Community. I'll provide the link in the description of the video, and I look forward to seeing you there!

Keywords

• Online Events
• Network
• Custom GPT
• Make Automation
• Airtable
• Conferences
• Event Details
• Text Parsing
• JSON
• AI Business Builders Community

FAQ

Q1: What is the main benefit of using this automation for event research? A1: The primary benefit is that it simplifies and speeds up the process of finding events. The automation handles the manual searching, making it less time-consuming and more efficient.

Q2: What kind of information does the custom GPT pull about each event? A2: It pulls the conference name, industry, date, link, city, and a description of the event.

Q3: Why do we need to parse the text that comes from GPT? A3: The text initially comes in big, unstructured chunks. Parsing helps separate it into individual elements that can be easily mapped into Airtable cells.

Q4: What is the purpose of the delay tool in the automation process? A4: The delay tool helps manage the flow of large amounts of data, ensuring that everything gets processed correctly without overwhelming the system.

Q5: How can I access the blueprint for this automation setup? A5: You can join the AI Business Builders Community through the link provided in the description of the video. The community gives access to all blueprints and templates for custom GPTs and Airtables.

Q6: Can the automation handle large amounts of data? A6: Yes, the automation is designed to handle large datasets effectively, with tools like the delay mechanism in place to manage flow.