AiThority Interview with Vishal Joshi, Head of Engineering at TabSquare.AI

Vishal Joshi, Head of Engineering at TabSquare.AI, shares more about the latest trends influencing AI and automation, secrets for scalable cloud integration, biggest engineering challenges, and more about AI bias and decision making in this Interview by AiThority.com…

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Hi Vishal, your career spans SaaS innovation, scalable architectures, and generative AI. Tell us about your role as an engineering leader at TabSquare.AI.

At TabSquare.AI, I lead a talented engineering team focused on developing AI-driven solutions that elevate customer experiences in the restaurant industry. My role involves shaping our technical strategy, overseeing architecture design, and ensuring the scalability and reliability of our products. I work closely with product managers, engineering leads, data engineers, and cloud teams to ensure alignment with our business objectives. My focus has been on building high-performance systems that scale seamlessly while nurturing a culture of innovation within the team. We emphasize the integration of cutting-edge technology with real-world problem-solving, ensuring that our solutions deliver tangible value to our restaurant partners.

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How do you balance AI automation with the need for personalized customer experiences in restaurants?

Balancing AI automation with personalized customer experiences requires a thoughtful approach where AI complements human interactions rather than replacing them. In our solutions, AI automates repetitive tasks such as tracking customer preferences, allowing restaurant staff to focus on more meaningful engagements. We ensure that AI recommendations and interactions remain personalized by leveraging data on customer behavior, past orders, and preferences. By embedding AI-driven insights into the dining experience, we provide restaurant partners with powerful tools to analyze data (not customer data but sales data), predict foot traffic, and tailor promotions to boost engagement and sales.

TabSquare operates across multiple countries. How do you ensure scalability and seamless cloud integration across diverse markets?

Ensuring scalability and seamless cloud integration across diverse markets requires a robust and flexible cloud infrastructure. At TabSquare, we primarily rely on Google Cloud Platform (GCP) to support our global operations, ensuring that our services can scale rapidly to meet the demands of different regions. We leverage Kubernetes for container orchestration, which enables us to deploy and manage microservices efficiently, ensuring high availability and low latency. To further enhance performance and reduce latency, we utilize a Content Delivery Network (CDN) for fast content delivery across various geographies. Additionally, we use Terraform to streamline regional deployments, allowing us to set up infrastructure quickly and consistently within minutes. Our services are also designed to be adaptable to regional regulatory and compliance requirements, ensuring scalability and optimization as we expand into new markets.

What are the biggest engineering challenges in the industry when deploying AI-driven solutions at scale?

The biggest engineering challenges when deploying AI-driven solutions at scale include ensuring high-quality, representative data, maintaining model accuracy, and scaling infrastructure. AI models rely on clean, diverse data, which often requires extensive preprocessing in real-world scenarios. Once deployed, models need continuous monitoring and retraining to adapt to evolving customer behavior and trends. Additionally, AI solutions demand significant computational resources, requiring scalable infrastructure that maintains low latency and high availability, often managed through technologies like Kubernetes. Lastly, integrating AI with existing systems and ensuring seamless operation across platforms is a complex task, demanding careful coordination and design.

What AI trends do you foresee becoming game-changers for the F&B sector in the next few years?

The future of AI in the F&B sector is incredibly exciting. I personally foresee AI-driven dynamic pricing models becoming mainstream, where AI analyzes factors like demand, time of day, and location to adjust pricing in real-time, creating more profitable opportunities for restaurants. Additionally, AI-powered voice assistants and chatbots will continue to improve, making ordering more conversational and accessible. Another key trend will be the evolution of hyper-personalization, where AI can offer individualized experiences at scale—such as tailored menus or real-time recommendations, based on customer preferences, demographics, and ordering history.

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How do you address the challenges of AI bias and decision-making transparency in automated restaurant solutions?

At TabSquare, addressing AI bias and ensuring decision-making transparency are core tenets of our AI strategy. We carefully curate and preprocess datasets to ensure that they are diverse and representative of the various customer segments. We also perform regular audits and validation of our AI models to identify and mitigate any potential biases. Additionally, we provide transparency in how our AI systems make recommendations and decisions by offering clear explanations of the data inputs and model outputs. This helps both restaurant owners and customers trust the AI-driven solutions and allows for continuous improvement.

Share the upcoming innovations or strategic expansions on the horizon at TabSquare.AI that AI leaders must know about.

At Tabsquare, we have consistently focused on developing products and platforms that address genuine merchant challenges and elevate guest experiences. This approach has guided our experimentation with AI-driven recommendations and hyper-personalization, even before AI’s widespread adoption. We remain committed to delivering tangible solutions rather than pursuing fleeting AI trends. AI’s role, in our view, is to accelerate and optimize the delivery of these enhanced experiences. Moving forward, we will harness our extensive datasets to significantly improve consumer journeys, engagement, and discovery.

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