A global leader in energy technology needed to modernize its data platform. The existing infrastructure was hindering efficiency, leading to high costs and sluggish time-to-insights. Additionally, data governance needed to be scaled to ensure data integrity and responsible utilization across the organization.

Space partnered with the client to develop a comprehensive two-year roadmap for data platform transformation. We identified key initiatives with clear dependencies and prioritization, addressing elements

• Data Platform Modernization
• Data Platform Architecture
• Cost Optimization

A leading pharmaceutical company's Research and development was impeded by fragmented data, inefficient data management, and slow insights.

By creating a robust and scalable data platform, we empowered the client to make data-driven decisions, accelerate research, and ultimately bring life-saving therapies to patients faster. This case study demonstrates our commitment to delivering innovative data solutions that drive positive impact in the healthcare industry.

• Research and Development
• Data Platform Architecture
• Efficiency

Life sciences and pharmaceutical companies are overwhelmed with data from various sources – clinical trials, research papers, market reports, regulatory filings, and more. Extracting meaningful insights from this data deluge is a significant challenge. Manual analysis is time-consuming, prone to errors, and often requires specialized expertise.

Our cloud-based DaaS platform offers on-demand access to curated, analyzed, and ready-to-use data tailored for the life sciences industry. It empowers informed decision-making by transforming raw data into actionable insights, freeing up valuable time and resources for research, development, and strategic initiatives. Key features include unified data access from diverse sources, AI-powered data curation using advanced NLP algorithms, and on-demand data analysis with customizable tools.

• Data-driven insights
• Real-time Inference
• Customizable Analytical Tools

A leading retail giant faced significant hurdles in scaling its deep learning models to handle the immense volume of product images and customer data generated daily. The complexity of managing infrastructure, coupled with the need for rapid model iteration, hindered the company's ability to innovate and gain a competitive edge.

To overcome these challenges, Space implemented a robust MLOps platform that democratized access to deep learning models and streamlined the entire model lifecycle. Our solution leveraged Azure Kubernetes Service (AKS) to create a scalable and cost-effective environment for model development, training, and deployment.

• MLOps
• Deep Learning
• Scalability