From data to decisions: Conversational supply chain intelligence with Amazon Quick and Amazon Bedrock AgentCore
21h
- Published
- Collected
Enterprise supply chains are data-rich but insight-poor, with the data to decide scattered across ERP, monitoring, procurement, and email. This post shows how Amazon Bedrock AgentCore exposes those systems as a governed tool layer, and how Amazon Quick turns them into one conversational experience for business users, with a deploy-it-yourself walkthrough.
Giving supply chain teams direct access to the systems behind their decisions. Every supply chain generates constant signals: orders in approval, shifting lead times, slowing ports, drifting prices, disruptions. The problem isn’t scarce data. It’s the distance between the data and the decision-maker. Answering “which top-revenue SKUs are exposed to the current port closure, and what will it cost?” used to mean knowing which system held each piece and stitching them together by hand while the…
overfeed.news indexes and links. We publish a short excerpt — the full article stays at AWS Machine Learning Blog.
More from AWS Machine Learning Blog
Log in to follow this source
Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch
Learn how i14 trained and served Laplace 3B, a linear attention model, on AWS Trainium2 using native PyTorch. Its recurrent state stays fixed at 29.5 MB per sequence regardless of context length, avoiding the growing key-value cache of transformer models and keeping serving memory predictable.
Work management context for AI agents with Smartsheet MCP on AWS
The Smartsheet Model Context Protocol (MCP) Server lets AI agents read, update, and act on live work management data. This post shows two ways to connect it on AWS: Amazon Quick for business teams and Kiro for developers, so your agents can work with Smartsheet without switching tools.
Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod
Learn how we fine-tuned GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod, and how to decide between supervised fine-tuning (SFT) and reinforcement learning with GRPO. SFT took reasoning-language accuracy from 22% to 100%, and GRPO added measurable gains where headroom existed.
How Octave built a secure multi-tenant AI foundation on Amazon Bedrock
Octave built a secure, multi-tenant AI foundation on Amazon Bedrock, using Cohere Embed v4 for embeddings and Amazon Bedrock AgentCore for isolated agent execution, to power a Complaint Triage Assistant that searches 2 billion regulated documents without crossing tenant boundaries.