4.24.2026
As restaurants navigate a rapidly evolving digital landscape, the balance of power between operators and third-party platforms continues to shift. Discovery, ordering, and customer engagement are increasingly shaped by algorithms, marketplaces and now AI-driven interfaces, often leaving brands with limited control over their most valuable asset: the customer relationship.
Against that backdrop, a new wave of technology providers is emerging with a different focus, helping restaurants reclaim first-party growth and improve the economics of digital ordering. Stephen Klein, co-founder and CEO of Saivory, is among those leading that shift. Built at the intersection of restaurant operations and software innovation, Saivory is developing a sophisticated set of AI-powered tools designed to improve customer discovery, streamline ordering, and increase the value of each digital transaction, particularly for high-margin use cases like large orders and catering. The platform reflects a broader industry movement toward using AI not just for automation, but for guiding customer decisions in real time and delivering measurable business impact for operators.
In this Spotlight Interview, Klein discusses the origins of Saivory, the growing importance of first-party channels and how AI is reshaping everything from search visibility to personalization and revenue optimization. He also shares practical insights on what restaurant leaders should be measuring, and where many are still getting AI wrong as the industry moves toward a more intelligent, data-driven operating model.
Can you start by sharing a bit about your background and the path that led you to co-found Saivory?
I started my career in strategy consulting at Bain & Company and then spent nearly a decade as a software and internet investor at PRIMECAP Management, where I focused on identifying high-growth technology businesses. From there, I moved into operating roles, including working in finance and early-stage growth at FinOptimal and in strategy and business operations at Houston-based eCommerce unicorn Cart.com.
I joined my partners at Saivory, who were already working on AI-solutions for my cofounder’s 30-unit restaurant brand, and saw the opportunity to combine my background in software investing with a first-hand operator perspective. Saivory was born inside this restaurant brand, where we saw firsthand how digital channels were evolving and how difficult it was for operators to drive growth in first-party vs. relying on third-party tech and delivery players that take hefty commissions and own customer relationships.
What specific problem in the restaurant technology landscape were you trying to solve when you launched the company?
Increasingly since COVID, third-party platforms control discovery and customer relationships, while restaurants absorb the cost and margin pressure and struggle to build engagement and loyalty with their customers. Saivory was built to help restaurants reduce their reliance on third parties by improving customer discovery, conversion, and engagement by using smarter AI-based technology that provides better customer experiences.
How would you describe Saivory’s platform today, and what core capabilities set it apart from traditional restaurant tech solutions?
Saivory is building an integrated set of AI-enabled tools that help restaurant brands drive first-party growth and a higher return on their technology spend. We start with dynamic and “smart” local pages 
that capture the intent of customer search queries and generate landing pages with more relevant content that improve search visibility, convert better, and when paired with paid search strategies, can drive higher return on ad spend via lower CPCs.
Once on a brand’s first-party website, our AI ordering tools streamline the cart-building process and minimize time, clicks, decisions, calculations, and the friction that leads to cart abandonment and suboptimal orders. These flows improve the user experience while increasing average order values by allowing brands to introduce upsell items, new products, and LTOs.
We view these two products as complementary, helping restaurants get more “shots on goal” and maximizing the value of those shots.
Currently, we are working on marketing automations that help brands create campaigns and ads that pair well with these tools, enable smarter franchisee LSM spend, and tangible ROI and visibility for the management and marketing leaders.
Who are your primary customers, and what types of restaurant brands tend to benefit most from your approach?
Saivory primarily works with multi-unit restaurant brands focused on growing their first-party digital business. These are often concepts with strong digital footprints, catering demand, or higher-value transactions. Brands like Shipley Do-Nuts and Fajita Pete’s are good examples.
For larger enterprise brands, we can layer our technology into their tech stack and integrate with existing vendors. For smaller chains, we can offer templatized eCommerce sites that combine all of our AI technology and can be spun up quickly and at low cost.
Saivory places a strong emphasis on high-value transactions like large orders and catering. Why focus there and what opportunity did you see?
