Structurizer Bot is a bot that can convert unstructured data, such as text or images, into structured data formats like tables. The bot uses advanced natural language processing algorithms to identify key data points and organize them in a structured format, making it easier for users to analyze and work with the information. This can save time and effort for teams who deal with large volumes of unstructured data, and improve the accuracy and consistency of data analysis.

One of the most frequent cases, when there is a need to convert unstructured text to a table, is when dealing with data extraction from sources such as PDF documents, web pages, or emails. These sources often contain unstructured data such as paragraphs of text making it difficult to extract and work with the data in a structured format. By using a bot, the data can be organized and processed more efficiently, enabling easier analysis. This is particularly useful in industries such as finance, healthcare, and legal where large volumes of unstructured data need to be processed on a regular basis.

In terms of common categories that can be treated as column names for the Structurizer bot, some examples include:

Dates and times: The bot can recognize and extract various date and time formats, such as "dd/mm/yyyy", "mm/dd/yyyy", "yyyy-mm-dd", and more. These can be treated as separate columns in the structured output.
Names and entities: The bot can recognize and extract names of people, organizations, locations, and more. These can be treated as separate columns in the structured output, depending on the specific use case.
Addresses: The bot can recognize and extract street addresses, cities, states, and ZIP codes. These can be treated as separate columns in the structured output.
Phone numbers: The bot can recognize and extract phone numbers, including area codes and country codes. These can be treated as separate columns in the structured output.
Prices and currencies: The bot can recognize and extract prices and currencies, including symbols such as dollar signs, pound signs, and euros. These can be treated as separate columns in the structured output.
Quantities: The bot can recognize and extract quantities, such as the number of items in a list or the amount of a particular product. These can be treated as separate columns in the structured output.

Anyone can start using this Bot free of charge, in case of any questions you can schedule a support team call using the Microsoft Teams Tab

Structurizer

by NLSQL