Since its founding in 1920, Occidental Petroleum (Oxy) has accumulated more than 12 million land-lease agreements, which govern the use of oil, gas, and minerals. “There are hundreds of people who use these documents every day,” said Alexander Lach, Artificial Intelligence (AI) Development Manager at Oxy. “It takes a lot of effort to find the information you need.”
Oxy had planned to hire 30 contractors to manually review a batch of 1.5 million documents, classify them, and extract pertinent information. But the company’s engineers thought they could significantly cut the project allocation of $4 million and 18 months by using a mix of cloud-based solutions that integrate with MongoDB Atlas.
Alexander Lach, Artificial Intelligence Development Manager at Oxy
The automated system that the company built using MongoDB Atlas saved Oxy $4 million and 12 months. The company plans to apply the architecture to other areas of the business, anticipating saving a further $3 million.
Engineers are experimenting with additional features of MongoDB. Using MongoDB Atlas Vector Search, they ran a proof of concept. This resulted in a retrieval time of less than 200 milliseconds as they searched more than 100 GB of text across millions of documents.
“I was just blown away,” said Pruet. “We’re excited for what MongoDB has to offer in the future.”
Andrew Pruet, Production Operations Engineer Adviser at Oxy