#StartupsEverywhere: Aidan Chau, CEO & Founder, Maple
This profile is part of #StartupsEverywhere, an ongoing series highlighting startup leaders in ecosystems across the country. This interview has been edited for length, content, and clarity.
Saving restaurants without sacrificing workers
Born to immigrant parents, Aidan Chau grew up around restaurants and saw firsthand the operational challenges they face. He built Maple, an Al phone answering service that handles orders and reservations so restaurant teams can stop missing phone calls and operate with greater efficiency. We sat down with Aidan to discuss building Maple, the potential implications of the New York RAISE Act, and how the right Al tools create jobs rather than replace them.
Tell us about your background. What led you to Maple?
I left Columbia to join Air AI as one of the early engineers working on voice AI for sales calls. These were early days for voice AI applications—APIs were undocumented, reliability was rare, and we were basically building at the application layer before the infrastructure was ready. I learned a lot about the difficulty of building real-world, production use cases versus demos or theoretical AI. After that, I started Eon, a restaurant tech company that was acquired about eight months in. That acquisition gave me the capital and relationships to start Maple.
I come from a restaurant family. A lot of my family and friends run Chinese restaurants in New York and beyond. The restaurant industry is huge and restaurant owners are constantly dealing with problems. The biggest one I kept hearing about was missing calls and losing orders because staff were slammed. So we built Maple to answer the phone, take orders and reservations, and sync everything with their existing systems. Since launch, we’ve brought on incredible partners like Shift4 and OpenTable and signed thousands of restaurants and salons.
You mentioned that the acquisition of your earlier company, Eon, helped fuel Maple. Can you share a bit more about that?
The timeline was fast—we went from founded to acquired in eight months. We built an initial version, got distribution through a partnership, and it became clear pretty quickly that restaurants wanted this as part of their POS suite. Our distribution partner decided to acquire it and roll Eon out to their customer base. The timing worked out because I was already thinking about Maple, so selling made sense. The capital went straight into Maple, and we leveraged the relationships we'd built.
What is the work you all are doing at Maple?
Our core product is an AI phone answering service for restaurants. An owner signs up, we integrate with their existing point of sale system and other tools like OpenTable, and Maple immediately starts handling their phone lines. It takes orders, books and modifies reservations, manages delivery requests, handles cancellations–basically anything a customer would call about. The goal is to free up restaurant teams from being stuck on the phone so they can focus on what actually matters: cooking great food and taking care of guests in person.
How is Maple built? What sorts of models and tools are you using and what do you want policymakers to know about them?
When it comes to our tech stack, we use lots of existing products and models in the marketplace, and policies that impact those products and their availability will affect us. Maple uses a smaller, open source model that is the core of our product. We use existing APIs for voice and transcription, and we leverage large frontier models to help with our training pipeline. Our smaller, specialized model is more resource efficient than using a large language model (LLM), but we have to spend a lot of time on post training, fine-tuning, and doing reinforcement learning to make sure that it works for the task we’re asking the model to perform. We rely on large models as part of that process. The Responsible AI Safety and Education (RAISE) Act here in New York could lead to reduced access to leading LLMs. Without easy access to those large models, these small models will perform significantly worse than the large models would even at baseline.
If there was one thing you wanted policymakers to know about AI, what would it be?
I would like policymakers to understand that AI is bringing about much more good than bad. There’s a lot of fear-driven conversations around AI, specifically around it taking jobs from people, but that misses what's actually happening. The restaurant industry is brutal–most restaurants fail within five years. Maple has helped restaurants stay more profitable and expand, which means more jobs, not fewer. It’s helping their business to grow. A restaurant with a single location can now provide more stable jobs to their employees and potentially expand to a second location and beyond, providing more opportunities in general. With products like Maple, there is a net increase in employment opportunities and profit. I don’t see positive impacts like our story being brought up often in policy discussions, and I think these are just the kinds of outcomes we should be striving for because AI brings a lot of value and potential to the U.S. and our economy.
What are your goals for Maple moving forward?
The best restaurant owners I know are deeply passionate about their craft, but they're constantly pulled away from that work to handle operations—managing phones, coordinating suppliers, tracking inventory, communicating with staff. Our goal is to keep expanding into workflows that have been untouchable by software until now: things like intelligent waitlist management, taking supplier calls, among other back of house automations, like voice-based shift notes for kitchen teams. There's an enormous opportunity to automate the operational complexity that buries restaurant owners.
Our north star is to make running a restaurant as easy as possible. When we do that right, owners can spend all their energy on the craft of great food and exceptional hospitality.
All of the information in this profile was accurate at the date and time of publication.
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