Founded in 2020 to democratize last-mile delivery, Coco currently operates its sidewalk robots in Santa Monica and West Los Angeles for food delivery. Their long-term vision is to deploy an autonomous fleet across various delivery verticals—without contributing to urban congestion or pollution.

As Coco scaled, resolving incidents became a major bottleneck, with single issues taking hours of engineers’ time. The team soon realized that achieving their goals required better robotics observability—a faster, more scalable approach to capturing, organizing, and learning from their data.

By adopting Foxglove, Coco quickly learned to make sense of their complex data, reducing incident resolution time from hours to seconds.

Challenges without Foxglove

Juggling different data across disparate tools.

Because Coco’s systems dealt with heterogeneous data streams, the hardware, software, and quality teams used various tools to collaborate on their data. While these solutions worked well individually, they didn’t integrate smoothly with each other. Analyzing, sharing, and cross-referencing data across these tools became an increasingly cumbersome process that cost developers hours each day.

This patchwork system made collaboration difficult for Coco’s engineers. Sensor data, log messages, control signals, and videos were recorded in different formats across multiple frameworks, forcing engineers to jump between as many as five tools to analyze a single trip. As a result, they rarely examined the robots’ ROS bag files, as these contained only a small portion of the full picture.

Bottlenecking progress with manual workflows.

To view a recording of a robot’s trip, Coco engineers first had to transcode the data, select a time range to explore, write a script to convert it to the correct format in a Jupyter Notebook, wait several minutes for the output MP4 file, and finally view it in a web browser.

This process was long, error-prone, and not scalable. Few team members had the technical skills to use all the tools, so teamwide progress often depended on the availability of these engineers. As a result, Coco frequently missed opportunities to properly resolve incidents, as the barrier to inspecting them was so high.

Training and auditing human pilots.

Training and auditing Coco’s pilots required a significant investment of time and resources. For new pilots, an experienced teleoperator had to be physically present to observe how they handled deliveries. Even for more experienced pilots, Coco found it challenging to audit whether they were accurately reporting all incidents (e.g., a robot flip or a pedestrian interacting with the robot). To review a session, an engineer had to cross-reference multiple databases to pull the relevant video for a given robot and timeframe, playback hours of footage to spot-check a few incidents, and then consult another database to identify which pilot was responsible for the incidents.

Deploying Foxglove to accelerate development

With the Foxglove platform, Coco knew they could tackle most of their challenges with one software solution. They were excited to get support across their entire development process – from data storage and management to visualization and analysis.

Bringing multimodal data into one integrated environment.

Whether recording data on the robot, from pilots’ workstations, or via its autonomy stack, Coco needed to consolidate its multimodal data streams into one place for efficient analysis. To achieve this, Coco leveraged MCAP, a container file format developed by Foxglove, to merge their heterogeneous data into a common log format. They then imported their newly consolidated files into Foxglove for seamless team-wide collaboration.

With Foxglove’s intuitive web interface, Coco engineers can now reference a central repository to annotate, organize, and analyze data. Clicking on a recording instantly allows them to visualize data, scrub through the timeline, and jump to key timestamps. Here, they can compose rich layouts that visualize everything from camera feed images to log messages and 3D markers.

With this migration, Coco now stores, visualizes, and debugs data in a single integrated development environment—eliminating the need to jump between software solutions or hand off tasks between teammates. This bird’s-eye view has made issue tracking more efficient than ever: analysis tasks that once took hours are now completed in seconds, and the number of developer tools has decreased fivefold.

Democratizing team access to data and insights.

After taking robots on test drives or deliveries, Coco team members can now access the recordings in Foxglove by the time the robots are brought back inside. They no longer need to wait for an available engineer to query multiple databases and use several tools to download video footage. Any technical or non-technical team member can easily click through the Foxglove timeline in seconds to find the footage they need, along with all associated metadata (e.g., recording robot, delivery trip details, map, etc.).

By integrating with Foxglove events, Coco has also streamlined incident triaging through batch reviews. Whether it’s a pilot tagging a robot-human interaction or a script automatically detecting issues like robot flips, the Coco team can step through a list of events within minutes to determine if further analysis is required. Not only does this facilitate short-term analysis, but the benefits of having organized data will continue to grow as Coco collects more data in the future.

“It was possible to look across a hundred trips, quickly locate all instances of an issue, and summarize its impact on the robots’ performance within minutes.” Rob Zehner, VP of Engineering, Coco

Tagging data with events has also made it easier for Coco to conduct deeper analysis. Engineers can now quickly review all instances of a failure mode and make informed decisions based on that information, rather than spending hours or days sifting through petabytes of unstructured data.

Improving human pilot oversight and training.

Better data visibility has empowered Coco engineers to better collaborate with human pilots. Previously when pilots manually reported incidents, their subjective judgment calls often resulted in inconsistent records. But with automatic tagging and easier access to video footage, engineers can now easily cross-reference generated events against pilots’ reports to audit them for accuracy. Instead of reviewing hours of footage to audit trip reports, they can now scan the main points of interest in seconds and use Foxglove to get more qualitative context on any discrepancies.

The impact of using Foxglove

Before Foxglove, Coco often made strategic business decisions based on assumptions or approximations—mainly because compiling the data necessary for informed decisions was so time-consuming. Once Foxglove put the data at Coco’s fingertips, it revealed parts of the business that the team hadn’t been seeing. Coco can now access data, triage incidents, and assess the impact of an issue on their business in seconds. Their engineers are also able to collect cleaner data around key metrics—data that will drive future iterations of their autonomy software—and make necessary adjustments to their roadmaps.

With this integration, Foxglove has become a one-stop solution for Coco’s development workflows. The platform is now pervasive across the company—used by the Trust and Safety team to review incidents, pilots to log their trips, and engineers to share collaboration links. By helping them tackle common development tasks, Foxglove has allowed Coco to bypass tooling debates and focus on building high-performance robots.