Case Studies

Detailed overview of some major projects, we delivered with success.

Adrigo Insights - Social Media Intelligence

Adrigo Insights is a revolutionary social media intelligence platform designed to empower businesses, organisations, and individuals with actionable insights derived from social media data. Leveraging cutting-edge Graph APIs, Adrigo Insights seamlessly collects, analyses, and visualises data from popular social media platforms like Facebook, Instagram, and YouTube. Its mission is to equip users with the tools they need to make informed decisions, elevate their social media strategies, and achieve tangible results in an increasingly digital world.

Multi-Instrument Music Generation

The project aimed to use deep learning techniques to generate multi-instrumental music compositions by combining LSTM, BiLSTM, and GRU layers in a hybrid neural network. Python and Keras were chosen for flexibility, and a dataset of 400 MIDI files was used for training. The model featured an intricate architecture with variable-length notes, multiple instrument options, and dynamic tempo control. Impressive results were achieved, with a 98% accuracy rate and minimal loss, showcasing the team's expertise in advanced technologies and their commitment to innovation in music generation.

Student Procrastination Solver and Support Chatbot

The project aimed to combat student procrastination by creating an AI chatbot that blends emotional support with task management. Leveraging the OpenAI GPT-3.5 model and Python, the chatbot offers empathetic responses and helps users break tasks into manageable steps. It respects user privacy and maintains a conversational interface. The innovative fusion of emotional support and practical task management has garnered high user satisfaction and showcases a commitment to addressing procrastination challenges effectively.

Request Resolution Time Prediction through Clustering Analysis

The project aimed to enhance a Customer Relationship Management (CRM) system by predicting request resolution times through clustering analysis. Using K-means clustering and Python, service requests were grouped based on various attributes, allowing for efficient resource allocation and improved operational efficiency. This innovative approach enabled data-driven decision-making, leading to better customer satisfaction. The project's success expanded expertise in data analytics and predictive modeling, reaffirming a commitment to delivering exceptional results through excellence and innovation.

Customer Segmentation for

"Customer Segmentation for Enhanced Customer Lifetime Value Analysis" project utilises RFM marketing techniques to deeply analyse customer data, focusing on major services. With a powerful tech stack including Python, PowerBI, and more, we performed RFM and LTV cluster analyses, churn prediction, and created an overall customer score. This project enhances customer relationships, optimises resource allocation, reduces churn, and empowers data-driven decisions, ultimately providing us with a competitive edge.

CRM Data Analysis and Visualisation for Enhanced Decision-Making

In the "CRM Data Analysis and Visualisation for Enhanced Decision-Making" project, a comprehensive analysis of five years of CRM data, from May 2018 to 2023, was conducted. By utilising Power BI, a refined dataset of 444,619 rows and 45 columns was meticulously processed and visualised. This powerful tool facilitated efficient data management, advanced visualisations, and real-time insights, enabling better resource allocation, service optimisation, and improved customer satisfaction. The project successfully merged data analytics and visualisation, innovatively harnessing Power BI's capabilities for a resounding success in CRM analysis.

CarMod Background Transformer

The CarMod Background Transformer project sought to revolutionise automotive imaging by introducing a sophisticated solution for seamlessly removing and replacing backgrounds in car images. Through the strategic integration of advanced image processing techniques, including the rembg library, GFPGAN (Generative Face Parsing GAN) for image restoration and threshold-based cut operations, the project aimed to set a new standard for presenting car modifications on websites.