Orby’s patented technology combines a multimodal large action model

Orby AI, a startup in the realm of enterprise automation, has secured a $30 mn funding in Series A round co-led by New Enterprise Associates (NEA), Wing VC and WndrCo with additional participation from Pear VC to propel its mission of streamlining mundane processes through the power of generative artificial intelligence.

The company aims to tackle the complexities of automation in business tasks that go beyond the capabilities of traditional rules-based systems.

Orby’s platform automates the mundane so that organizations can focus on their most strategic and impactful work.

startup Orby AI secured $30 mn funding in Series A

The new investments will be used to accelerate the development and commercialization of the industry’s first generative AI process automation platform powered by a sophisticated large action model (LAM). The Series A financing follows a seed funding round co-led by NEA and Pear VC.

At WndrCo, we believe that AI agents will fundamentally transform the future of work

Jeffrey Katzenberg, Founding Partner of WndrCo

Most of today’s businesses have essential activities, such as data entry, document processing and validation of complex forms, invoices, expenses and contracts, that can be fully automated.

However, many organizations don’t have a clear technical understanding of what is feasible. This forces them to waste time and money discovering what workflows are best to automate first.

Unfortunately, traditional automation solutions lack the necessary AI sophistication and remain too costly and technically challenging to automate more complex repetitive work beyond simple rules-based automation.

Bella Liu, Co-Founder and CEO of Orby AI

Yesterday’s process automation solutions were never designed to address the dynamic and complex nature of work being performed within the enterprise

Bella Liu, Co-Founder and CEO of Orby AI

Orby AI has developed the first self-service, end-to-end generative AI platform to meet these modern automation goals. By removing complexity and creating a unique workflow discovery, Orby’s generative AI platform seamlessly builds automations for virtually any business workflow.

“Orby’s approach and large action model technology overcomes key challenges of traditional process automation, simplifying workflow definition for business users,” said Dan Twing, President and Principal Analyst for Intelligent Automation at Enterprise Management Associates.

With neuro symbolic programming, their platform captures standard process flows and ensures robust exception handling to provide a resilient automation solution that now makes AI-driven automation accessible and highly efficient for the enterprise.

Orby’s patented technology surpasses legacy automation solutions by integrating a multimodal large action model (LAM) foundation with an AI agent.

This agent uses neurosymbolic programming, combining symbolic reasoning with neural network-based analysis, to rapidly create accurate, actionable workflow automations that learn, adapt, and improve over time. Workers can now automate tasks independently without needing technical assistance.

Orby’s platform uniquely observes, learns, automates, and adapts to repetitive workflows across various enterprise processes, outperforming legacy rules-based systems.

It reduces automation time from months to minutes and handles tasks requiring decision-making and domain knowledge that traditional methods cannot manage.

Generative Process Automation (GPA) marks a new era in business process automation. Also known as “agentic process automation (APA),” this approach merges AI’s cognitive abilities with automation benefits, allowing systems to execute tasks involving complex planning, reasoning, and adaptation.

In this framework, an AI agent receives instructions and autonomously generates workflows, incorporating specialized agents for sub-tasks like data analysis or customer interaction. This method reduces repetitive cognitive labor for humans and enhances process capabilities to manage complex, variable scenarios autonomously.

Peter Sonner   by Peter Sonner