The Center for Data Innovation spoke with Eyal Feldman, co-founder and CEO of Stamplia California-based fintech company that uses AI to streamline accounts payable processes for a range of companies. Feldman talked about how an accounting mistake inspired him to start Stampli and how Stampli’s AI assistant Billy is helping companies make their accounting departments more productive.
Martin Makaryan: What inspired you to start Stampli?
Eyal Feldman: The decision to start a business was not an easy one. After gaining experience with Enterprise Resource Planning (ERP) and document management systems, I thought there had to be a more efficient way to connect people, processes and documents. I wanted to use technology to break through barriers that often hinder process improvements, especially in large companies. After considering various options, I decided on accounts payable processes because it is an important function for any business – for example, it ensures that accounts are in order and invoices are processed correctly – but an area that needed more innovation.
The impetus for me personally came when I almost lost business at my previous workplace due to an invoicing error. The error was completely avoidable and I realised the complexity of invoice processing. An invoice can go through a number of different channels depending on the type of organisation and therefore effective collaboration between departments is very important. It was a moment of enlightenment that showed me that we could make a significant difference.
Makaryan: How does Stampli AP automate?
Feldman: AP should be a collaborative process and Stampli provides organizations with software that allows them to turn each invoice into a landing page where all relevant parties can collaborate. On this landing page, we provide all the necessary information: discussions, questions, captured data, relevant documents, etc. This central hub allows customers to reduce the amount of time accountants spend on invoice processing, reduce bottlenecks, and avoid human errors. We also ensure seamless integration with the customer’s accounting environment, either in the cloud or using on-premise hardware. We offer native integrations for more than 70 ERP systems, including complex ones. Our goal is to avoid disruption and ensure that Stampli feels like a natural extension of the organization’s existing system.
We’ve also integrated AI to predict next steps and save our clients time. We’ve built a generative AI assistant called Billy that automates many time-consuming accounting tasks, like summarizing text and answering questions about an invoice. Billy frees up users’ time to focus on more important tasks and avoid simple human errors in accounting. Billy can also suggest cost allocations and predict work steps to ensure an invoice goes through all stages based on a company’s AP policies. Another key feature that makes Billy very useful is that it learns from mistakes and corrections that accounting staff may make.
Makaryan: What success stories show how Stampli’s automation has led to significant improvements?
Feldman: We have numerous success stories. For example, one of our customers reported that their invoice processing time went from 40 hours per week to just a few hours, with nearly 100 percent accuracy. Many customers have told us that they were able to scale their business without hiring additional staff and use their existing team for more strategic tasks. Perhaps the most exciting feedback I hear is that companies that use AI tend to feel more valued for their accounting and finance departments. The improved collaboration has company-wide benefits, as accounts payable processes involve many people outside of accounting departments.
Makaryan: How do you use data to improve your product?
Feldman: We use data in two main ways: First, we have a sophisticated approach to creating, distributing, and reviewing our own data and insights. We have a team that works closely with me to ensure different departments have the right insights to focus on our priorities and leverage customer feedback to optimize our offerings. Second, to improve our AI systems, we leverage our customers’ complete workflow data. By understanding each step of the AP workflow for our users, we can effectively reverse engineer what a user would do in a given situation. This, in turn, helps us build AI systems that perform tasks our customers are most likely to need help with. By using this comprehensive data, we are well positioned to build sophisticated solutions in the accounting space.
Makaryan: What challenges do you face as a data innovator?
Feldman: A recent challenge for me has been the market noise around AI. The rise of generative AI has been very exciting and promising for many sectors, but many companies claim to be “using AI” when they are only scratching the surface of what is possible with this technology. Our challenge is to educate both our existing and prospective customers and focus on providing real value, not just what looks good on marketing slides. We approach AI like hiring an employee – it’s not about making big promises, it’s about demonstrating real value to customers.
