iRepository (Institute of Business Administration)
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No-Code API Management Platform (APIfy)
No full textThe increase in demand of APIs in today’s rapid software development requires efficient creation, testing, deployment, documentation and monitoring of APIs. Traditionally, a technical individual is required to code the API endpoints, setup databases, integrate third-party services and setup DevOps configurations. These are time-consuming, resource-intensive and costly. To address these challenges and allow people with minimal technical skills to do the above, this document introduces APIfy, a no-code Saas API management tool. APIfy’s intuitive drag-drop interface allows users to create, test, deploy and monitor APIs without writing a single line of code. Our methodology involved developing a full-stack web application using a Node.js Backend, Next.js Frontend and MongoDB atlas for a scalable. Lightweight application. This platform significantly reduces development and delivery time, along with reducing costs by automating and simplifying processes while providing users a purely drag-drop interface to setup their application’s backends
VidSense
No full textVidSense represents a comprehensive multimodal AI platform that leverages Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and advanced Natural Language Processing techniques to extract intelligent insights from video content. The platform addresses the growing need for automated video content analysis in an era where video consumption has exponentially increased across educational, corporate, and entertainment domains. The project\u27s core innovation lies in its integration of multiple AI technologies: OpenAI Whisper for robust speech-to-text transcription, custom sentence transformers for semantic understanding, and RAG architecture with vector embeddings for context-aware video question-answering. The system supports both YouTube video URLs and local file uploads, making it versatile for various use cases. Key capabilities include automated generation of video summaries, extraction of key moments with timestamps, creation of highlight reels for social media, AI-powered podcast generation from video content, intelligent dubbing and subtitling systems, and comprehensive meeting minutes generation. The platform demonstrates significant advancement in multimodal AI by seamlessly bridging audio, visual, and textual content understanding. Experimental validation shows the system successfully processes videos in multiple languages, maintains temporal coherence in generated content, and provides contextually relevant responses to user queries. The RAG implementation enables precise information retrieval with timestamp accuracy, while the modular architecture ensures scalability and maintainability. Results indicate superior performance in content extraction tasks compared to traditional video analysis tools, with processing times optimized through algorithmic and LLM-based hybrid approaches
Vroo
No full textUniversity students in Pakistan face daily transportation challenges due to rising fuel costs, un- reliable public transit, and unstructured carpooling options. Informal platforms such as WhatsApp and Facebook groups are widely used, but lack essential features such as scheduling, safety protocols, and consistent pricing. To address these issues, our project introduces Vroo, a context-aware, university-focused ride-sharing application designed to optimize daily com- mutes while promoting cost savings and sustainability. Vroo provides a comprehensive solution through features such as verified university email- based authentication, dynamic fare estimation, real-time location tracking, and an AI-powered recommendation system. Our architecture follows an event-driven modular monolith pattern using Node.js, Flask microservices, and Firebase integrations for real-time communication and authentication. The core back-end services are hosted on the cloud, with persistent data stored in MongoDB Atlas and PostgreSQL. The ride-matching algorithm uses spatial (S2 cell-based route matching) and temporal con- straints to pair riders with compatible drivers, while also considering gender preferences, seat availability, and detour feasibility. The recommendation engine applies a content-based approach to rank matched drivers by analyzing route similarity, travel time preferences, reliability, ride frequency, user ratings, and previous successful matches, ensuring personalized and trustworthy suggestions. In addition, the fare estimation algorithm incorporates distance, time, fuel prices, and vehicle mileage for accurate cost calculation. The project also emphasizes usability with a mobile-first user interface featuring intuitive ride management and communication tools. Comprehensive test cases ensure reliability, security, and user satisfaction. The initial results of the prototype testing demonstrate a successful match of the ride and reduced CO2 emissions through shared travel. By addressing key gaps in the Pakistani carpooling ecosystem, Vroo offers a scalable, se- cure, and user-centric commuting solution aligned with global environmental and technological standards
ETL/ELT Pipeline – Dataflow
No full textToday’s organizations deal with huge amounts of data every day, and they need efficient systems to move, clean, and organize that data for analysis. But in many cases, the data pipelines in place are rigid and overloaded with manual coding and scripting. This not only makes the process slow and tedious but also leaves room for inefficiencies and delays. Our project introduces a Hybrid ETL/ELT Data Processing Pipeline that provides manual control with intelligent recommendations. The system analyzes metadata such as data size, structure, and transformation complexity to recommend the best strategy (ETL or ELT), while users retain full control over the final choice. Our platform supports multiple relational database sources and destinations (MySql, PostgresSql, MS SqlServer) alongside AWS S3 buckets for data lakes, integrates AI agents to suggest warehouse/mart schemas, and enables users to build complete data pipelines through an interactive UI, with the option to fine-tune them using SQL queries. It’s built with React on the frontend, .NET and Python microservices on the backend, and packaged with Docker for easy cloud deployment. This hybrid setup boosts performance, offers greater flexibility, and makes the entire data integration process more transparent and user-friendly
SocialBrain
