Restaurant Software Solutions for Long-Term Growth

How Samosa Party is Using AI to Scale 100+ Locations Had an insightful conversation with our portfolio founders Diksha Pande and Amit Nanwani from Samosa Party about their AI-first approach to restaurant operations. Here's how they're solving real problems across their 100+ locations: Customer Experience Revolution The Challenge: How do you track order-taking quality, stock-outs, and customer insights across dine-in locations? Their Solution: Storefox.ai uses ambient audio analysis at point-of-sale to automatically capture: Real-time stock-out alerts Customer product suggestions and feedback CX compliance (greetings, upselling, order accuracy) New product ideas directly from customer conversations Think about it: Every customer interaction becomes actionable data without any manual effort. Supply Chain Intelligence The Challenge: Forecasting and replenishment for 100 stores from multiple commissaries and warehouses. Their Solution: Crest AI platform generates automated indents considering: New store openings Seasonal patterns and holidays Product launches and promotional offers Historical demand patterns The game-changer? Full ERP integration means zero manual intervention for day-to-day operations. Operational Acceleration Beyond the core systems, AI is transforming their: Innovation cycles: Product development decisions that took weeks now happen in days Store design: AI-powered visualization for optimal layouts and workflows Marketing: Faster collateral creation and campaign development Training: Team members using AI for structured communication and training materials The Bigger Picture What impressed me most isn't just the tools—it's the systematic integration approach. Instead of isolated AI experiments, Samosa Party is weaving intelligence into every operational layer. Key Takeaways for Restaurant Tech: StoreFox-style ambient data capture can provide insights without disrupting workflows Crest-integrated ERP AI eliminates manual decision-making bottlenecks Democratizing AI tools across teams accelerates innovation at every level The restaurant industry often lags in tech adoption, but companies like Samosa Party are proving that strategic AI implementation can be a serious competitive advantage. What opportunities do you see for AI in traditional industries? Would love to hear your thoughts! #RestaurantTech #ArtificialIntelligence #SupplyChain #CustomerExperience #FoodTech #Innovation #Scaling #RetailTech Kalaari Capital

When Yoshinoya America switched away from Olo, it wasn’t a quiet breakup, it was a bet. A 17-month detour through a large POS provider that promised savings and delivered headaches. Fraud stacked up. #Menu updates crawled. #Franchisees lost patience. The math didn’t add up. In May 2025, they came home. Olo, founded by Noah Glass back in 2005 before the word “app” meant anything, started as text-based ordering for coffee shops. Now it’s the backbone for 750 restaurant brands and 89K locations. The mission hasn’t changed, make #hospitality scale without killing its soul. When Yoshinoya flipped their system back on, it wasn’t nostalgia. It was necessity. Jason Perovich, Yoshinoya America’s VP of Tech, joined in Jan 2025 and found a tech stack bleeding efficiency. Fraud north of $200K. Menu updates taking days instead of mins. Franchisees toggling between tabs like air-traffic controllers. His first major move, re-activate #OloOrdering and #OloPay. The applause from operators when he announced it wasn’t polite, it was cathartic. Olo’s Serve storefront brought menu agility back, under 20 mins for an update, not three to five days. Olo Pay dropped chargebacks by 88% within 4 mos. #Machinelearning fraud models tuned for restaurant patterns caught what generic processors missed. Franchisees could see #orderfailures in real time instead of learning about them through angry customers. That kind of visibility protect revenue and restores trust. For Glass, the win wasn’t about reclaiming a customer, it was validation of Olo’s original thesis. Generic software can process an order, but only restaurant-specific infrastructure can run the business. It’s why Olo went public in 2021 at a $3.6B valuation, and why private equity giant Thoma Bravo took it private again in Sept 2025 for $2B, to double down on innovation, not cost-cutting. Leadership changes at Yoshinoya, Jon Gilliam out in Oct 2024, Glenn Lunde in and then out by Sept 2025, only amplified the tension. Through that turnover, the tech stayed the signal. When Perovich made the call to bring Olo back, it wasn’t a pivot. It was a recalibration of what scale means in an era where every tap, swipe, and sec counts. The return proved something simple, switching costs aren’t just technical, they’re cultural. You can’t outsource operational DNA. Yoshinoya’s teams run faster, cleaner, sharper now. #Menuagility, #fraudcontrol, #dataclarity. All connected through Olo’s open SaaS stack that’s processing 2.5M orders a day. It’s easy to chase cheaper platforms. Harder to build durable systems that pay for themselves in speed, precision, and peace of mind. Yoshinoya’s applause was the market speaking in real time. In this game, that’s the only ROI that matters. #Startups #StartupNews #Fraud #FraudTech #Restaurant #RestaurantTech #Infrastructure #Data #DataDriven #SaaS #Technology #Innovation #TechEcosystem #StartupEcosystem #TechNews If software engineering peace of mind is what you crave, Vention is your zen

New technology capabilities are revolutionizing the operations of small and medium-sized restaurants, offering practical solutions to enhance efficiency, customer experience, and profitability. Automating reservations and customer service through chatbots or virtual assistants is one innovative application of technology that streamlines processes and optimizes workflows. By handling common inquiries, booking tables, and managing waiting lists efficiently, these systems reduce friction and enable staff to focus on delivering exceptional service, elevating overall dining experiences. Moreover, technology is transforming inventory management for restaurants. Advanced applications analyze inventory, historical sales data, seasonal trends, and local events to predict demand accurately, helping businesses avoid overstocking or ingredient shortages. This precise management leads to reduced waste, increased revenue, and enhances customer satisfaction, particularly crucial for small and medium-sized restaurants with narrow profit margins. Furthermore, personalized marketing powered by data analytics is enhancing customer engagement. By leveraging customer data to create targeted promotions and loyalty programs, restaurant owners can boost customer retention and drive repeat business. AI-driven recommendation engines can elevate the dining experience by suggesting menu items tailored to individual preferences, enhancing both customer satisfaction and restaurant profitability.

