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Using AI to Help Process Documents in Insurance
The insurance industry has billions of historical documents; with hundreds of thousands more being generated every day. The documents, in varying formats are used both internally and with other insurers to agree terms, assess risk and create accurate quotes. Historically each document can take a few hours or even days to manually process and needs to be loaded into each company’s systems.
AI is helping several companies to reduce this processing time to minutes by automating the process using Intelligent Document Processing (IDP), saving time, increasing the accuracy, and readying the data for further analysis – giving valuable insights back to the business. IDP uses the latest AI services; Amazon’s Bedrock, Textract to extract text, Amazon Comprehend to classify and detect entries in the documents and custom models with labelled grown truth in SageMaker. This architecture will look at these services alongside the pre-processing, processing, and post processing challenges and showcase how to jointly leverage these services for the best success.
Using IDP, one customer achieved an accuracy of 90%+ and a more than 500x reduction in processing time across over £500 million worth of business
AI is helping several companies to reduce this processing time to minutes by automating the process using Intelligent Document Processing (IDP), saving time, increasing the accuracy, and readying the data for further analysis – giving valuable insights back to the business. IDP uses the latest AI services; Amazon’s Bedrock, Textract to extract text, Amazon Comprehend to classify and detect entries in the documents and custom models with labelled grown truth in SageMaker. This architecture will look at these services alongside the pre-processing, processing, and post processing challenges and showcase how to jointly leverage these services for the best success.
Using IDP, one customer achieved an accuracy of 90%+ and a more than 500x reduction in processing time across over £500 million worth of business
A Hitchhiker’s Guide to Nitro
A large range of EC2 instance families and AWS services make use of AWS’ custom-built hypervisor, AWS Nitro. Learn what it is, how we ended up there and what advantages it has for your workloads in this whistle stop tour, featuring special guests, such as dom0
Advanced Event-Driven Patterns with Amazon EventBridge
Asynchronous communication and event-driven architecture are core to building distributed applications. Amazon EventBridge, a fully-managed serverless event bus, enables coordinating distributed and loosely coupled microservices to accomplish business functionality.
In this session, understand the characteristics of EventBridge and how it plays a pivotal role in serverless architectures. Learn the primary elements of event-driven architecture and some of the best practices.
With real-world use cases, explore how the features of EventBridge support implementing advanced architectural patterns in serverless.
In this session, understand the characteristics of EventBridge and how it plays a pivotal role in serverless architectures. Learn the primary elements of event-driven architecture and some of the best practices.
With real-world use cases, explore how the features of EventBridge support implementing advanced architectural patterns in serverless.
My AI Coding Partner: Building the London Improv Calendar on AWS
What happens when you treat AI like a coding partner? In this talk, I’ll share how I used AI tools to build the London Improv Calendar – a fully serverless application on AWS (Lambda, DynamoDB, API Gateway, and more).
This isn’t theory; it’s a practical, in-the-trenches account of working with AI as a solo engineer. We’ll cover what worked well, what fell flat, and where AI truly accelerated development.
If you’re curious about pairing AI with serverless, or just want some real-world lessons you can apply to your own projects, this session is for you.
This isn’t theory; it’s a practical, in-the-trenches account of working with AI as a solo engineer. We’ll cover what worked well, what fell flat, and where AI truly accelerated development.
If you’re curious about pairing AI with serverless, or just want some real-world lessons you can apply to your own projects, this session is for you.
From Prototype to Production with Strands Agents and Amazon Bedrock AgentCore
AI agents are a new class of software applications that use AI models to reason, plan, act, learn, and adapt in pursuit of user-defined goals with limited human oversight. Building AI agents that can reliably perform complex tasks has become increasingly accessible thanks to open source frameworks like Strands Agents. However, moving from a promising proof-of-concept to a production-ready agent that can scale to thousands of users presents significant challenges.
Through hands-on demos, we’ll build a system from scratch and progressively deploy it to production using the comprehensive enterprise-grade services provided by AgentCore.
You’ll learn to implement key production capabilities, including secure session isolation, persistent memory, identity management, and real-time observability. The learnings can be applied to any framework and model, hosted on Amazon Bedrock or elsewhere.
Through hands-on demos, we’ll build a system from scratch and progressively deploy it to production using the comprehensive enterprise-grade services provided by AgentCore.
You’ll learn to implement key production capabilities, including secure session isolation, persistent memory, identity management, and real-time observability. The learnings can be applied to any framework and model, hosted on Amazon Bedrock or elsewhere.
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