The problem we solve is profiling tables: this is the initial step where you need to understand the table and identify any anomalies.

During the process, many small decisions require semantic understanding. For example, missing values are normal for 'deathdate' (still alive) but abnormal for 'name.' For outliers, 100 for ages is fine, but some are -1, which is impossible! We use LLMs to semantically understand your tables and detect anomalies.

You can try it by uploading a CSV, and we will email back the profile: https://cocoon-data-transformation.github.io/page/

Let me know your feedback. Thanks!