Sid Bendre

Sidhant “Sid” Bendre is a co-founder of Oleve, the New York consumer-software company behind Quizard AI and Unstuck AI. He builds consumer AI products around viral distribution, profitable small teams, reusable infrastructure, and structured methods for making unpredictable models reliable.

Bendre grew up in Nigeria and studied computer science at the University of Rochester, graduating in 2023. Before founding Oleve, he worked as a machine-learning engineer at FLX AI and held internships at Microsoft, Slack, and Zendesk. At Stanford’s TreeHacks 2023, his team won the Moonshot Prize for DroneFormer, a system that converted natural-language objectives into drone-control programs. The University of Rochester’s account of the project documents the award and his graduation-year work.

Bendre and his co-founders launched Quizard AI in January 2023 while still in college. An early TikTok campaign accelerated adoption; after graduating, the founders moved to New York, where Quizard became profitable and reached $1 million in annual recurring revenue within its first nine months. They subsequently launched Unstuck AI, a study companion built around students’ learning materials, which reached one million users in under nine weeks.

By the 2025 AI Engineer World’s Fair, Oleve was a profitable four-person company generating approximately $6 million in annual recurring revenue across its portfolio and developing a third product outside education. Those figures describe the company in 2025, not its current performance.

How he builds dependable consumer AI

• TRELLIS framework. Bendre’s structured approach to AI-product reliability clusters actual user behavior into intents, converts those intents into bounded workflows, and recursively isolates failures within each workflow. He prioritizes improvements using traffic, negative user sentiment, achievable gains, and strategic importance, preserving conversational flexibility while making individual experiences testable and accountable.

• Reusable product infrastructure. Oleve carries model integrations, internal libraries, experimentation systems, notification infrastructure, and product templates from one application into the next. Its feature-management infrastructure routes requests between model providers and Azure OpenAI endpoints, reprioritizes file-ingestion services during outages, and changes interfaces or paywalls without redeployment.

• Tiny teams with explicit ownership. Bendre organizes engineers into product-owning “harvesters” and infrastructure-building “cultivators.” Harvesters own user outcomes and experiments; cultivators build shared automation across products and business functions. His lean operating model assigns individuals measurable business goals and uses AI-assisted tooling to expand each person’s effective scope.

Bendre ultimately envisions coordinated AI systems handling product, marketing, research, and operational workflows under human strategic direction. His writing on educational AI also emphasizes measurable learning outcomes, interoperability, transparent recommendations, privacy protections, and monitoring for bias.