Staffing for Sales AI Agent Application

Client goals The company’s co-founder - Schlumberger and Shell alum got curious about data, analytics, and machine learning during the major downfall of oil prices in November 2014. It led him to ask questions about why it was happening and can it be predicted in a better way. He wanted to simplify complex industrial knowledge that was easy to access. He and his colleagues have a long-term goal to create an ecosystem of workflows around the smart assistant so that everyone in an organization can ask the tough questions and get back the answers without any delay.

Problem - Manual and Slow Data Extraction The smart assistant was using Natural Language Processing to integrate with multiple systems used by a company to unstructured data from documents, emails, and much more. The then-current version of the search engine had manual data extraction which was extremely time-consuming. They were using a third-party API to extract the financial data.

Our Collaboration Our team has been a technology partner of the energy tech startup under the staff augmentation model since September 2020. Results Response time has been reduced drastically and the UI/UX is better in the new version. With the new infrastructure development, the user is now able to ask questions based on suggested, and typeahead questions. The user can also see financial information about his peers and compare them. And filter and sort responses on the basis of different tags.