OurPortfolio.

It's incredibly difficult for even qualified job candidates to tailor their resume perfectly for each application to satisfy automated screening tools (ATS).

The client's existing Python script for geometric image analysis was producing inaccurate results due to transparency and DPI handling errors, completely blocking their workflow.

Recruiters using the client's platform were inundated with hundreds of applications, but traditional ATS filters were too blunt. They needed an intelligent ranking system to serve as a core differentiator.

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.

Aspiring CAT students lacked an accessible platform with a large question bank and the engaging, competitive features (like leaderboards) needed to stay motivated.

The client's trading platform lacked the professional-grade, customizable, real-time charting that all serious traders expect, making the product look amateurish and limiting its appeal.

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.

The client's team was spending dozens of hours each month manually scraping websites for critical market research and lead generation data.

Real-time video processing is computationally expensive. The challenge was to create a dehazing model that was both accurate and efficient enough to run on low-power edge devices like a Raspberry Pi.

The client had a Python script to automate the formatting of .docx documents that was completely broken, stalling their entire workflow.

The client needed to send bulk marketing emails, but their low deliverability rate (~60%) was destroying their domain's reputation and wasting marketing spend.

For their competitive "treasure hunt" event, managing registrations and updating a leaderboard manually via a spreadsheet was clumsy and lacked the real-time excitement needed to engage attendees.

The client, a business coach, needed to aggregate business data from multiple online directories based on ZIP codes and business types—a tedious and time-consuming manual process.

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.

While functional, user feedback showed the initial AI-generated reports were not perfectly optimized. They contained redundant information and lacked key data points, creating friction for the end-user.

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 spiritual community lacked a modern, authentic digital tool for their Japmala meditation practice, risking disconnection from a younger, digitally-native audience.

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.

Indian retail traders were overwhelmed using multiple disconnected tools for charting, screening (Chartink), and brokerage (Dhan), leading to missed opportunities and analysis paralysis.

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.

Student engagement was suffering because there was no central, dynamic platform for clubs to showcase activities and for students to find projects. Information was scattered across notice boards.

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.

This was a technical challenge to explore and master the NEAT (NeuroEvolution of Augmenting Topologies) algorithm, a powerful but complex AI technique for training agents without explicit programming.

The "decision fatigue" of planning meals often leads to food waste and unnecessary spending on takeout. The client needed to validate an idea to solve this problem.

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 school's staff was drowning in over 50 hours of manual administrative work per week for student management, fee tracking, and attendance, leading to errors and delays.

This was a technical exploration into the practical challenges of integrating with a major third-party API (Spotify), handling a complex authentication flow (OAuth 2.0), and replicating a high-quality user interface.

While standard recommendation algorithms are good, this project explored if modern LLMs could create more nuanced, personalized recommendations by understanding the deeper context of a user's listening history.

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.

The client needed a simple, well-designed utility app to master the native Android development and publishing lifecycle as part of a broader mobile strategy.

Growing a YouTube channel requires consistent engagement, but doing this manually across multiple accounts is unsustainable and takes time away from creating content.