Aito.ai - Introducing a new database category - the predictive databaseIntroducing a new database category - the predictive database<br>#PredictiveDatabase<br>#StructuredData<br>#MachineLearning<br>#NoTrainingStep
Photo by Jan Antonin Kolar
Antti Rauhala<br>Co-founder<br>April 25, 2026 • 9 min read
(Originally published August 2019. Revisited April 2026.)
Could machine learning on structured data be made radically more accessible and faster to use? How about, if you could query the predictions, the recommendations and other AI / ML functionality, with no training step, with queries like this:
"from": "engagements",<br>"where": {<br>"customer": "john.smith@gmail.com"<br>},<br>"recommend": "product",<br>"goal" : "purchase"
In a typical tech environment it is extremely easy to find applications for predictions on structured data. The end users have gotten used to machine learning-driven features like recommendations, and such features can deliver huge business benefits. There is simply an abundance of ideas and desire to bring predictions to software.
Yet, while machine learning is huge, it can be inaccessible for the average development team. Not every development team is armed with a data scientist. Even more, the traditional model training process is often prohibitively expensive. The image below depicts one way to frame a typical machine learning project. What the picture omits is, that the process can take weeks or months, it can cost tens or hundreds of thousands of euros to get through, and the results are not always what was expected.
Traditional ML project workflow: weeks to months per model, multiplied by the number of prediction targets
Few product teams can spare an 'extra two months' to try a predictive feature that might improve the product. As a consequence most, maybe over 90%, of the value-adding predictive functionality is not feasible to even attempt.
Given all this, the true question is that could predictions on structured data be made radically more accessible, with no model training step at all ?
The Predictive Database
To truly understand the significance of the predictive database, let’s consider the following scenario.
You have a database that provides you the normal database operations for your grocery store. You can use the database to list the historical customer purchases like this:
"from": "purchases",<br>"where": {<br>"customer": "john.smith@gmail.com"
Now, your PO has done some interviews and found the customers complaining that filling the weekly shopping list is a huge hassle. What would you do? Perhaps you could use the following query for predicting the customer’s next purchases:
"from": "purchases",<br>"where": {<br>"customer": "john.smith@gmail.com"<br>},<br>"predict": "productIds",<br>"exclusiveness": false
The query results will list the customer’s next purchases by the purchase probabilities. You can use it to prefill the shopping basket with the weekly butters and milks. It can also be used to provide recommendations.
Cart Autofill in the grocery store demo. One click prefills the basket with the customer's predicted weekly purchases. Try it at demo.aito.ai.
But there is even more. How about, if you have some impression and click data, and your PO, your customers and the team itself desire the personalized search? Let’s try the following query to recommend ‘milk’ related products:
"from": "impressions",<br>"where": {<br>"customer": "john.smith@gmail.com",<br>"product.text": {<br>"$match": "milk"<br>},<br>"recommend": "product",<br>"goal": { "click": true }
The query returns the products containing the word ‘milk’ by the probability the customer might click it. If the customer is lactose-intolerant, the lactose-free products will be listed first. As such, the query seamlessly combines the soft statistical reasoning with the hard text search operation to get the sought results.
Personalized search in the grocery store demo. Lactose-free products ranked first based on purchase history. Try it live at demo.aito.ai.
You can try the personalized search and other predictive queries live at demo.aito.ai.
There is a wide array of other prediction problems on structured data that can be quickly solved with queries. For example, you can form simple queries to propose tags for products, to propose personalized query words, match email-lines with the products in the database or explain the customer purchases and behavior. As such, these simple queries can provide the intelligent user experience, the process automation and analytics.
Four categories of predictive queries running against the same data: personalization, automation, conversational AI, analytics. Each tile in the demo is a single query. See them at demo.aito.ai.
The Technology
While the previous examples may sound visionary, we have succeeded in realizing the vision. It occurs, that by integrating columnar inference deeply in a database optimized for structured data, it is possible to optimize the model building to a level, where you can create lazy statistical...