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Knowledge bases that ground agents and generative AI applications over your enterprise data are hard to build at scale. Teams typically stitch together connectors, parsers, vector stores, knowledge graphs, and retrieval logic, then operationalize all of it for production. Each piece brings its own challenges. You must decide which data sources to connect and how […]

This post is co-written with Eric Jiang from xAI (SpaceXAI). xAI’s Grok 4.3 is now generally available on Amazon Bedrock, giving teams that build agents and AI workflows a model that reasons reliably over long inputs. With this launch, xAI joins Amazon Bedrock as a model provider. Grok 4.3 is a model with configurable reasoning

You’ll be able to securely link and interact with your go-to services directly in AI Mode. ​ 

Gemini Omni and personal avatars in Google Vids make video creation easier than ever. ​ 

Restaurants miss an average of 150 phone calls per location every month, and about 60 percent of those are customers trying to place an order or book a table. Most of these calls come in during dinner service, exactly when the host is seating guests, servers are turning tables, and the phone becomes an afterthought.

Document processing in real estate is complex and highly manual, impacting critical business decisions at scale, making it ripe for automation. Built Technologies, a real estate finance software provider, processes over $500B in real estate projects. The company deployed an AI-powered document processing engine on Amazon Bedrock and the AWS Intelligent Document Processing (IDP) Accelerator.

The integration of AI into real-world applications has long been hindered by a fundamental challenge: the disconnect between systems that can see, systems that can think, and systems that can act. Developers have struggled with complex integrations, managing multiple APIs, and creating custom solutions to bridge these gaps, resulting in inefficient, costly, and often fragile

Using Amazon SageMaker Pipelines, organizations can automate their machine learning (ML) workloads and distribute them over many AWS accounts and AWS Regions as part of their Machine Learning Operations (MLOps) strategy. However, monitoring SageMaker Pipelines can become complex when they are distributed across many AWS environments. Developers and operations engineers must manually switch between multiple

Your prospects leave trails across multiple sources: a founder asks “What should I use for X?” in r/SaaS while their product launches on Hacker News. Stack Overflow questions spike. A GitHub repo crosses 2,400 stars. Each signal alone is noise, but correlated across sources, they reveal a prospect ready to buy. Multi-agent systems built with

Production quality assurance (QA) workflows require more than individual test execution. You must organize tests into regression suites that run as a batch, and integrate them into continuous integration and continuous delivery (CI/CD) pipelines so that test results gate deployments automatically. In a previous post, we introduced QA Studio, a reference solution for agentic QA