Execution in Action

The client's core workflow was a 2-hour daily chore of manually scanning API data and placing bets through another platform. It was tedious, error-prone, and slow.

The client's team was spending hours each day manually looking up and transcribing hundreds of Betfair Starting Prices from a website into a spreadsheet.

The client's manual quoting process, which involved complex tiered pricing and multiple add-ons in spreadsheets, was a major bottleneck in their sales cycle.

During peak lunch hours, campus food stalls are chaotic with disorganized queues. The hackathon challenge was to design and build a solution to bring order to this chaos.

The client had a manual, multi-step process for generating AI reports that was unscalable, requiring them to copy data from Google Forms, run an AI, and manually email a PDF.

The client's HR team was spending 2-3 hours daily manually posting single job openings across five different platforms, creating a significant operational bottleneck.

The client's AI agent was powerful but inaccessible. Without being embedded on their website, it couldn't generate leads, validate user interest, or provide the interactive experience needed to build a real product.

The client's core product was completely non-functional. Their main value proposition—AI-generated fitness plans—was failing, leading to a surge in support tickets and a loss of all business momentum.

The platform had a working AI feature but no monetization engine. Every user who generated a plan represented a missed revenue opportunity. The business model was stalled.

The client anticipated their new app's growth would make their manual, spreadsheet-based operations for sales, support, and user account management completely unsustainable.

The client needed to launch a professional brokerage but was wary of closed, black-box solutions from other agencies that offered no code ownership or transparency—a major risk in a regulated financial space.

The client had a validated product idea for an AI relationship analyzer but lacked the technical team to handle the entire execution stack: frontend, backend, AI, and payments.

The client was running a paid Telegram trading community and faced a nightmare of manual work: tracking Razorpay payments, adding/removing users, and sending renewal reminders.