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This post is co-written with Julieta Rappan, Macarena Blasi, and MarÃa Candela Blanco from the Government of the City of Buenos Aires. The Government of the City of Buenos Aires continuously works to improve citizen services. In February 2019, it introduced an AI assistant named Boti available through WhatsApp, the most widely used messaging service […]
Finance analysts across Amazon Finance face mounting complexity in financial planning and analysis processes. When working with vast datasets spanning multiple systems, data lakes, and business units, analysts encounter several critical challenges. First, they spend significant time manually browsing data catalogs and reconciling data from disparate sources, leaving less time for valuable analysis and insight
Most organizations evaluating foundation models limit their analysis to three primary dimensions: accuracy, latency, and cost. While these metrics provide a useful starting point, they represent an oversimplification of the complex interplay of factors that determine real-world model performance. Foundation models have revolutionized how enterprises develop generative AI applications, offering unprecedented capabilities in understanding and
As data becomes more abundant and information systems grow in complexity, stakeholders need solutions that reveal quality insights. Applying emerging technologies to the geospatial domain offers a unique opportunity to create transformative user experiences and intuitive workstreams for users and organizations to deliver on their missions and responsibilities. In this post, we explore how you
Enhance Geospatial Analysis and GIS Workflows with Amazon Bedrock Capabilities Read More »
This post is the second part of the GPT-OSS series focusing on model customization with Amazon SageMaker AI. In Part 1, we demonstrated fine-tuning GPT-OSS models using open source Hugging Face libraries with SageMaker training jobs, which supports distributed multi-GPU and multi-node configurations, so you can spin up high-performance clusters on demand. In this post,
Fine-tune OpenAI GPT-OSS models using Amazon SageMaker HyperPod recipes Read More »
As an Amazon Web Services (AWS) enterprise customer, you’re probably exploring ways to use generative AI to enhance your business processes, improve customer experiences, and drive innovation. With a variety of options available—from Amazon Q Business to other AWS services or third-party offerings—choosing the right tool for your use case can be challenging. This post
Accelerate enterprise AI implementations with Amazon Q Business Read More »
Machine learning (ML) has evolved from an experimental phase to becoming an integral part of business operations. Organizations now actively deploy ML models for precise sales forecasting, customer segmentation, and churn prediction. While traditional ML continues to transform business processes, generative AI has emerged as a revolutionary force, introducing powerful and accessible tools that reshape
Enterprises across industries like healthcare, finance, manufacturing, and legal services face escalating challenges in processing vast amounts of multimodal data that combines text, images, charts, and complex technical formats. As organizations generate multimodal content at unprecedented speed and scale, document processing methods increasingly fail to handle the intricacies of specialized domains where technical terminology, interconnected
Every day, enterprises process thousands of documents containing critical business information. From invoices and purchase orders to forms and contracts, accurately locating and extracting specific fields has traditionally been one of the most complex challenges in document processing pipelines. Although optical character recognition (OCR) can tell us what text exists in a document, determining where
Benchmarking document information localization with Amazon Nova Read More »
To fulfill their tasks, AI Agents need access to various capabilities including tools, data stores, prompt templates, and other agents. As organizations scale their AI initiatives, they face an exponentially growing challenge of connecting each agent to multiple tools, creating an M×N integration problem that significantly slows development and increases complexity. Although protocols such as
