# Customer Solutions Published articles for Customer Solutions. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale DevFeed: [Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale]() Original publisher: [Read original article]() Author: Aswin Vasudevan Published: 2026-09-14T21:22:45Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [threat detection](), [Amazon Bedrock](), [Security](), [Serverless](), [API](), [Amazon Web Services](), [AWS CloudTrail](), [Amazon CloudWatch](), [Amazon S3]() Tags: [advanced-300](), [agentic](), [agents](), [amazon-bedrock-agentcore](), [amazon-cloudwatch](), [amazon-s3](), [analysis](), [api](), [aws](), [aws-cloudtrail](), [code](), [customer-solutions](), [data](), [data-processing](), [security](), [serverless](), [threat-detection]() ### AI overview Abnormal AI uses Amazon Bedrock AgentCore Code Interpreter as an ephemeral, serverless compute scratch pad for real-time inline email threat detection. The article describes its sandbox isolation, networking and file-handling options, preloaded Python capabilities, observability integrations, and use at billion-message scale. ### Source excerpt Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ## How Ninth Wave built AI-powered open finance onboarding on Amazon Bedrock DevFeed: [How Ninth Wave built AI-powered open finance onboarding on Amazon Bedrock]() Original publisher: [Read original article]() Author: Shawn Kelly Published: 2026-09-14T15:58:57Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [Artificial Intelligence](), [API](), [Architecture & Design](), [Security](), [Finance](), [Amazon Web Services](), [data]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [apis](), [customer-solutions](), [fintech](), [onboarding](), [pci-dss](), [security](), [soc-2]() ### AI overview The article describes how Ninth Wave built Compass, a multi-agent AI onboarding assistant powered by Amazon Bedrock AgentCore. Compass helps financial institutions validate bank APIs, map fields to Financial Data Exchange standards, and score production readiness while supporting secure, compliant open finance connectivity. ### Source excerpt Learn how Ninth Wave built Compass, a multi-agent AI onboarding assistant on Amazon Bedrock AgentCore that validates bank APIs against Financial Data Exchange (FDX) standards, scores compliance, and compresses open finance onboarding from weeks to minutes while meeting SOC 2 and PCI DSS requirements. ## How AvioBook builds turnaround insights from operational data with Amazon Bedrock AgentCore DevFeed: [How AvioBook builds turnaround insights from operational data with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Petra Lafond Published: 2026-09-10T15:53:05Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [data](), [Software]() Tags: [agentic](), [agents](), [airline](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [analytics](), [apis](), [customer-solutions]() ### AI overview AvioBook prototyped Connected Analytics on Amazon Bedrock AgentCore to turn flight and ground-operations data into evidence-based answers about airline turnaround delays. ### Source excerpt AvioBook, a Thales Group Company, prototyped Connected Analytics on Amazon Bedrock AgentCore to turn AvioBook Connect's operational data into plain-language, evidence-based answers for airline managers and dispatchers, helping them find and act on the causes of flight turnaround delays. ## How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore DevFeed: [How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: JW Wang Published: 2026-09-09T18:11:12Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Claude](), [Orchestration](), [tracing]() Tags: [advanced-300](), [agent](), [ai](), [amazon-bedrock-agentcore](), [anthropic-claude](), [customer-solutions](), [identity](), [memory](), [observability](), [sandbox](), [tracing]() ### AI overview Heurist Finance uses Amazon Bedrock AgentCore to run a conversational investment workbench that accesses premium data per query and produces auditable, personalized research responses. ### Source excerpt Learn how Heurist built Heurist Finance, a conversational AI investment workbench, on Amazon Bedrock AgentCore. This customer story shows how AgentCore payments, Identity, Memory, Code Interpreter, and Observability let a small team buy premium market data per query, isolate analysis in a sandbox, and keep every action auditable. ## Gallup scales real-time coaching for thousands with Amazon Bedrock DevFeed: [Gallup scales real-time coaching for thousands with Amazon Bedrock]() Original publisher: [Read original article]() Author: Tamil Sambasivam Published: 2026-08-26T17:36:23Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Chat](), [Language models]() Tags: [amazon-bedrock](), [amazon-bedrock-knowledge-bases](), [anthropic](), [aws](), [claude](), [customer-solutions](), [generative-ai](), [retrieval-augmented-generation-rag](), [safety]() ### AI overview Gallup built a generative AI coaching assistant with Amazon Bedrock and Anthropic Claude models to provide leaders with real-time, personalized workplace guidance in Gallup Access. ### Source