A freight company was struggling to effectively utilize its Bill of Lading (BoL) data. The company couldn’t gather valuable insights, identify profitable shippers, or quickly detect risky shippers. It also faced a lack of standardization in assigning BoLs to agents. By implementing Dataturf.ai, the client could gather quality BoL insights through gamification and conduct scenario analysis to maximize revenue.

The company also used our BoL recommender for agents, identified risky billing patterns, and merged data analysis for Account Receivables. Additionally,it provided an end-to-end BoL management system. As a result, the company improved its predictive analytics, achieved greater visibility and operational excellence, and made better data-backed decisions.

A banking and financial services client was facing challenges with its Anti-Money Laundering (AML) screening process. The company was spending excessive time manually searching through multiple databases to identify fraudulent entities and linking data and entities. With Dataturf.ai, the client automated the process of information scraping from disparate data sources, generated relevance scores to establish entity relationships, and created knowledge graphs among configured financial crime entities. The client saw a reduction in turnaround time and improved decision-making with automated link identification.

A client in the vehicle auction industry was struggling to gain actionable insights from its auction processes owing to multiple siloed systems and no single source of truth for its data. By implementing Dataturf.ai, we were able to facilitate seamless data collection from various sources, allowing the client to manage operations in one place. We also automated the ‘operations handshake’ and created a knowledge base for front-end and back-end operations. This enabled the client to generate insights by comparing auctions and associated data sets and provided them with an integrated analytics module for real-time data insights. Consequently, the client saw an improvement in its CSAT and generated ancillary sales by utilizing data from multiple sources.