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Presenting our case studies
Discover how AlgoLabs has been revolutionising industries through AI and ML solutions. These diverse case studies are testament to our expertise in transforming real-world challenges into success stories.
Customer Service Automation
Companies in the customer service sector often face issues due to lack of uniform data entry processes, different text formats and poor data quality. AlgoLabs employed customized ML and distance algorithms to identify candidate matches resulting in proper entity matching and the automation of routine customer service tasks.
Performance Anomaly detection in Micro Service Architecture-based Cloud Applications
Fraud/Anomaly Detection in cloud applications is often tricky and laborious. The graphical & causal search approach developed by Algolabs allowed detection of unusual spending patterns, strange patterns in network traffic and outliers. We have observed precision and recall over 80%.
Fault Prediction for Enterprise Order Provisioning System
Enterprise Order Provisioning System required technologies that predicted the failure of equipment and applications. Algolabs developed a health dashboard for key parameters using Multivariate Regression Model. We helped prevent the need for major repairs, extend the life of assets, reduce maintenance costs, enhance safety and productivity and prevent delays.
Optimising Marketing Spend for an E-commerce Company
Agolabs used the Bayesian Optimization Model to evaluate the ROI and inform optimal allocation of the marketing budget across multiple channels, geographies and product categories.
Dynamic Pricing for a Fashion Merchandise Company
A fashion merchandise company needed to maximise merchandise sales and profits. Algolabs came up with a hierarchical Bayesian approach for predicting price elasticity for products based on market demand and markdown, and integrated this solution with customer’s framework through R model function call. This improved ROS by 15%.
Computer Vision-based Image Processing for Crop Yield Analysis
Manual counting in precision farming leads to inaccuracies and poor productivity. Algolabs combined the use of drones for aerial image analysis, deep learning models for prediction and enhanced image processing techniques to perform digital count of corn plants. Around 90% accuracy was achieved.
Automatic Mental Health Evaluation System for Effective Diagnosis
AlgoLabs’s Convolutional Neural Network model utilises speech signals from therapy sessions to predict the presence of depression and assess the severity of symptoms with 80% accuracy.
Risk & Compliance Automation of Client’s Travel Expense System
The client is a multinational IT company who had a travel expense system for employees to submit their expenses. Due to the large size of the organisation, the auditors had to verify all the claims manually. AlgoLabs developed an ‘AI-based Auditor’ that used statistical and machine learning models to analyse transactional expense data and classify them as genuine or fraudulent, reducing the auditing time by more than 50%.
Marketing Budget optimization for a Telecom company
The client is a telecom company in a developing market who needed support to devise a loyalty program to give incentives to prepaid mobile phone retailers in such a way that it would help them attract more customers without affecting profitability. AlgoLabs applied statistical and machine learning techniques to predict loyalty, optimal target, assign nearest target band to predicted target and check the achievement and minimise the gap between Target pay-out and achievement pay-out.
Quality Prediction & Optimisation for a Tyre Manufacturer
The properties of rubber – the raw material in the tyre manufacturing industry – keeps changing based on source of procurement, and could impact the quality of the end tyres. AlgoLabs worked with the client team to help them code a model that would predict the end product’s viscosity, and further enhanced the model to predict the quality of output of each batch process in the industry based on that process’s input quality. We realised the benefit of higher control over final product quality and reduced variation due to changes in raw material.