About Tangram Vision

Providing perception tools and managed infrastructure (e.g. sensor calibration and fusion) for robotics teams building sensor-enabled products.

• Founded: 2020 in Boulder, CO
• Size: <10 employees (Seed)

Impact

Tangram Vision helps robotics teams building sensor-enabled products scale their perception stack – from one proof-of-concept robot to an entire fleet. Whether you’re adding new sensing modalities, layering on perception algorithms, or simply trying to get your cameras and lidars to communicate with each other, Tangram’s software takes these esoteric tasks off of your engineers’ to-do lists and magically makes all of it work.

But over the last year, Tangram began to realize that it was taking too long to get their software up and running at customer sites. To assess customers' data, Tangram engineers had to visit their robots in person, bring the data back to their offices, and convert it into a supported format to finally inspect it.

In short, Tangram lacked a unified solution for robotics observability. By adopting Foxglove and the MCAP file format, Tangram was able to slash their onboarding workflows from months to days.

Challenges

Spending weeks to demonstrate value to customers

When engaging with a new customer, Tangram Vision takes them through a pilot process to demonstrate the value of integrating with their software. Before adopting Tangram, customers typically want to first see it adding concrete value to their development efforts.

Before integrating with Foxglove, this typically meant traveling on-site to the customer to collect sample data off a physical robot. Tangram engineers then offloaded this data, took it back to their offices, converted it into a supported format, and inspected it with their internal data visualization tooling. At this point, they could discover critical issues – parts of the data could be corrupt, unusable, or missing altogether. They would then work with the customer to fix these issues – by either scheduling time to come back on-site or guiding the customer’s engineers through sending another data set. Once they finally have “good data” to work with, Tangram can process it with their software to assess the customer's existing perception processes and provide actionable recommendations for leveling up their system.

This entire process could easily take weeks to complete, depending on the state of customers' data. Not catching issues early often introduced days to weeks of delays. Other unforeseen issues – like the designated robot now being busy for a week, out of commission due to hardware issues, or partially disassembled for maintenance – could make the back and forth needed to get this sample dataset even longer.

Hard-to-scale consultative work

Currently, robotics development is a very fractured ecosystem, and no two robotics stacks are alike. For Tangram Vision, every individual integration meant getting to know a new stack with its own unique requirements. This made onboarding new customers incredibly manual, as each one required an unsustainable amount of time and assistance for configuration and validation. Not only did this stretch out the time needed to convert a pilot user into a paying customer, it also limited the number of new customers Tangram could take on at any given time.

Tangram Visions’s customers also recorded their data in a wide range of serialization formats, further contributing to onboarding friction. When accepting and juggling these different formats for validation, Tangram had to invent ways to organize each customer’s files – with methods as basic as using different Google Drive folders for different types of sensor data. Even after collecting the right data, Tangram then had to consult closely with the customer’s engineers to integrate that unique data with their software. All this unstandardized data collection made it difficult to collect accurate, complete, and synced customer data both quickly and repeatedly.

Wasting resources on undifferentiated tooling

Tangram Vision developed their own data visualization tooling in-house to help them understand customer data. While the tool initially served their purposes, it quickly became clear that building robust visualization software was neither the team’s interest nor specialty.

As the customer base grew and development work scaled, it became increasingly difficult to maintain this visualization code. Tangram engineers struggled to keep pace with new and changing requirements, especially while serving customers that weren't using a unified format for their data. Whenever Tangram changed anything in their perception software, engineers would then have to spend a day tending to the newly broken visualization features downstream and updating rendering logic to adapt to the changes.

Adopting Foxglove to streamline processes

More efficient customer onboarding with MCAP

Tangram Vision adopted MCAP as their preferred file format for collaborating on data – both internally and with customers. This has provided all parties with a common data language and some much-needed consistency, making it infinitely easier for Tangram to support their customers.

By collecting and evaluating data in one standard format, Tangram Vision can now understand the state of their customers’ processes – and demonstrate the worth of their software – in record time. Instead of taking each customer's uniquely formatted data files back to their workstations for bespoke conversion, deciphering, and analysis, they can feed all of it through the same process. Even for new customers that haven’t already made the transition to MCAP, they can easily point them to the provided CLI tool to convert their data files with a few simple commands.

Recording in MCAP has also resulted in much more lightweight files, making it easier than ever to ingest data off the robot, send files back and forth, and load them for validation. Instead of waiting days or weeks to inspect a customer’s data, Tangram engineers can now offload the robot's newly recorded MCAP files, load them directly into Foxglove for visualization, and immediately surface any critical issues – all while they’re still on-site with the customer.

Increased bandwidth to take on new customers

By adopting MCAP and Foxglove, Tangram Vision has been able to take stock of critical friction points in their customer onboarding and streamline each of them, one by one.

Instead of having to juggle different data formats, maintain in-house visualization tooling, or provide customer-specific integrations, Tangram can now rely on the Foxglove platform to do most of the heavy lifting. Every step that required Tangram's consultative knowledge has been standardized and simplified, so that they can spend less time holding their customers' hands and more time building cutting-edge perception infrastructure.

The Foxglove integration has also unlocked new sales opportunities for Tangram Vision. Since they no longer have to provide as much consulting, the entire customer onboarding process has become much more self-serve and sustainable than ever, meaning more bandwidth to pursue new leads and onboard more users.

Extending out-of-the-box visualizations with powerful customizations

Instead of spending their own resources building and maintaining a homegrown data visualization tool, Tangram Vision now relies on Foxglove's expertise to explore and analyze their customers’ data.

Thanks to Foxglove's extensibility, the team has been able to build on top of the general-purpose feature set to address their perception-specific needs. Some engineers have authored custom data exploration panels and arranged them into a shareable layout to visualize their customers’ calibration results. Even though these features did not come out-of-the-box, building these modular extensions were a relatively straightforward process that required much less work than building an entire visualization suite from scratch. Tangram engineers have been able to apply this one summary view across a wide range of customers, sensors, and robotics applications, further streamlining their debugging and customer onboarding workflows.

Outcome

By adopting MCAP and Foxglove, the Tangram Vision team has been able to cut entire weeks out of their pilot onboarding process. They can now get concrete results for their customers within minutes of receiving their data, subsequently extend the proof of concept to days of deployment, and then set their sights on engaging with the next set of customers.

With this streamlined data collection and customer collaboration process, Tangram Vision no longer has to waste months of precious time performing repetitive data cleaning tasks or navigating each customer’s unique setup. Instead, their team can focus their efforts wholly on their actual mission – building perception tooling that helps robotics teams manage the growing complexity of their sensor systems.