Data Mesh
Data Mesh on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on data mesh in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around data mesh, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models
Unlock the potential of AI agents with a data layer designed for their needs. Fabiane Nardon’s presentation, "Architecting the Data Layer for AI Agents," details how TOTVS is preparing enterprise data for token-intensive AI workflows, balancing precision, security, and cost. Nardon explores critical strategies including data mesh architectures, low-latency databases, semantic ontologies, and dynamic MCP selection to optimize context windows and minimize token overhead within transactional systems. For further exploration of securing data in modern applications, see our article, "Post-Quantum Cryptography in Spring Boot."
At VB Transform 2026, Zillow's engineering chief said AI ROI numbers only hold up if you measure before you build
At VB Transform 2026, Zillow's engineering chief, Toby Roberts, underscored a critical lesson for enterprise AI: establish measurement baselines *before* implementation. Zillow’s experience revealed that context, not just raw data, presents the most significant challenge when building AI architecture to support customers navigating complex real estate transactions. Their solution—a persistent context layer—demonstrates the value of owning this layer, alongside partners like Glean, to streamline workflows and optimize costs by leveraging smaller, task-specific models.