Numerous.ai has an =INFER() function that learns patterns from examples and I use it for messy client data

Most coverage of Numerous.ai focuses on the =AI() function for generating text from prompts. I want to write about =INFER() because it solves a different and in some ways more useful problem for data work.

=INFER() lets you teach the AI a classification or transformation pattern by showing it examples rather than describing it in a prompt. You provide a column of input values and a column of your correct outputs for a handful of rows. The function learns the rule from those examples and applies it to the rest of the dataset.

I use this for client data that arrives in inconsistent formats. Product category labels that have been entered by different people with different conventions over years. Company name variations that need standardizing. Address fields where the format was never enforced. Describing the standardization rule in a prompt is often harder than just showing five examples of what input should map to what output and letting =INFER() generalize from there.

The practical difference from =AI() is that I do not need to be able to articulate the rule. I just need to know what the right answer looks like for a few cases and the model figures out the rest.

The Data Cleaning and Extraction functions handle the structural issues alongside this. Extracting specific fields from unstructured text, reformatting dates, pulling product names from messy descriptions, all run as cell functions across the full dataset.

Custom Templates for recurring data cleaning projects mean I set up the workflow once and reuse it when the same client sends another export in the same format.