No full textIn today’s fast-paced digital environment, maintaining a consistent and relevant social media presence is a demanding task for individuals, brands, and marketing teams. Traditional content planning tools fall short in automating ideation, adapting to real-time trends, and unifying scheduling across platforms. To address these challenges, we present Social Brain—an AI-powered tool designed to automate the content ideation pipeline using generative models, trend analysis, and intelligent scheduling. Social Brain leverages real-time social data and large language models (LLMs) to generate trend-aligned content ideas, suggest suitable media, and recommend optimal posting times. The system architecture is modular and microservice-based, comprising a React frontend, a Node.js backend, and a FastAPI-driven AI engine. The tool integrates prompt engineering, keyword extraction, image generation, and content scheduling into a unified workflow. While LLMs currently supported include GPT-4, image suggestions are powered through tools like DALL·E, offering an end-to-end creative experience for content creators. We propose a framework that prioritizes user ease-of-use, scalability, and intelligent automation. The system has been tested under controlled conditions with simulated inputs, evaluating the effectiveness of prompt chains, keyword relevance, and scheduling accuracy. Results indicate substantial reductions in manual effort while improving engagement potential through timely and context-aware content suggestions. Social Brain introduces a novel intersection between trend forecasting, generative AI, and content management. It offers a powerful, extensible foundation for future work in automated digital marketing, social media analytics, and creator-focused AI tooling
Regulating global Sukuk
No full textThe global Sukuk market, valued at over USD 1 trillion, continues to expand as an attractive alternative to conventional bonds. Addressing industry concerns, the Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) has introduced a new Shariah standard on Sukuk. The standard aims to enhance investor confidence, ensure compliance, and promote transparency. Sukuk represents ownership in tangible assets or business ventures rather than debt, playing a vital role in infrastructure development and sustainable investment. While the standard sets clear guidelines on asset ownership, legal frameworks, and risk-sharing principles, it has sparked discussions regarding its potential impacts on market flexibility and legal adaptations. The initiative is seen as a positive move toward strengthening global Sukuk governance and aligning it with both Shariah principles and international market practices
Social Media Marketing Activities as a Catalyst for Sustainable Fast Fashion: A Study of Brand Love and Consumer Buying Intention
No full textThis study examines the impact of customer community identification, engagement, and brand love on purchase intention in the context of online sustainable apparel, emphasizing the role of social media marketing (SMM) in Pakistan. Data were collected from 405 Pakistani consumers using a purposive sampling method. Respondents, who purchase clothing via social media, completed a structured online questionnaire. Structural equation modelling was used for data analysis. SMM activities significantly enhance customer engagement. Community identification positively influences engagement, which strongly affects brand love. Brand love, in turn, significantly drives purchase intention. The study contributes to limited research on sustainable fashion and SMM in emerging markets, particularly Pakistan. Sustainable fashion brands can increase customer loyalty and purchasing behavior by fostering community identity and emotional connection through targeted SMM strategies
Understanding the New Coffee Concept in the Urban Centers
No full textThrough our Experiential Learning Project (ELP) with Tapal, one of Pakistans top fast-moving consumer goods firms, we got the chance to study and understand the changing consumer bheaviour and preferences in the coffee market and provide a strategy for the brand according to the consumer research insights.
During stage one we focused on primary research, running focus groups, in-depth chats, coffee-shop interviews, and retail store audits to see how customers behave, what they like, and what are gaps in the current market. The main takeaway was simple: people drink coffee for a energy to get them through the day and companionship. The consumer are asking for options that don\u27t cost alot, are convenient, and come in tasty flavors. To ease fears about Tapals tea image, we proposed a strategy including a shadow-endorsement plan rooted in behavioral theory and brand-extension thinking.
In phase two we crafted a full-market launch strategy that focus on 2 product format under a new sub-brand-Brew Coffee, proposed the pricing according to the consumer insights and outlined a multi-channel campaign. This research and its insights allowed us to craft an efficient marketing plan for the brand without diluting it\u27s main taget audience and core association and in the same time enter the coffee market efficiently.
This ELP project is a perfect example of how consumer based research can help a brand expand into new categories and introduce new products without bringing a damage to its current positioning according to the research insights
Lead Generation Support Toolkit and Inquiries Management Website
No full textThis Experiential Learning Project (ELP) was developed in collaboration with Toyota Indus Motors (IMC), one of Pakistan’s leading automobile manufacturers and distributors. The purpose of the project was to create a centralized, dynamic sales and lead tracking dashboard that integrates input from 19 Toyota dealerships across Karachi into a live reporting system for Indus Motor Company. The dashboard was envisioned as a scalable, digital alternative to the outdated, Excel-based monthly reports that Toyota dealerships previously submitted via email. The first part of the project involved the creation of two web-based dashboard interfaces: a dealership-facing website for daily data entry and an admin dashboard for Toyota Indus to monitor activity. Each dealership could log their sales funnel data (categorized by vehicle variant, lead source, and lead temperature: cold, warm, hot) on a daily basis. The admin site then aggregated this information in real time and provided IMC with multiple reporting features—filtered views by time period (day, week, month, quarter, year), dealership, and vehicle variant—as well as comparison features to evaluate performance across any two time slices. These reports are downloadable in PDF and Excel formats, which enhances usability. The second phase of the project focused on analyzing gaps in Toyota’s current lead generation and sales conversion pipeline. Based on client discussions and industry insights, we identified opportunities for Toyota to introduce a corporate CRM layer, standardize lead definitions across dealerships, integrate service-to-sales conversion programs, and leverage rejected financing applicants through bank collaborations. These recommendations formed the basis of a roadmap for IMC to scale its digital transformation beyond dashboards into a complete lead management ecosystem. This project aligns directly with SDG 9, (Industry, Innovation and Infrastructure), by making digital infrastructure for major industrial player. The thought leadership area it fits in is Entrepreneurship and Innovation as the project involved the end-to-end design of a scalable user-centric digital system to improve business processes. Our work with Toyota Indus not only improved operational visibility but also set the stage for broader CRM and data-driven decision-making capabilities within the organization