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Upgrading legacy systems has become increasingly important to stay competitive in today’s market as outdated infrastructure can cost organizations time, money, and market position. However, modernization efforts face challenges like time-consuming architecture reviews, complex migrations, and fragmented systems. These delays not only impact engineering teams but have broader impacts includingContinue Reading

Organizations across industries face challenges with high volumes of multi-page documents that require intelligent processing to extract accurate information. Although automation has improved this process, human expertise is still needed in specific scenarios to verify data accuracy and quality. In March 2025, AWS launched Amazon Bedrock Data Automation, which enablesContinue Reading

As organizations seek to derive greater value from their AWS Support data, operational teams are looking for ways to transform raw support cases and health events into actionable insights. While traditional analytics tools can provide basic reporting capabilities, teams need more sophisticated solutions that can understand and process natural languageContinue Reading

Organizations across various sectors face significant challenges when converting meeting recordings or recorded presentations into structured documentation. The process of creating handouts from presentations requires lots of manual effort, such as reviewing recordings to identify slide transitions, transcribing spoken content, capturing and organizing screenshots, synchronizing visual elements with speaker notes,Continue Reading

Vehicle data is critical for original equipment manufacturers (OEMs) to drive continuous product innovation and performance improvements and to support new value-added services. Similarly, the increasing digitalization of vehicle architectures and adoption of software-configurable functions allow OEMs to add new features and capabilities efficiently. Sonatus’s Collector AI and Automator AIContinue Reading

Data is your generative AI differentiator, and successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Traditional data architectures often struggle to meet the unique demands of generative such as applications. An effective generative AI data strategy requires several key components like seamlessContinue Reading

Extracting information from unstructured documents at scale is a recurring business task. Common use cases include creating product feature tables from descriptions, extracting metadata from documents, and analyzing legal contracts, customer reviews, news articles, and more. A classic approach to extracting information from text is named entity recognition (NER). NERContinue Reading

What if you could replace hours of data analysis with a minute-long conversation? Large language models can transform how we bridge the gap between business questions and actionable data insights. For most organizations, this gap remains stubbornly wide, with business teams trapped in endless cycles—decoding metric definitions and hunting forContinue Reading

In Part 1 of this series, we explored how Amazon’s Worldwide Returns & ReCommerce (WWRR) organization built the Returns & ReCommerce Data Assist (RRDA)—a generative AI solution that transforms natural language questions into validated SQL queries using Amazon Bedrock Agents. Although this capability improves data access for technical users, theContinue Reading

Businesses often face challenges in managing and deriving value from their data. According to McKinsey, 78% of organizations now use AI in at least one business function (as of 2024), showing the growing importance of AI solutions in business. Additionally, 21% of organizations using generative AI have fundamentally redesigned theirContinue Reading