EPAM Systems (EPAM), TGS Collaborate to Accelerate AI Adoption in Energy Sector
Key takeaways
- EPAM Systems (EPAM), TGS Collaborate to Accelerate AI Adoption in Energy Sector Maham Fatima Sun, June 28, 2026 at 12:29 AM GMT+7 2 min read EPAM EPAM Systems Inc.
- These tools provide a unified environment that democratizes access to complex data and supports end-to-end subsurface analysis.
- This initiative positions TGS to become an AI-native geoscience firm by combining its energy intelligence with AWS's computational scale and EPAM Systems Inc.'s (NYSE:EPAM) cloud engineering expertise.
EPAM Systems (EPAM), TGS Collaborate to Accelerate AI Adoption in Energy Sector Maham Fatima Sun, June 28, 2026 at 12:29 AM GMT+7 2 min read EPAM EPAM Systems Inc. (NYSE:EPAM) is one of the underperforming tech stocks to buy according to analysts. On June 8, EPAM Systems and TGS announced a collaboration to accelerate AI adoption in the energy sector, headlined by the successful migration of TGS Imaging Any Ware to AWS. By using cloud-native infrastructure, this platform allows energy companies to process petabyte-scale seismic data faster and more cost-effectively than traditional on-premises systems, removing critical bottlenecks in exploration and production.
The collaboration centers on three core innovations: TGS Data Verse for secure, OSDU-compliant subsurface data access; cloud-native seismic imaging that uses AWS Graviton and Spot instances for enhanced performance; and EPAM's Energy HPC Orchestrator/EHO for modular, AI-enabled workflows. These tools provide a unified environment that democratizes access to complex data and supports end-to-end subsurface analysis.
This initiative positions TGS to become an AI-native geoscience firm by combining its energy intelligence with AWS's computational scale and EPAM Systems Inc.'s (NYSE:EPAM) cloud engineering expertise. The partnership provides energy operators with a more agile, high-performance infrastructure, enabling them to move from raw data to discovery more efficiently while reducing the capital intensity of large-scale computational projects.