About Coco

Providing an affordable, sustainable, and delightful last-mile delivery experience for merchants operating in dense urban environments.

• Founded: 2020 in Santa Monica, CA
• Size: ~100 employees (Series A)

Impact

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 tackling a wide array of delivery verticals – all without contributing to urban congestion or pollution.

As Coco scaled, resolving incidents became a major bottleneck. A single issue started taking hours of engineers' time. Eventually, the team realized that to achieve their goals, they needed better robotics observability – a faster and more scalable approach to capturing, organizing, and learning from their data.

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

Challenges

Juggling different data across disparate tools

Because Coco’s systems dealt with heterogeneous data streams, the hardware, software, and quality teams used a variety of tools to collaborate on their data. While these solutions worked great individually, they didn’t integrate well with each other. Analyzing, sharing, and cross-referencing data across these tools became an increasingly clunky process that cost hours of developer time daily.

This patchwork system made it difficult for Coco’s engineers to collaborate. Sensor data, log messages, control signals, and videos were recorded in different formats across various frameworks, forcing engineers to jump between as many as five tools to analyze a trip. In fact, they rarely looked at the robots’ ROS bag files, since they contained such a minor subset of the full picture.

Bottlenecking progress with manual workflows

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

This process was long, error-prone, and not scalable. Few teammates were technical enough to use all the tools, so teamwide progress often depended on these engineers’ availability. In fact, Coco routinely missed opportunities to properly resolve incidents, since the barrier to inspecting them was so high.

Training and auditing human pilots

Training and auditing Coco’s pilots required a large investment of time and resources.

For new pilots, an experienced teleoperator had to physically be in the room to watch how they handled deliveries. Even for more experienced pilots, Coco found it difficult to audit whether they were reporting all incidents (e.g. a robot flip, a pedestrian interacting with the robot, etc.) accurately. To review a session, an engineer had to reference multiple databases to pull the relevant video for a given robot and timeframe, playback hours of footage to spot-check a few incidents, then reference another database to figure out 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 the pilots’ workstations, or via its autonomy stack, Coco needed to pull its multimodal data streams together in one place for efficient analysis.

To accomplish this, Coco leveraged MCAP, a container file format developed by Foxglove, to merge their heterogeneous data into a common log format. They imported their newly consolidated files into Foxglove for easy 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, where they can scrub back and forth to jump to the timestamps they care about most. 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 one integrated development environment, without jumping between software solutions or handing off tasks between teammates. This bird’s-eye view has made their issue tracking more efficient than ever – analysis tasks have gone from taking hours to seconds, and the number of developer tools used has decreased fivefold.

Democratizing team access to data and insights

When taking robots on test drives or deliveries, Coco team members can now access the recordings in Foxglove by the time they bring the robots back inside. They no longer have to wait for an available engineer to query various databases and use multiple tools to download video footage. Any technical or non-technical team member can spend a few seconds clicking around on the Foxglove timeline to find the footage they’re interested in, along with all its associated metadata (e.g. recording robot, delivery trip details, map, etc.).

By integrating with Foxglove events, Coco has also been able to batch-review incidents for more streamlined triaging. Whether it’s a pilot adding a tag for a robot-human interaction or a script automatically detecting issues like robot flips, the Coco team can step through a list of these events within minutes to assess whether further analysis is necessary. Not only would it facilitate analysis in the short-term, but the benefits of having organized data would continue growing as Coco collected more data into the future.

Tagging data with events has also helped Coco more easily follow up on deeper analysis. Engineers can now quickly review all instances of a failure mode and make decisions based on that information, instead of spending hours or days wading 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.

Outcome

Before Foxglove, Coco often made strategic business decisions based on assumptions or approximations – largely because it was so time-consuming to compile the data needed to make informed decisions. Once Foxglove placed the data at Coco’s fingertips, it exposed parts of the business that the team hadn’t been seeing. Coco can now access data, triage incidents, and weigh the impact of an issue on their business in seconds. Their engineers can also now collect cleaner data around their key metrics – data that will power future iterations of their autonomy software – and make the 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 – the Trust and Safety team use it 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 avoid tooling debates and focus instead on building high-performance robots.