This conversation with Axion founder and CEO Daniel First, hosted by Inspired Capital founder and managing partner Alexa von Tobel, dives into how AI can fundamentally change the way manufacturers detect and solve product issues long before they become recalls or catastrophic failures. Daniel and Alexa explore how Axion turns fragmented data into an AI command center, exposes the invisible multi‑trillion‑dollar quality crisis, and evolves from a single‑team quality tool into a multi‑department operating layer for leading manufacturers. 

The discussion highlights Axion’s human‑in‑the‑loop approach to uncovering life‑critical root causes, how closing the loop from field issues to product roadmaps drives innovation, and why vertical AI, robotics, and an empathy‑driven manufacturing culture define the next frontier for American industry. Key highlights of the conversation captured below. 

Turning fragmented data into an AI command center

Axion links together IoT and telematics, fault codes, technician investigations, factory systems, and customer feedback into a single observability platform. Manufacturers in aerospace, medical devices, and consumer products use this platform to see the top issues impacting customers, understand why they are happening, and decide how to fix them-often before they turn into recalls or widespread failures.

Axion is a command center that helps manufacturers understand the top issues impacting their customers, why they are happening, and how to solve them.

Exposing the invisible multi‑trillion‑dollar quality crisis

Across manufacturing, quality problems quietly consume an estimated 2–4 trillion dollars of value out of roughly 30 trillion in annual industry revenue. The most visible symptoms are recalls and warranty costs-like one automaker spending around 5 billion dollars on warranty each year-but the real damage comes from hundreds or thousands of small, recurring issues that never get fully resolved between product generations.

From single team tool to a multi‑department operating layer

Many manufacturers initially bring Axion in for their quality teams, but usage quickly spreads across the organization. At Harley‑Davidson, for example, 12 different departments adopted the platform within months, including marketing, customer experience, supply chain, software engineering, and product strategy, all using the same quality insights to inform their decisions.

Human lives, root causes, and a different way of building AI

In one deployment with a medical device manufacturer, Axion was used to investigate surgical equipment that was malfunctioning during procedures, causing patients to lose significant amounts of blood. The manufacturer believed the issue had already been fixed, but Axion’s AI identified two distinct root causes that were still affecting patients, allowing the company to address both issues and materially improve safety in the field.

We found two distinct root causes behind surgery equipment failures that were still harming patients, even after the manufacturer thought the problem was solved.

To solve problems like this at scale, Axion pairs AI with deep domain expertise by hiring aerospace, medical device, and other specialized engineers into the company. These experts map how they scope issues and perform root-cause analysis inside the platform, and the product team then progressively automates the most manual, repetitive steps-from pattern detection to portions of the investigative workflow so the system gets smarter over time.

Closing the loop: from field issues to future product roadmaps

While many customers start by using Axion after products are launched to rapidly detect and resolve issues in the field, they are increasingly pulling those insights earlier into product development. Axion is investing in an ecosystem of tools that connect quality signals from the field back into design, engineering, and supplier decisions, so future generations of products are shaped directly by real customer experience.

Axion’s customers are using “quality issues” as a lens into unmet customer needs, not just as problems to be contained. For instance, with an at‑home ice cream machine, Axion surfaced that one of the biggest real‑world use cases was making high‑protein “fitness” ice cream, which the original machine was not built to handle-an insight that informed the creation of a premium model designed specifically for those thicker, protein-heavy recipes.

The future of American manufacturing will be defined by how fast you can learn about your customers’ needs and iterate your products in response.

Vertical AI, robotics, and the new manufacturing frontier

Enterprise AI pilots often fail not because the models are weak, but because of gaps between prototypes and reality: expert knowledge is missing, existing processes do not fit, and there is no clear path from model output to action. Axion was founded after watching many of these pilots stall, and its platform is deliberately built to close those gaps by embedding domain expertise, aligning with real workflows, and driving issue‑to‑resolution loops that can actually scale.

As manufacturers adopt robotics and other advanced hardware‑software systems, the number and complexity of quality issues tend to increase, at least initially. Robots, electric vehicles, and other cutting‑edge systems are difficult to engineer and integrate, which makes rapid learning from failures essential; Axion already supports customers in robotics and expects this category to grow significantly as the technology scales.

When Axion wins, the world gets safer, innovation gets faster, and everyday products work better for all of us.

Watch the full conversation with Daniel First and Alexa von Tobel