Enterprise Case Study

Supertype use continual deep learning models on historical data & real-time water level sensors to elevate Adaro's mining logistical operations, enabling a machine learning model to assist in the decision making process that results in a cost savings of USD 2 million annually.

The design and implementation of a comprehensive predictive maintenance system that predicts equipment failure and maintenance schedules, reduce unplanned downtime, and reduce reactive maintenance — the end result is a maintenance cost savings of up to USD 1.2 million annually for each type of heavy equipment across its fleet of mining equipment, and a health index that scores each equipment's health status.

Supertype embarked on building an ambitious project to completely revolutionize analytics creation for the Indonesia Stock Exchange, incorporating automatic agents that generate beautiful visual-heavy PDF and PowerPoint reports with end-to-end automation. IDX put the estimates of analytics creation time savings at 95%, shaving 450 hours of analytics laborfrom their skilled workforce while increasing the accuracy of stock market reporting from 85% to 99.5%.

Supertype build an audience segmentation and lookalike creation model for Opera, one of the world's largest browser company, thus enabling the publisher to generate user segments for their advertisers. By training on a 256-dimensional device level embedding, we were able to achieve an accuray of 99.57% on 10 million raw data in Opera's internal and external benchmarks.

The e-commerce category leader in Singapore adopts Supertype's Enterprise Analytics model to revamp its data infrastructure, diagnose its e-commerce tracking leakage, and through the help of our engineers, develop a series of innovations that monitor and optimize the prices of over 300+ SKUs (Stock Keeping Units) that take into account marketplace demand factors, seasonality, and competitor pricing. Equivalent work by a team of 3 analysts would have cost 3kraters USD 150,000 annually.

Supertype develop a deep learning model to help Creadits — a global creative agency — quantify the creative decay process of their digital assets, and mathematically model when ad creatives plateau in performance. The insights these models are able to yield lead to Creadits producing ad creatives that are 40% more performant than industry peers in its benchmarks.

Supertype develops an API service that integrates neatly into Zoomd's data infrastructure and intelligently score each device based on their likelihood to respond to a campaign offer and reduce advertising waste by 60% while yielding a lift in response rate of up to 23%.

Supertype's engineers works with Toyota-Astra Motor's data teams to implement an ETL pipeline that closely integrates with their Azure Blob Storage and Azure Machine Learning Services, processing more than 2,000,000 automobile sales in a custom-built recommendation engine that saves the company upward of USD 240,000 annually in manual data preparation costs.