Case studies that speak for themselves

The Challenge

PixelBotStock needed a scalable stock image marketplace that could handle a large and growing image library without depending on manual tagging or slow search. For a platform with thousands of assets, manually writing titles, descriptions, categories, and keyword tags becomes time-consuming and difficult to maintain. The client also needed a fast search experience and a background processing system that could resize images, generate watermarks, enrich metadata, and index assets without slowing down the platform.

Our Solution

We engineered a full-stack, service-based stock image platform using Next.js, Laravel, PostgreSQL, Meilisearch, and OpenAI. The system automatically processes every uploaded image through a background queue worker. Each image is resized, compressed, watermarked, analysed by OpenAI, enriched with SEO-friendly titles, descriptions, tags, and category suggestions, then indexed into Meilisearch for instant discovery. The frontend was deployed on Vercel for fast global performance, while Laravel handled the API, admin panel, subscriptions, licensing logic, and image workflow orchestration.

Results Achieved

Tech Stack

The Challenge

Laundry Baba had customer and order data stored in Firebase, but they were not using it properly to understand customer behaviour, ordering patterns, or sales trends. Without clear insights, it was difficult for the client to plan better offers, identify repeat customers, or make data-backed marketing decisions.

Our Solution

We created an interactive Tableau dashboard integrated inside a Python-based admin UI. The dashboard was designed specifically to analyse customer purchase behaviour, order frequency, sales trends, and marketing opportunities. Data was fetched from Firebase and presented in a simple, visual format so the client could easily understand what was happening in the business and take action.

Results Achieved

Tech Stack

The Challenge

A growing business was running marketing campaigns across multiple channels, including Google Ads, Meta Ads, email campaigns, organic social, and promotions. However, the team did not have a clear view of which channels were actually driving sales, which campaigns were wasting budget, and how future marketing spend could impact revenue. Most decisions were based on basic platform reports, which made it difficult to understand the real contribution of each channel.

Our Solution

We developed a data science solution using Marketing Mix Modelling and machine learning to analyse campaign performance, customer response, and sales impact. Historical marketing spend, impressions, clicks, conversions, promotions, seasonality, and revenue data were combined into one clean dataset. Marketing Mix Modelling was used to estimate the contribution of each channel, while machine learning models were built to predict future campaign performance and sales outcomes based on different budget scenarios.

Results Achieved

Tech Stack

The Challenge

Laundry Baba - A professional laundry business was managing customer orders through WhatsApp, where customers sent messages and photos manually. As the business grew, this process became difficult to manage. Orders, pricing, delivery updates, customer details, and communication were scattered, creating confusion for both the team and customers.

Our Solution

We developed a complete mobile app ecosystem to move their business from manual WhatsApp-based operations to a fully digital app-based model. The solution included three applications: one for customers, one for delivery agents, and one admin panel to manage the entire business. Through the admin app, the client could control customer accounts, pricing, laundry orders, delivery timelines, agents, and key business KPIs from one place.

Results Achieved

Tech Stack