This post is co-written with Mauro Rallo and Patrick van der Plas from HEMA.
When engineers at HEMA needed an answer, they went portal-hopping, navigating disconnected wikis, service catalogs, and IT portals to find it. To turn that friction into instant answers, the 100-year-old Dutch retailer built a knowledge layer on Amazon Bedrock AgentCore. HEMA has over 750 stores across multiple countries, served by a technology organization of engineers, product owners, and business analysts driving digital transformation. It needed a solution that worked across roles and tools.
Over the years, HEMA had quietly built something valuable: a large, structured picture of its own technology landscape. A service catalog mapped people to teams, teams to services, and services to the APIs we expose, and the business capabilities we support. The problem was never that the knowledge didn’t exist. It was that the knowledge was hard to reach. As the engineering organization grew, the informal “just ask the person next to you” model broke down, and teams ended up scattering answers across portals, wikis, and documentation that few people knew how to navigate.
In this post, we describe the challenge HEMA faced with fragmented internal knowledge, why we chose to build HAL, HEMA’s internal AI assistant, using Model Context Protocol (MCP) and Amazon Bedrock AgentCore, and how it changed the way our teams work.
The idea rests on two complementary goals. HAL puts knowledge in one place, and MCP delivers that knowledge inside the tools people already use (the HAL chat, Kiro , Claude , and other agents).
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