In the last 6 years, Yum! Brands saw their digital sales jump from 19% in 2019 to over 50% today. And we are way post-COVID, so it is a very good benchmark for where a successful restaurant business could be. Below are some things I've learned about Yum's way of approaching AI and digital by following the company's CDTO Joe Park. Inventory Management & Sales Forecasting One of the most successful AI implementations at Yum! Brands has been in inventory management. KFC locations achieved a remarkable 90% reduction in stock-outs after implementing AI-powered forecasting. Previously, store managers spent up to four hours monthly making calls between stores to manage inventory shortages. The AI system not only eliminated this inefficiency but also reduced food waste and improved customer satisfaction. Kitchen Management Systems Pizza Hut's implementation of AI for order orchestration shows how technology can solve real operational challenges. During peak hours, like Friday dinner rush, the system acts as an "air traffic controller," determining optimal cooking sequences and delivery timing. This ensures customers receive fresher, hotter food while reducing stress on kitchen staff. Computer Vision Applications Yum is piloting computer vision for several purposes in QSR operations: - Monitoring food safety compliance - Verifying order accuracy before serving - Managing drive-thru efficiency by counting cars and suggesting faster-to-prepare items during peak times Integration Challenges & Solutions The average QSR restaurant juggles about 15 different technology vendors - a nightmare for managers. Yum! Brands' solution, Byte by Yum, demonstrates how an integrated platform can reduce this complexity. The platform consolidates point-of-sale, mobile apps, kitchen management, and team productivity tools under one AI-powered system. Byte POS is rolling out at KFC U.S.; the UI is redesigned to feel iPad-simple, and training time is now a fraction of the old green-screen system Training AI systems presents unique challenges in the restaurant industry. Common menu items like "Baja Blast" or "chalupa" don't exist in standard English dictionaries, requiring custom training for voice recognition systems (hence the recent NVIDIA partnership). On NVIDIA podcast, Joe mentioned the partnership helped them reach viable voice-AI products in under four months Focus on Problems, Not Technology Joe Park emphasizes the importance of "falling in love with the problem." Whether it's order accuracy, drive-thru speed, or inventory management, successful AI implementation starts with clearly defined business challenges. According to Joe, and based on the problems he sees, emerging opportunities in tech for restaurants include: - Enhanced voice AI for order taking - Advanced computer vision for quality control - AI-powered restaurant management systems that provide proactive recommendations for inventory, staffing, and local marketing

Toast bet on hardware during peak SaaS. VCs called them insane. In 2013, Silicon Valley had one gospel: be asset-light. Zero marginal costs. Scale infinitely. Salesforce owned CRM at 70% gross margins. Workday just IPO'd. Zendesk scaled without servers. Toast did the opposite. Manufacturing POS terminals. Hiring installation armies. Running cables through restaurant walls. "You're insane," VCs said. "Hardware doesn't scale." They learned the hard way. Their first attempt - a sleek consumer app - crashed and burned. That failure taught them what competitors missed: Toast wasn't building a product. They were becoming the restaurant's operating system. Today: $25B market cap. 106,000 locations. 15% of US restaurant transactions. While everyone fought for the surface, Toast owned the plumbing. LegacyCo sells features. NewCo stacks outcomes. After their app failed, Toast realized restaurants didn't need features. They needed operational hell eliminated. Outcome 1: Orders Synchronized (2013) Kitchen synced with servers. No lost tickets. Critical workflow owned. Outcome 2: Payments Reconciled (2015) No more 2am reconciliation. Toast gets 2.6% of every transaction. Outcome 3: Unified Accounts (2016) Eliminated "Tablet Hell." One system while competitors added complexity. Outcome 4: Automated P&L (2017+) Self-running restaurant. Sales triggers payroll, inventory, everything. This is Outcome Stacking. LegacyCo sells licenses. NewCo creates partnerships. Core principle: We only win when you win. Payment Processing: Busy Saturday? Toast earns more. Slow Tuesday? Less. Success shared. Toast Capital: Repayment from daily sales. No sales, no payment. True partnership. Square tried pretty iPads. Clover pushed modularity. They missed the insight: Restaurants want one system that never fails when the kitchen's on fire. The NewCo Playbook Choose Constraints Wisely: Toast picked restaurants only. Focus became fortress. Stack Outcomes: Each layer solved complete problems. Do Hard Things First: Hardware was slow but unassailable. Become Essential: Not through contracts, through infrastructure. Toast embraced what others avoided: Hardware. Cables. Payments. Payroll. The unsexy became the moat. NewCo's future isn't everything for everyone. It's everything for someone. Choose your fortress wisely. (Condensed version for Linkedin - Subscribe to my newsletter to read the full case study)

You don’t scale to 110 locations by guessing. In Franklin, Tennessee, we spent the day filming at the newest Taziki’s Mediterranean Café location. What stood out wasn’t the branding or the buildout. It was the discipline. I sat down with Alfredo, the General Manager, and Dan Simpson, the CEO, to talk about how a fast-growing brand with 110 locations and more than 3,000 employees stays financially aligned while continuing to grow. The answer was simple: visibility. When operators understand their numbers in real time, they make better decisions on labor, food costs, and growth. When leadership trusts the data, they can scale without losing control. Restaurant365 isn’t a “tool” in that environment. It’s infrastructure. This is what modern restaurant growth actually looks like — not hype, not shortcuts, just systems that let great operators do their jobs well.