excerpt Gallup transformed 90 years of workplace science into Gallup AI, a generative AI assistant powered by Amazon Bedrock that delivers real-time, personalized coaching to leaders directly within the Gallup Access application. ## CORTO's billion-scale legal semantic search with Aurora PostgreSQL pgvector DevFeed: [CORTO's billion-scale legal semantic search with Aurora PostgreSQL pgvector]() Original publisher: [Read original article]() Author: Anisa Dean Published: 2026-08-26T16:40:00Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [AI search](), [tenant data protection]() Tags: [advanced-300](), [ai](), [amazon-aurora](), [architecture](), [compliance](), [customer-solutions](), [embedding](), [postgresql](), [postgresql-compatible](), [production](), [scale](), [search]() ### AI overview CORTO describes scaling legal semantic search on Amazon Aurora PostgreSQL with pgvector for billions of documents and vectors. The article focuses on embedding choices, multi-tenant isolation, cost efficiency, and sub-second query performance. ### Source excerpt How CORTO scaled Amazon Aurora PostgreSQL with pgvector to 7.6 billion vectors and 2.5 billion documents in production, delivering sub-second legal search for 10,000+ law firms at 75% lower storage cost. ## How Channel Corporation modernized their architecture with Amazon DynamoDB, Part 3: User and Badge DevFeed: [How Channel Corporation modernized their architecture with Amazon DynamoDB, Part 3: User and Badge]() Original publisher: [Read original article]() Author: Haibin (Binu) Lee Published: 2026-08-25T18:39:51Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [data](), [Amazon Web Services](), [migration](), [Amazon S3](), [Latency](), [App]() Tags: [advanced-300](), [amazon](), [amazon-dynamodb](), [app](), [architecture](), [aws](), [customer-solutions](), [data](), [latency](), [migration]() ### AI overview Channel Corporation describes how it addressed DynamoDB User table throttling by separating role-specific data, including Badge data, from an all-purpose User table. The article explains the effects of bursty badge writes, item-size-based write capacity consumption, and transaction processing, and introduces an online migration approach using DynamoDB Export and Import with Amazon S3 and AWS Glue. ### Source excerpt Channel Corporation shares how they split their all-purpose Amazon DynamoDB User table into role-specific tables, moving Badge data into a dedicated UserBadge table to stop transaction-conflict throttling and GSI back pressure, and how they ran a zero-downtime online migration using DynamoDB Export and Import with Amazon S3 and AWS Glue. ## How a global payment processor preserved AWS RAM shares and Lake Formation permissions during an AWS Organizations migration DevFeed: [How a global payment processor preserved AWS RAM shares and Lake Formation permissions during an AWS Organizations migration]() Original publisher: [Read original article]() Author: Sam Mukherjee Published: 2026-08-24T15:23:21Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AWS Lake Formation](), [migration](), [Amazon Web Services](), [Authorization](), [Software as a service]() Tags: [aws](), [aws-lake-formation](), [customer-solutions](), [enterprise](), [migration](), [payment](), [payments](), [resource-access-manager-ram](), [saas](), [us]() ### AI overview AWS describes a migration pattern for preserving AWS RAM shares and AWS Lake Formation permissions while moving 382 accounts between AWS Organizations. Temporary bridge shares maintain continuity during the move, after which the original shares are restored as durable permission objects. ### Source excerpt When AWS accounts move between organizations, organization-bound AWS RAM resource shares break and control-plane access is lost. Learn how a global payment processor used temporary bridge shares to preserve AWS Lake Formation permissions across a 382-account AWS Organizations migration, then restored the original shares as the durable source of truth. ## How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 2 DevFeed: [How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 2]() Original publisher: [Read original article]() Author: Muhammad Musab Iqbal Published: 2026-08-21T10:01:18Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [ai observability]() Tags: [advanced-300](), [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [architecture](), [autonomous](), [autonomous-agents](), [aws](), [aws-fargate](), [customer-solutions](), [observability](), [policy](), [sales](), [security](), [serverless]() ### AI overview AgentFlo's second architecture post explains how it uses Amazon Bedrock AgentCore and AWS serverless components to operate AI sales agents with layered safeguards. It emphasizes controls before requests, during tool execution, and after responses, while reporting an early 12% net-revenue uplift. ### Source excerpt Part 2: how AgentFlo built trusted, reliable AI sales agents on Amazon Bedrock AgentCore and AWS serverless architecture. Learn the three-layer guardrails, grounded data foundation, and end-to-end observability behind a +12% net revenue uplift, plus what's next for real-time voice and server-side tool execution. ## How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 1 DevFeed: [How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 1]() Original publisher: [Read original article]() Author: Muhammad Musab Iqbal Published: 2026-08-20T00:32:52Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [AI Chat]() Tags: [ai-agents](), [amazon-bedrock-agentcore](), [customer-solutions](), [sales](), [scalability](), [strands-agents](), [whatsapp]() ### AI overview AgentFlo describes building always-on AI sales agents with Amazon Bedrock AgentCore and the Strands Agents SDK. Part 1 focuses on velocity, standardization, and scalability for commerce conversations. ### Source excerpt Learn how AgentFlo built always-on AI sales agents on Amazon Bedrock AgentCore and the Strands Agents SDK. Part 1 covers three pillars of production-grade agents--velocity, standardization, and scalability--including recipe-based deployment, tool routing through AgentCore Gateway, and elastic, stateful commerce conversations. ## How Clario technology detects PHI/PII in DICOM images using Amazon Bedrock DevFeed: [How Clario technology detects PHI/PII in DICOM images using Amazon Bedrock]() Original publisher: [Read original article]() Author: Alex Boudreau Published: 2026-08-19T14:29:31Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Bedrock](), [Medical imaging](), [Amazon Textract](), [Amazon Web Services](), [data]() Tags: [amazon](), [amazon-bedrock](), [amazon-textract](), [analysis](), [architecture](), [aws](), [customer-solutions](), [data](), [healthcare](), [images](), [medical-imaging](), [pii](), [technology](), [workflow]() ### AI overview Clario uses Amazon Bedrock and Amazon Textract to automate the detection of PHI and PII in DICOM image slices from clinical trials, including information stored in metadata and text embedded in image pixels. ### Source excerpt Clario, part of Thermo Fisher Scientific, uses Amazon Bedrock and Amazon Textract to automatically detect protected health information (PHI) and personally identifiable information (PII) across thousands of DICOM image slices in clinical trials, covering both metadata tags and text burned into the image pixels. ## Serverless vehicle tracking at scale: Bosch L.OS on AWS DevFeed: [Serverless vehicle tracking at scale: Bosch L.OS on AWS]() Original publisher: [Read original article]() Author: Yogish Kutkunje Pai Published: 2026-08-14T10:02:13Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Web Services](), [Serverless](), [real-time](), [Amazon Elastic Container Service](), [AWS Lambda](), [cloud-infrastructure](), [data](), [Networks](), [Amazon EC2]() Tags: [amazon-elastic-container-service](), [architecture](), [aws](), [complexity](), [compliance](), [cost](), [customer-solutions](), [data](), [india](), [integration](), [intermediate-200](), [networks](), [operations](), [performance](), [platform](), [real-time](), [scale](), [serverless](), [supply-chain](), [workflows]() ### AI overview Bosch Mobility Platform Solutions built L.OS, a serverless platform on AWS that unifies fragmented vehicle-tracking data across India's spot logistics market. The article describes how the platform standardizes data from multiple providers and supports scalable, real-time visibility through workflows for discovery, tracking, and termination. ### Source excerpt Learn how Bosch Mobility Platform Solutions built L.OS, a serverless vehicle tracking platform on AWS that unifies India's fragmented spot logistics market into a single real-time visibility layer using Amazon ECS, AWS Lambda, and Amazon MSK. ## Adobe Firefly: Simplified observability with Amazon Managed Prometheus DevFeed: [Adobe Firefly: Simplified observability with Amazon Managed Prometheus]() Original publisher: [Read original article]() Author: Dev Arora Published: 2026-08-13T00:14:19Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [observability](), [GPU](), [Amazon EKS](), [Amazon Web Services](), [Monitoring](), [telemetry](), [migration](), [Training AI Models]() Tags: [adobe](), [amazon](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [amazon-managed-service-for-prometheus](), [amazon-web-services-aws](), [aws](), [compute](), [cpu](), [customer-solutions](), [data](), [deployment](), [efficiency](), [gpu](), [infrastructure](), [jobs](), [kubernetes](), [metrics](), [observability](), [performance](), [prometheus]() ### AI overview Adobe Firefly migrated critical GPU infrastructure metrics from self-managed Prometheus to Amazon Managed Service for Prometheus. The article describes the observability challenges of large-scale model training on Amazon EKS, including high-cardinality GPU, compute, memory, and network telemetry, and reports 28x faster GPU metric queries with improved reliability and operational efficiency. ### Source excerpt Learn how Adobe Firefly achieved 28x faster GPU metric queries by migrating from self-managed Prometheus to Amazon Managed Service for Prometheus, with improvements in query performance, infrastructure reliability, and operational efficiency.