The first is a preventive maintenance and calibration tracker (https://pmcal.net) that was born out of my day job as an engineer in small business manufacturing.
The second is an AI engine for pulling structured data out of incoming email (either via IMAP on your email server or via SES). If you think of the engine that powers TripIt, they had to write about 10,000 different ingestors for each airline and hotel and travel booking site. With a structured output AI, the need to write specific ingestors goes away.
You can also get structured data out of mailboxes with my project EmailEngine. You can use an API request to fetch message contents, or you can configure EmailEngine to send a webhook for every new email in a structured JSON, for example, like this: https://emailengine.app/webhooks#messageNew
I don't think I was specific enough on what kind of structured data. The idea is that it extracts information from the text/HTML content of emails (e.g. a flight itinerary from an airline booking email or an ingredient list from a recipe) using AI.
Since you already have a method for reaching into folks Microsoft 365 inboxes and such, you could probably train an LLM to extract arbitrary data based on a user's prompt quite quickly though.
The first is a preventive maintenance and calibration tracker (https://pmcal.net) that was born out of my day job as an engineer in small business manufacturing.
The second is an AI engine for pulling structured data out of incoming email (either via IMAP on your email server or via SES). If you think of the engine that powers TripIt, they had to write about 10,000 different ingestors for each airline and hotel and travel booking site. With a structured output AI, the need to write specific ingestors goes away.