The Work of Vedant Singh at Mustard, Inc.

Local restaurants operate in one of the most competitive and margin-sensitive sectors of the economy. Customer preferences shift quickly, competition is constant, and marketing dollars must translate into measurable results. In recent years, social media has become one of the most important channels for customer acquisition. Yet influencer marketing—particularly in the food industry—has historically lacked structure, predictability, and accountability. Identifying the right creators, coordinating campaigns, and determining whether content drives measurable business outcomes has often required significant manual effort with inconsistent results. Transforming influencer marketing into a scalable, performance-driven growth channel requires more than marketing expertise. It requires robust data infrastructure, carefully engineered systems, and production-grade machine learning. As Head of AI and Data Strategy at Mustard, Inc., Vedant Singh architected and leads the core data and AI systems that underpin the company’s platform. His work has been central to converting influencer marketing from a subjective, manually managed process into a structured, AI-powered engine capable of delivering measurable growth for restaurants and food brands.Technical InterviewQ1. Could you describe Mustard’s platform and your role within the organization? Mustard, Inc. is a subscription-based influencer marketing platform designed specifically for restaurants and food brands. The platform connects restaurant partners with vetted, hyper-local food creators who publish short-form content across platforms such as Instagram and TikTok. As Head of AI and Data Strategy, Vedant Singh is responsible for the architecture, development, and oversight of the data and machine learning systems that power Mustard’s core matching and recommendation engine. These systems form the technological backbone of the platform’s creator selection and campaign optimization capabilities. His role ensures that influencer selection operates at scale, produces consistent and measurable results, and can be directly tied to outcomes such as engagement, website visits, reservations, orders, and revenue.Q2. What problem does your AI system aim to solve in influencer marketing? Influencer matching at the local level is significantly more complex than it appears. Effective matches depend on multiple interacting variables, including geography, cuisine alignment, audience demographics, creator performance history, timing, availability, and budget constraints. When handled manually, these decisions vary across operators and cannot scale reliably. Vedant’s work focuses on replacing subjective, judgment-based selection with a data-driven ranking and recommendation system. This system systematically identifies creators most likely to perform successfully for a specific restaurant and campaign objective, introducing consistency, transparency, and scalability into the process.Q3. How is the influencer matching system architected from a data perspective?The system is designed as a scalable recommendation and ranking engine comparable to those used in established marketplace platforms. Vedant led the integration of restaurant attributes, creator attributes, and historical campaign performance data into unified, production-ready datasets. On the restaurant side, variables include location, neighborhood proximity, cuisine type, business model, price range, operating hours, and target audience. On the creator side, variables include audience geography, follower demographics, engagement rates, content style, posting frequency, and historical performance on similar campaigns. Campaign constraints—such as timing, regulatory considerations, and budget—are incorporated into the final ranking logic. This structured architecture enables the platform to generate recommendations based on quantifiable signals rather than intuition, forming a core component of Mustard’s operational infrastructure. Q4. How does the system incorporate local relevance and explainability? Local relevance is a foundational signal within the system. For example, a restaurant in Alhambra derives limited value from an influencer with a nationally dispersed audience, regardless of follower count. In contrast, a creator whose audience is concentrated in the San Gabriel Valley and aligned with the restaurant’s cuisine offers materially higher potential impact. Vedant’s models explicitly encode these geographic and behavioral relationships, ensuring that recommendations are not only performance-oriented but also interpretable. This explainability supports internal decision-making and reinforces trust with restaurant partners as the platform scales. Q5. What machine learning approaches do you use to generate recommendations?Vedant employs a combination of supervised learning models, learning-to-rank techniques, and hybrid recommendation approaches. These models estimate expected engagement and projected return on investment, prioritizing creators based on predicted performance. Model performance is evaluated using concrete outcome metrics, including engagement lift, post-level performance, and downstream business impact. Where appropriate, Vedant also evaluates advanced AI methodologies, including generative techniques, to improve operational efficiency while maintaining reliability and governance standards. Q6. How do you manage the full lifecycle of AI system development? Vedant oversees the full lifecycle of AI system development. This includes defining performance metrics in collaboration with leadership, designing and implementing ETL pipelines, and standardizing data across restaurant onboarding workflows, creator profiles, and campaign results. Significant emphasis is placed on data integrity, schema consistency, and validation processes, recognizing that production-grade machine learning systems depend on high-quality inputs. Following deployment, models are continuously monitored using real-world performance data to ensure stability, accuracy, and sustained effectiveness. Q7. How do you ensure the reliability of your models and their improvement over time? Reliability is maintained through structured governance and empirical validation. Vedant monitors model drift, conducts A/B testing, and performs controlled experiments prior to deploying updates into production. Campaign outcomes are systematically fed back into the system, enabling continuous learning from seasonal trends, evolving creator behavior, and changing restaurant requirements. This feedback loop ensures that the platform’s recommendation engine remains adaptive while preserving performance standards. Q8. What kind of visibility does the platform offer to stakeholders? Vedant led the development of analytics dashboards that provide transparency to both restaurant partners and internal teams. Restaurant partners can evaluate predicted versus actual engagement, compare creator performance, and review campaign-level insights.Internally, teams monitor platform health, creator supply quality, and cross-city performance metrics. This visibility reinforces accountability and supports data-driven operational decisions across the organization. Q9. What is the broader implication of your work at Mustard? Vedant’s systems have enabled influencer marketing within Mustard to transition from a manually coordinated marketing activity into a scalable, AI-powered growth channel. By automating creator selection, campaign optimization, and performance measurement, the platform allows restaurants to leverage social media without proportionally increasing operational complexity. The data and machine learning infrastructure he architected supports the company’s ability to expand while preserving local relevance and return on investment. His contributions form a core component of the platform’s technological differentiation and scalability. Vedant Singh’s work at Mustard, Inc. demonstrates the practical application of advanced machine learning within a high-friction segment of local commerce. By architecting production-grade recommendation engines, scalable data pipelines, and structured governance frameworks, he has helped transform influencer marketing into a repeatable and accountable growth mechanism for restaurants.His leadership in developing and operationalizing these systems connects sophisticated AI methodologies to measurable business outcomes. As technology continues to reshape local commerce, his work reflects the role of rigorous data strategy and machine learning infrastructure in enabling sustainable, scalable growth.



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