Third Wave Automation is transforming the industrial landscape into a safer, more efficient, and cleaner environment powered by the third wave of machine learning.
Founded: 2018 in Union City, California, USA
Size: ~60 employees (series C)
Reduced debugging times from hours to minutes.
Saved days on development cycles.
Removed developer friction.
“Many triaging tasks that once took an engineer an hour to diagnose using our home-grown tools now take just 10–15 minutes with Foxglove.” Titus Klinge, Staff Software Engineer at Third Wave Automation
Third Wave Automation is transforming warehouse logistics with its Shared Autonomy Platform, a system designed to enhance forklifts with perception, motion planning, and AI-driven autonomy. The platform enables seamless transitions between fully autonomous operation, human-assisted interventions, and manual control, making it a flexible and scalable solution for the industry.
Third-party logistics providers, including Holman Logistics, rely on Third Wave Automation to increase efficiency, improve safety, and optimize operations. As these companies scale, they need automation that can handle real-world warehouse complexities while maintaining human oversight. Third Wave Automation’s forklifts integrate advanced perception models, motion control systems, and real-time monitoring, ensuring consistent, reliable performance in high-demand environments.
Before integrating Foxglove, debugging was an inefficient, manual process that required engineers to piece together information from multiple sources. Third Wave Automation stored logs in an internally developed format. While this logging format was efficient, the team relied on a tedious process of setting up a Jupyter Notebook—and other tools—for every debugging and analysis session. Extracting relevant data required manually loading logs, selecting time ranges, and writing custom scripts for visualization.
Interpreting perception data was even more challenging. The team used Jupyter notebook plotting tools to analyze sensor outputs, but these visualizations lacked the spatial depth required to fully understand forklift perception failures. Debugging an issue often involved sifting through raw numerical data and cross-referencing it with multiple independent plots, making it difficult to determine whether a forklift had misinterpreted its environment or if a detection failure had occurred.
On-site debugging introduced another layer of complexity. Engineers at customer locations had to manually retrieve logs from on-site servers, which often faced storage limitations or procedural challenges, restricting the amount of data available for analysis. These roadblocks meant that diagnosing an issue could take hours—before even determining an appropriate fix.
Efficient collaboration was also a challenge. Engineers often shared screenshots of log outputs in issue reports, providing only a static snapshot of a problem without any interactive context. Debugging was rarely a one-person task, yet team members had no efficient way to step through logs together or replay events in a structured way. Each investigation required multiple back-and-forth exchanges, and without an intuitive way to share insights, debugging remained a slow, disconnected process.
As the company expanded its autonomous forklift fleet, these inefficiencies became a bottleneck. Debugging issues at scale required faster iteration, better tools for visualizing complex interactions, and a way to seamlessly share insights across teams. The need for a more efficient, centralized debugging solution became clear.
“Foxglove made debugging far more interactive and efficient.” Titus Klinge, Staff Software Engineer at Third Wave Automation
Recognizing these challenges, Third Wave Automation sought a solution to streamline its development workflow. The team began by integrating Foxglove, first addressing their fragmented log analysis process. When offloading logs, they convert their internal logs to MCAPs, so engineers can easily take advantage of Foxglove’s State Transitions, Plot, and 3D panels, three widely adopted and favored panels. With an automated pipeline to process and offload logs from forklifts in the field, data that once required manual retrieval and setup was now accessible for analysis through Foxglove’s data management—within minutes.
The impact was immediate. Instead of manually retrieving data and setting up Jupyter notebooks, engineers could now open Foxglove and start debugging immediately. The State Transitions panel allowed them to track changes in state across a detailed timeline, making it easy to pinpoint when a failure occurred. Instead of writing custom scripts for log parsing, they could step through messages frame by frame, reconstructing events with far greater efficiency.
Perception debugging became more structured with Foxglove’s 3D panel. Engineers could now inspect point clouds, verify sensor alignment, and analyze object detection failures effectively. Rather than relying on fragmented 2D plots, they could visually confirm how forklifts were interpreting their surroundings, helping them validate perception models with greater confidence.
Foxglove quickly became the primary tool for post-run analysis, recorded log debugging, and collaborative issue resolution. Shifting from manual log processing to a centralized, structured debugging workflow eliminated inefficiencies, optimized development cycles, and saved valuable time—accelerating innovation.
“Foxglove has become an essential part of how we analyze and improve autonomy.” Titus Klinge, Staff Software Engineer at Third Wave Automation
With Foxglove integrated into its workflow, Third Wave Automation reduced debugging time from hours to minutes, improved visibility into perception failures, and established a more structured development process. Engineers can now quickly analyze system behavior, leading to more reliable perception models and faster iteration cycles, reducing the time required to test and deploy improvements.
Collaboration has also become more seamless. Instead of relying on static screenshots, engineers share direct Foxglove session links, enabling multiple team members to step through logs together and analyze data in real-time. This shift has eliminated inefficiencies in debugging, making it easier for teams to diagnose and resolve issues faster.
With a more efficient development process, Third Wave Automation is scaling its autonomous material-handling fleet with greater agility while staying focused on advancing the future of warehouse automation.
