# aito.ai tutorials

## Aito.ai Tutorials

- [Get Started](https://docs.aito.ai/readme.md): aito.ai brings AI capabalities to no-coders working on any data or any workflow.
- [What is aito.ai?](https://docs.aito.ai/the-basics/what-is.md): So what exactly is this thing we are talking about here?
- [Instances](https://docs.aito.ai/the-basics/instances.md)
- [Datasets](https://docs.aito.ai/the-basics/datasets.md): They are called "Tables" in Aito.
- [Predictions](https://docs.aito.ai/the-basics/predictions.md): Predictions are what generate the value. The fun part!
- [Evaluating accuracy](https://docs.aito.ai/the-basics/evaluating-accuracy.md): We have built in no-code way to evaluate if your data is good enough for high quality predictions.
- [Airtable](https://docs.aito.ai/integrations/airtable.md): aito.ai Instant Predictions extension brings machine learning to every no-coder right inside the Airtable base.
- [Installing the extension](https://docs.aito.ai/integrations/airtable/installing-the-extension.md): Setting up aito.ai for Airtable is quick. You'll need an aito.ai account, and then connect it with the Airtable extension.
- [Syncing data](https://docs.aito.ai/integrations/airtable/syncing-data.md): Keeping data up to date between Airtable and aito.ai ensures you always predict from the latest dataset.
- [Predictions](https://docs.aito.ai/integrations/airtable/predictions.md): aito.ai extension is a great companion when adding and working with data, as it can auto-fill column values based on predictions.
- [Similiarity](https://docs.aito.ai/integrations/airtable/similiarity.md): Uncover hidden links in your data and get suggestions for similiar records.
- [Insights](https://docs.aito.ai/integrations/airtable/insights.md): aito.ai's magic is to uncover statistical links across your entire base. Uncover things like "what is typical for my best paying customers" without writing any code.
- [Using automation scripts](https://docs.aito.ai/integrations/airtable/using-automation-scripts.md)
- [FAQ](https://docs.aito.ai/integrations/airtable/faq.md)
- [Integromat / MAKE](https://docs.aito.ai/integrations/integromat-make.md)
- [Zapier](https://docs.aito.ai/integrations/zapier.md)
- [Parabola.io](https://docs.aito.ai/integrations/parabola.io.md)
- [Robocorp](https://docs.aito.ai/integrations/robocorp.md)
- [UiPath](https://docs.aito.ai/integrations/uipath.md)
- [Automation Anywhere](https://docs.aito.ai/integrations/automation-anywhere.md)
- [Blue Prism](https://docs.aito.ai/integrations/blue-prism.md)
- [Power Automate](https://docs.aito.ai/integrations/power-automate.md)
- [TagUI](https://docs.aito.ai/integrations/tagui.md)
- [Airtable script to fill in missing data](https://docs.aito.ai/solution-examples/airtable-scripts-predict.md): This example shows you how to add a scheduled automation that fills in missing data using aito.ai predictions.
- [Labeling support tickets with Robocorp tools](https://docs.aito.ai/solution-examples/labeling-support-tickets-with-robocorp-tools.md)
- [Purchase invoice automation with Robocorp - video tutorial](https://docs.aito.ai/solution-examples/purchase-invoice-automation-with-robocorp-video-tutorial.md)
- [Purchase invoice automation with UiPath - video tutorial](https://docs.aito.ai/solution-examples/purchase-invoice-automation-with-uipath-video-tutorial.md)
- [Freshdesk ticket triage with Integromat - video tutorial](https://docs.aito.ai/solution-examples/freshdesk-ticket-triage-with-integromat-video-tutorial.md)
- [Contacting us](https://docs.aito.ai/getting-help/contacting-us.md)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on a page URL with the `ask` query parameter:
```
GET https://docs.aito.ai/readme.md?ask=<question>
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
