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Matatika accelerates into 2024
2023 has been a transformational year for Matatika and the entire data industry. It is no longer acceptable to be making decisions on untrusted, days old data. Matatika, which means right, straight, ethical, fair, honest, impartial, unbiased, upright, and moral in Maori, was founded on the idea that trusted data will be necessary for all data led decisions. Not sometime in the future, that time is now.
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Product Update: October
Is your data team stretched to breaking point? We meet data teams just like yours all the time. A day without pipeline errors is worth celebrating. Spending hours moving changes from one place to another is normal. Working on the actual data and helping their business counterparts analyze business opportunities is a distant dream. Read on to learn more about modern dataops with Matatika’s latest features to help you get more done, be more agile, and create trusted data!
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Product Update: July
Longer days mean we get more done! Right? Too often we find the opposite to be true - data teams are stretching and straining to gather the data the business demands, but are producing fewer insights, facing more data quality issues, and are fully occupied just keeping their data pipelines running. Read on to learn more about Matatika’s latest features to help you get more done and cost you less!
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Product Update: May
The force was with us this month as we broke ground on a host of AI-powered innovations for our clients. These features both large and small are now available in our cloud, in our free Community Edition, and hosted on our client's Azure, AWS, and GCP cloud. Read on to see how our data platform uses AI to extract structured information from documents, connects to Google APIs with custom OAuth clients, and has enabled inventory insights in Veeqo. Plus, you may have seen us on UK Parliament TV this month!
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Technology: Using GPT-3 large language model to extract structured information from documents
In this article, we explore the use of the GPT-3 large language model to extract data from documents for a leading Private Equity Co. In plain English: We used AI to read a document and put the answers into a spreadsheet. Millions of documents can be read at the same cost as one human. Complex questions can be asked with better-than-human performance reading comprehension. Extracting answers to questions into a spreadsheet enables analytics across document dimensions, time for example. Other potential use cases could include better-than-human performance on data entry, document text extraction, and screen scraping (which requires a human to pinpoint the required data).
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