We are working with a major organisation to implement comprehensive data infrastructure and advanced analytics capabilities. The project encompasses data engineering, data science implementation, and strategic alignment of business objectives with data infrastructure. Key focuses include centralising analytics operations, establishing robust data governance, and enhancing executive decision-making through predictive analytics.
We are collaborating with a growing company to develop comprehensive KPIs, performance metrics, and predictive models. The project culminates in a predictive insights dashboard to drive business decisions.
We are developing a full-stack expense claims application for the NUS Department of Statistics and Data Science using WASP, Django and React, which demonstrates our technical capabilities in modern application development.
Engineered an advanced ML pipeline for real-time risk assessment in auto insurance. The system processes diverse data streams including customer profiles, historical claims, and policy information to generate instant risk evaluations, leading to more accurate pricing and reduced fraud exposure.
Developed and deployed machine learning models for early risk detection in neonatal care settings. The AI system processes real-time vital signs and clinical indicators to predict potential complications, enabling proactive intervention protocols.
Designed and implemented a scalable data architecture for managing complex clinical trial data. The system handles multi-source medical data integration while ensuring HIPAA compliance and data governance standards.
Engineered an NLP-driven analytics platform that processes customer feedback across multiple channels. The system performs:
Architected a comprehensive data pipeline for maritime emissions monitoring:
The platform supports environmental compliance while providing actionable insights for operational efficiency.
Contact us to discuss how DACC can support your organisation’s data engineering and AI needs.