Play multiple files from start to end as if they’re a single recording and stay organized with layout folders. Available now with release 2.17. View the full changelog here.
Play multiple files from start to end as if they’re a single recording and stay organized with layout folders. Available now with release 2.17. View the full changelog here.
Dexterity is transforming warehouse logistics and workflows with AI powered robotics.
Founded: 2017 in Palo Alto, California, USA
Size: ~250 employees (Series B)
~20% development time saved
$150,000 saved annually in tooling and development time
<10 mins to triage an issue with <1 day from issue discovered to fix
“Things that were impossible before are now possible.” Robert Sun, Founding Engineer, Dexterity
Dexterity is revolutionizing logistics, warehousing, fulfillment, and e-commerce by providing advanced AI robotics that help Fortune 500 companies reduce their carbon footprint, address labor challenges, and maximize their automation investments. The teams (that use Foxglove) at Dexterity focus on robotic controls, motion planning, vision, and state estimation of robotic systems, creating technology that enhances and automates critical parts of their customers' operations.
Before integrating Foxglove into their workflows, Dexterity’s team relied heavily on in-house tools and various log analyzers. While these tools met their basic needs, they required constant maintenance and lacked the rich visualization capabilities essential for efficient debugging and development. Tasks like adding and removing scene state estimators and combining it with robot views were challenging. Iterating on computer vision (CV) visualization was time-consuming, as researchers preferred to focus on algorithm development rather than building custom visualization libraries and hooks. Additionally, the inability to record or replay visualizations and data resulted in significant time wasted, developer friction, and the need to juggle multiple disparate tools. With the adoption of Foxglove, Dexterity was able to reduce the number of developer tools by threefold, contributing to annual savings of over $150,000.
The decision to integrate Foxglove was driven by the need to eliminate visualization boundaries and streamline workflows. Dexterity undertook a deep integration process, migrating to Pub/Sub high-frequency data and overhauling their services to feed directly into Foxglove WebSocket and MCAP recordings. This integration significantly shaped Dexterity’s architecture, enabling both edge sites (customer locations) and primary sites, while unlocking additional benefits of the Foxglove data platform. It made it incredibly easy for Dexterity developers, test engineers, field ops, and partners to access, share, and collaborate on snippets of robot events. This also allowed the team to discover issues in less than a day, triage them in under 10 minutes, and deploy a fix within the same day.
Initially, the team expected Foxglove to provide easier visualization and a few new features. However, as the integration progressed, it became clear that Foxglove offered much more, reshaping their entire workflow. Like many scaling robotics companies, Dexterity was experiencing growing pains with their middleware, which Foxglove and MCAP helped overcome. The additional effort to overhaul their workflows—not just their visualization—proved to be well worth it, resulting in a deeply integrated and highly functional system for their team.
“We expected just easier visualization, but it turned out to be much more—unlocking invaluable efficiency for all our developers.” Robert Sun, Founding Engineer, Dexterity
The impact of integrating Foxglove was felt within 3 months, as the team gradually adopted the new workflows. Foxglove transformed Dexterity’s debugging and development processes by enabling every step of their AI Directed Acyclic Graph (DAG) to be visualized independently or combined for a comprehensive state view. This capability allowed the team to create a full state visualization for their robots, similar to visualizations we see now for self-driving vehicles, enhancing collaboration and data sharing across teams. Iteration on new features, particularly in computer vision, became significantly more efficient, as developers could instantly visualize algorithmic issues and address them promptly.
Foxglove’s integration provided Dexterity with invaluable tools to pull and piece together data across production and development environments. The ability to save and share views indefinitely has created a treasure trove of data that was previously inaccessible. As a result, the platform has not only solved immediate business problems but also unlocked new possibilities for the team. The improvements in workflow efficiency and the introduction of new capabilities have had a profound impact, making previously impossible tasks achievable and dramatically reducing the time needed to diagnose issues. Ultimately, Foxglove has been instrumental in helping keep Dexterity at the forefront of robotics innovation, ensuring that it can deliver transformative value with AI powered robotics.
