EPRI
Weather forecasting
Forecasts that took 10+ hrs on shared CPU clusters now run in 150 sec—giving grid planners time to act, not just react.
- 50-member weather forecast in 150 sec vs 10+ hrs on CPU
Context Windows brings together credible case studies on what companies are doing with AI, from across the open web. We compare their results so you can prioritise the use cases that are actually working.
Real-world implementations from
Forecasts that took 10+ hrs on shared CPU clusters now run in 150 sec—giving grid planners time to act, not just react.
Finance, supply chain, and field data scattered across cloud and on-prem systems—no unified view of profitability. One lakehouse, real-time.
Creditworthy but credit-invisible: millions of Colombians locked out. Graph ML mapped their behavior instead, making them legible to lending.
Across 120 markets, fixed dispatch couldn't track pricing shifts or personal goals. AI now adapts to each grid, each user, each goal.
Rent, phone bills, utilities paid on time—proof scattered across silos. Consolidated into AI profiles, the invisible became creditworthy.
Manually pulling flight risk data took half a day — so it never got done. Three words later, Rippling built a scoring rubric from scratch.
Context Windows brings together credible case studies on what companies are doing with AI, from across the open web. We compare their results so you can prioritise the use cases that are actually working.
Real-world implementations from
Forecasts that took 10+ hrs on shared CPU clusters now run in 150 sec—giving grid planners time to act, not just react.
Finance, supply chain, and field data scattered across cloud and on-prem systems—no unified view of profitability. One lakehouse, real-time.
Creditworthy but credit-invisible: millions of Colombians locked out. Graph ML mapped their behavior instead, making them legible to lending.
Across 120 markets, fixed dispatch couldn't track pricing shifts or personal goals. AI now adapts to each grid, each user, each goal.
Rent, phone bills, utilities paid on time—proof scattered across silos. Consolidated into AI profiles, the invisible became creditworthy.
Manually pulling flight risk data took half a day — so it never got done. Three words later, Rippling built a scoring rubric from scratch.