Why your data stack is almost right but still holding you back

Operating in a World Built on Data

Data is everywhere. From product development to fleet operations, the flow of information is what powers modern decision-making. For robotics and autonomous mobile robots (AMRs) in particular, the stakes are high, and the volume is massive.

Teams today are well aware of this. Most have already invested in the tools they need to process, move, and analyze multimodal datasets across their organization. Whether it’s structured pipelines for ingesting logs, cloud infrastructure for storing sensor data, or dashboards for reviewing key metrics, the components are in place.

And yet, something still feels off.

For all the progress made in scaling infrastructure, many teams still find themselves slowed down. Logs take too long to explore. Key events are hard to isolate. Cross-team alignment requires extra effort. In short: the data is there, but turning it into action is still work.

That’s not because the tools are bad. It’s because making them work together seamlessly is harder than it looks.

Building the Right Stack (and Making It Work)

In robotics and autonomy, most teams don’t start from scratch. While a few large organizations still build their own in-house systems to manage robot data flows, this is becoming the exception rather than the rule. These setups can be powerful, but they’re often complex to maintain and rarely optimized for speed or cross-team usage.

Today, the vast majority rely on modular workflows.

A typical setup combines existing tools and robotics platforms, each covering a piece of the data puzzle:

• Data is recorded using ROS or ROS 2, two common versions of the Robot Operating System, in formats like .bag or .mcap.
• Files are stored in cloud buckets such as AWS S3 or GCP.
• Visual tools help teams replay, inspect, and debug data locally or remotely.
• Annotation, labelling, and QA are handled through lightweight scripts or open-source dashboards.
• Developers stitch everything together using CLI tools or automation frameworks.

This patchwork approach offers flexibility and quick deployment. It comes with tradeoffs.

Every added component creates a new layer to align, monitor, and scale.

And while each tool works well on its own, combining them into a smooth, collaborative workflow remains an open challenge.

Closing the Loop: What Teams Really Need

Most robotics teams today have managed to build functional workflows.

Data is recorded, stored, and sometimes replayed for debugging or analysis.

With the right tools and enough technical support, these systems offer a decent level of control, at least on the surface.

But in practice, some challenges persist.

Even with structured pipelines, teams often find themselves:

• Collecting too much data without being able to act on it
• Spending hours locating key moments inside massive logs
• Working in silos, with limited visibility across roles
• Struggling to reuse data efficiently across projects or departments

What is missing in a seamless add-on workflow gluing all existing components together.

That’s where dedicated data capture services make a difference.

Teams now look for solutions that can:

• Capture the right data directly at the edge, using event-driven data logging to avoid storing unnecessary volume
• Enrich each snippet with metadata to make search and filtering instant
• Offer an intuitive, shared interface, accessible to engineers, QA, ops, and PMs alike

At Heex Technologies, we’ve built exactly that.

Our platform integrates with your existing robot operating system and infrastructure, or runs as a standalone solution.

It doesn’t require changing everything. But it does change how fast you move, how clearly you see, and how much value you get from your robotics data.

Because at the end of the day, data is only as powerful as the way you use it.