Beyond Work · Wed Jan 22 2025
A global biopharmaceutical leader, with operations in over 100 countries and more than EUR35 billion in annual revenue, accelerated its sustainability reporting by applying Beyond Work's adaptive AI platform in collaboration with Accenture. Within weeks, the organization refined document extraction, instantly adjusted processes based on human feedback, and approached 95% accuracy - ultimately saving over 20,000 hours of manual work.
A global biopharmaceutical leader, operating in over 100 countries and generating more than EUR35 billion in annual revenue, required an efficient approach to meet evolving sustainability reporting mandates. Working with Accenture, this organization leveraged Beyond Work's unique workblock architecture and adaptive learning capabilities to transform an intensive, error-prone manual process into a streamlined, AI-driven workflow.
Within weeks, the platform's modular, iterative design structured previously unharmonized documents from hundreds of suppliers, extracting critical product attributes and enabling teams to validate data more efficiently. As soon as human validators provided targeted feedback, the platform learned and adjusted instantly, refining its extraction patterns and bolstering accuracy. This iterative improvement cycle allowed the system to rapidly approach approximately 95% accuracy, drastically reducing manual effort. By the end of the three-month period, the company extracted more than 100,000 attributes, saving over 20,000 hours that would otherwise have been spent on manual data processing. Freed from repetitive tasks, compliance teams dedicated more time to analysis and strategic decision-making, resulting in a highly efficient and reliable sustainability reporting framework.
As future regulations and stakeholder expectations evolve, the platform remains ready to adapt and integrate new data points without extensive manual reconfiguration. This flexible, scalable foundation ensures that the company can continue to maintain rigorous reporting standards, efficiently incorporate new sustainability criteria, and remain responsive to the global market's shifting demands.
As one of the world's largest logistics companies with tens of thousands of employees operating in over 100 countries, Kuehne+Nagel faced mountains of busywork every month. Beyond Work's approach to enterprise work outcomes eliminated the most tedious tasks in a matter of weeks - slashing invoice review time by a factor of ten and cutting cash allocation from hours to minutes - freeing people to focus on what truly matters.
Our latest research unveils MATRIX (Memory-Augmented agent Training through Reasoning and Iterative eXploration), setting a new standard in enterprise LLM reasoning. Partnering with Kuehne+Nagel (link to use-case) and researchers from Oregon State University and Penn State University, including Assistant Professor Qingyun Wu - coauthor of AG2 (previously known as Autogen) - we introduced the first open-source agent benchmark for UBL-format invoices. MATRIX outperforms direct LLM prompting and all existing reasoning benchmarks, leveraging iterative, memory-driven refinement to boost accuracy, cut resource usage, and scale effortlessly for complex, real-world workflows.
Managing thousands of vendors across international markets, one of the world's largest producers in the energy and chemical sector rapidly transformed its complex onboarding process using Beyond Work's AI-driven platform and Accenture's support. By eliminating manual back-and-forth and consolidating data review into a single interface, this industry leader cut onboarding times by more than half, reduced manual corrections by 40%, and streamlined process steps by 30%. These measurable gains free teams to focus on higher-value activities - ensuring an efficient, resilient, and scalable operation.