AiThority Interview with Rich Waldron, CEO and co-founder at Tray.ai

Rich Waldron, CEO and co-founder at Tray.ai highlights more about the core challenges in managing unstructured data, benefits of AI in workflow management, AI fundamentals for internal teams and more in this quick chat:

——

Hi Rich, tell us about yourself and Tray.ai?

I am from the UK, specifically Southampton, and studied at a university in Bournemouth where I found myself in the company of one of my co-founders, Alistair Russell, who is now Tray.ai’s CTO. In 2012, we co-founded Tray.ai, along with Dominic Lewis, Tray.ai’s CRO, after spending a number of years running a web agency together. We realized the value of using APIs in tandem with automation for businesses with minimal resources. Yet, this method poses complications because of the risk of failure if not set up correctly. This is how the concept for Tray.io, now Tray.ai, was created.

Tray.ai has undergone a metamorphosis over the past 12 years, especially with the proliferation of AI. From low code/no code to integration and automation, Tray.ai has focused on prioritizing customer experiences by ensuring simplicity and safety. Tray.ai’s composable AI integration and automation platform allows organizations to navigate the complexities of AI adoption. With the Tray Universal Automation Cloud, enterprises can integrate and automate intricate internal and external business processes while facilitating fast, flexible, and safe AI adoption.

Also Read: Predictive GenAI: Redefining ROI in the AI Revolution

What are some of the core challenges that businesses face today when it comes to managing unstructured data?

AI projects require organizations to manage huge volumes of unstructured data to see actual value. However, legacy enterprise integration tools were not built to process and manage this type of data. Instead, some teams have turned to single-purpose solutions to address new unstructured data demands for AI projects which adds additional complexity and cost at a time when speed and ROI are a top priority. Data complexity concerns will only continue to grow as AI is infused into business processes more and more. If organizations don’t have a solid integration and automation strategy that is built to handle unstructured data, they will be quickly left behind.

How is AI enabling better workflows here?

Any one organization could potentially have hundreds of integrations, most necessary but some unnecessary. As the number of integrations will only continue to increase, traditional data pipelines must now evolve to keep up with AI data demands. Workflows must be documented clearly, consistently, and confidently. Now with AI, teams have the ability to utilize capabilities like Tray.ai’s GenAI-powered workflow documentation to ensure they can build at maximum velocity without sacrificing maintainability or understandability. Managing multiple tools creates additional complexity, and AI-powered workflows can reduce this complexity to allow teams to accelerate time to value.

Can you throw brief insights into Tray.ai’s capabilities around data management?

Tray.ai helps organizations maximize the value of unstructured data and solve the scalability challenges found in AI projects. Specifically, Tray.ai’s new built-in Vector Tables complement traditional Lookup Tables so teams can now handle unstructured data for AI embeddings and metadata as high-dimension vectors to drive RAG pipelines, agents, or AI-infused processes. With the new Inline Functions feature, businesses can efficiently transform data through multiple steps in their Tray workflows, all within a single operation. The intuitive interface eliminates the need for coding while maintaining powerful processing capabilities.

Our built-in Merlin Intelligent Document Processing system uses AI to automatically analyze documents, identify key information, and transform unstructured content into structured data through a user-friendly interface. From invoice payment details to contract deadlines, the system extracts and validates critical information instantly. This allows organizations to streamline document processing workflows and feed data directly into AI and business systems, all within one platform.

By merging vector database capabilities with intelligent document processing in one unified platform, we make it simple to create AI workflows in a single solution. This allows teams to focus their energy on driving business results instead of dealing with complex technical setups.

When using AI to scale and hasten how unstructured data is handled, what fundamentals should internal teams follow?

Internal teams should prioritize tight integration and clear documentation when using AI to scale. AI-readiness for data means teams must be able to meet the inevitable increase in complexity while maintaining strong control and flexibility to meet processing demands. As more complexity is added to systems through AI projects, teams need to ensure clear communication and cross-collaboration across business units to maintain visibility and agility.

Also Read: From Aspirin to AI: Revolutionizing the Drug Approval Process

AI and data: what does the future hold in your view?

As AI becomes central to business operations, organizations must move beyond fragmented data systems and single-purpose solutions to succeed in their projects. Leaders should prioritize data integration and platforms that can process massive amounts of unstructured data, rather than managing hundreds of disconnected applications. By consolidating solutions and eliminating data silos now, organizations can create a flexible foundation that enables rapid deployment of AI innovations and adapts quickly to changing business needs.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]