Commercial vehicles face a resource problem as their AI capabilities expand: running every model continuously adds demands on finite compute and memory, making new functions harder to integrate, validate, and operate. The architectural challenge is how to support more intelligence within the resources available on the vehicle.
Ask an engineer what a quality issue will cost to remedy, and they’ll usually describe the technical work: reproduce the issue, isolate the cause, write and validate a fix. That’s one cost, to be sure, but it’s a fraction of the cost that will show up on a balance sheet. The factor that matters is when the fix happens, because the same technical correction can trigger wildly different bills depending on which side of one particular line it falls on.
Vehicle "quality latency" costs automakers millions in recalls and warranty claims. By leveraging AI-driven diagnostics, the Sonatus Continuous Quality framework compresses the time between defect detection and resolution to fix issues faster.
Modern vehicles generate an overwhelming volume of diagnostic data: sensor logs, fault codes, network traces, telemetry from dozens of ECUs. As that data grows, engineering and support teams are turning to vehicle AI to help make sense of it. But there’s a problem. A single AI model’s best guess isn’t good enough when the answer needs to hold up to an engineer’s scrutiny or a customer standing at a dealership service counter.
From AI models to agents: What makes a system “agentic”? AI models and LLMs have made incredible progress in recent years, with uncanny gains in natural language processing to create and understand language. But the real shift happening right now…
The automotive industry is undergoing a period of incredible transformation. New technologies entering the vehicle include more software, electric powertrains, and new sensors. But perhaps the most important change is the deployment of artificial intelligence. Today, vehicle AI is no longer a future concept.
The first two parts of this series made the case for the Agentic Loop as an architecture — the problem it solves, and what happens at each of its five stages, from detection through learning. This final post is less…
Part I of this series laid out the case for why the automotive industry’s data infrastructure has outpaced its intelligence infrastructure and introduced the Agentic Loop — Sonatus’s framework for closing that gap: Detect → Collect → Reason → Act…
There is a moment that most automotive engineers recognize. A fault appears during a test drive — a thermal anomaly, an unexpected message on the vehicle network, a transient system behavior that shouldn't be there. The engineer notices it. The…