Large orders and catering are some of the highest margin opportunities for restaurant brands, but they’re also where digital ordering breaks down most often. These orders involve complexity around group size, preferences, and budgets. Traditional ordering systems don’t help customers navigate those decisions, which leads to abandoned carts or pushes orders back to phone calls and manual processes.
We saw a clear opportunity to apply AI to guide customers through that complexity in real time, essentially recreating the experience of a great in-store associate, but digitally and at scale. When you remove that friction, you see higher conversion, larger order sizes, and more efficient operations.
What kind of measurable impact are your customers seeing?
The most immediate impact shows up in conversion and average order value, particularly on higher value orders.
In early deployments, we’ve seen ~25% increases in average order value from guided recommendations with our AI-enabled flows. In the case of Shipley Do-Nuts, AI-assisted orders delivered 26% higher AOV and were more than twice the value of third-party orders.
More broadly, when you remove friction from decision-making, you don’t just improve order metrics. Better consumer experiences improve loyalty, and growing your first-party CRM for effective remarketing is one of the most important things restaurants can do in an era where third parties have become so dominant.
How does your platform help restaurants strengthen first-party customer relationships in a landscape dominated by third-party marketplaces?
The first-party vs third-party landscape is one of the most commonly discussed challenges in the restaurant ecosystem given its strategic and financial importance to brands. There are numerous approaches and restaurants need to employ several to tip the scales back in their favor. Meeting customers wherever they are, providing them with the best possible experiences, collecting their data efficiently, and then using that data to power smarter personalized marketing all need careful consideration. Saivory is building tools for each part of the customer journey.
How do you think about customer discovery today and how is that changing as AI increasingly influences how guests find and choose restaurants?
Traditionally, customers have discovered brands via Google search or from third party marketplaces and delivery platforms. When they do start with Google, they often land on third parties that have learned how to “game the system” for SEO. We help brands fight back with our dynamic local pages that generate landing pages based directly on search queries and user intent, improving SEO search visibility by ranking higher in organic search. Additionally, pairing these landing pages with paid ads can drive more volume at lower CPCs.
Google will continue to be a major source of discovery and traffic, and brands must optimize their performance in that channel. However, in the last few years we’ve seen an explosion in AI assistant usage as customers gravitate toward platforms like ChatGPT and Claude. We build our technology around MCP, which ensures that brands’ first-party websites can show up, and consumers will ultimately transact in those assistant platforms. We want to future proof our customers for the world for this evolution of consumer journeys so they can meet their customers no matter where they start their search.
What does real-time personalization actually look like in a restaurant context beyond basic upsells and static offers?
Real personalization is contextual and problem oriented. For example, a customer ordering for 40 people gets a fully constructed cart with the right mix and quantities. A returning guest sees recommendations based on the restaurant’s order data and prior behavior. A budget-constrained customer gets an optimized order that maximizes value without overspending.
Consumers are increasingly learning how to prompt AI assistants to get their desired result as quickly as possible and we want to replicate that experience on our brands’ websites. If a customer says, “I’m planning an office lunch for 40 people and 10 are vegetarian,” we want to curate a cart immediately that ideally suits that request. We can introduce new products or LTOs that a customer may not be aware of, but would ultimately like, in a frictionless way that drives better experiences and higher AOVs.
How should restaurant leaders rethink KPIs and success metrics in a more AI-driven environment?
At the end of the day, KPIs around first-party growth are most critical for brands looking to grow strategically and sustainably. That includes visibility in search and AI assistants, traffic growth, conversion rate on high-value orders, average order value, time to check-out, and the mix between first-party vs. third-party.
What are the most common misconceptions restaurant operators still have about AI and its role in their business?
While AI can be applicable to a broad range of use cases and business problems, it has to be deployed thoughtfully and to specific challenges. We’ve seen studies across all industries that highlight disappointing deployments or ROIs from AI initiatives and often it’s more of an organizational or process problem than a technology flaw. To maximize value from deploying AI solutions, it is critical to have reliable data infrastructure and organizational alignment.
Looking ahead, what will separate the restaurants that successfully operationalize customer intelligence from those that fall behind?
The restaurants that succeed will treat customer intelligence as an operational capability, not a reporting function. They’ll unify data across channels, activate it in real-time, and give operators clear, actionable guidance rather than static reports.
