Making ML as easy as SQL — introducing the predictive database

{
"from": "engagements",
"where": {
"customer": "john.smith@gmail.com"
},
"recommend": "product",
"goal" : "purchase"
}
The workflow of a typical ML modeling project

The predictive database

{
"from": "purchases",
"where": {
"customer": "john.smith@gmail.com"
}
}
{
"from": "purchases",
"where": {
"customer": "john.smith@gmail.com"
},
"predict": "productIds",
"exclusiveness": false
}
{
"from": "impressions",
"where": {
"customer": "john.smith@gmail.com",
"product.text": {
"$match": "milk"
}
},
"recommend": "product",
"goal": { "click": true }
}
The grocery store demo using the AI queries

The impact

  1. Add the AI functionality into the internal tools, PoCs, the MVPs and the smaller products.
  2. Add the numerous smaller AI functionalities, like the little things that help ease the users’ lives.
  3. Make the software thoroughly smart and include all the smart functionality from the AI features’ buffet.

Meet Aito

The workflow with the Aito AI queries

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Aito.ai decision automation in the cloud. #ML for #nocode and #rpa operators.

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aito.ai

aito.ai

Aito.ai decision automation in the cloud. #ML for #nocode and #rpa operators.

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