In the depths of a Boliden mine, 700 meters underground, running traditional cloud-first AI on heavy-duty machinery falls short on delivering results. In a collaboration with Volvo Group and academic partners, we validated a novel approach: turning the trucks themselves into intelligent and interactively queryable computational databases. By embedding a tiny combined main-memory database and computation engine directly onto heavy-duty mining vehicles, we transformed the fleet into a distributed system where data streams are analyzed and queried at the source in real-time.
This talk shares the architectural lessons learned from deploying this "database-on-wheels" model to monitor critical metrics like battery health, energy regeneration, and driving patterns in a connectivity-constrained environment.
Beyond the immediate deployment, this architecture offers a fundamental shift in how we build industrial AI. We will explore how exposing physical assets via a familiar SQL-like interface paves the way for the next generation of Agentic Workflows. Instead of dealing with rigid firmware cycles, autonomous agents can simply “query” the fleet for insights or update model weights as easily as updating a row in a database table. We will discuss the trade-offs of edge-native computational query processing and how this approach decouples rapid AI innovation from the slower engineering cycles of heavy machinery.
Stefan Månsby is the CEO of Stream Analyze, an Edge AI company enabling intelligent decision-making on distributed devices. A serial co-founder and team builder, he co-founded Basefarm, growing it from a startup to a leading European managed services provider and has built innovation units, consulting practices, and AI teams throughout his 25+ year career. His experience spans CTO, CIO, and senior advisory roles at Orange Business and Basefarm, where he led projects in industrial IoT, fintech, and cybersecurity across three continents. And he still writes code.
