Category: FAANG

Rocket Close is a Detroit-based title agency and appraisal management company within Rocket Companies that provides title insurance, property valuation, and settlement services. As demand for mortgages and loans grew, title operations became a bottleneck in the homebuying process. Time-intensive, state-specific title examinations, combined with manual research and fragmented systems, slowed throughput and made it …

Read more “Building Supercharger: How Rocket Close optimized title operations with agentic AI”

Many companies have large volumes of paper or electronic documents that contain untapped business intelligence. With the advancement of generative AI, various large language models can be used to accurately extract relevant data from these documents. This post demonstrates an intelligent document processing pipeline that consists of both on-demand inference and batch inference options on …

Read more “Extract Data with On-demand and Batch Pipelines Dynamically”

Frontier teams are not just using AI to code faster. They’re redesigning how software gets built. The result is 4.5x productivity gains, in some cases more than 10x. Six engineers. Seventy-six days. A project scoped for 30 developers over 12 to 18 months, delivered within a quarter. That is not hypothetical. It’s what happened when …

Read more “How frontier teams are reinventing AI-native development”

Physical AI is moving from research into production. Robots are increasingly trained in high-fidelity simulation before being deployed to factories, warehouses, and logistics centers, because training in the real world is slow, expensive, and often unsafe, while GPU-accelerated simulation can compress months of learning into hours. This shifts the challenge to compute. Reinforcement learning (RL) …

Read more “Scale Robot Reinforcement Learning with NVIDIA Isaac Lab on Amazon SageMaker AI”

Editor’s Note: This is the fourth post in a series exploring how Palantir customizes infrastructure software for reliable operation at scale. The following is a guest contribution to the Foundations series from the Gotham Core Platform organization, which builds and maintains the bedrock for mission-critical applications within the Gotham ecosystem. This blog post by Kevin Liang, …

Read more “Managing Elasticsearch Reindex at Scale: Performance, Reliability, and Observability”

With access to the latest generative AI models and high-performance accelerated compute in high global demand, AWS customers need tools to take advantage of model availability and capacity across multiple AWS Regions, while still meeting their security and privacy requirements. cross-Region Inference (CRIS) on Amazon Bedrock meets these needs by automatically routing requests across multiple …

Read more “Unlocking AI flexibility in Europe: A guide to cross-region inference for EU data processing and model access”