How Blase Labs Helps Hardware Companies Deliver the Best AI Models to Their Clients

This post is written for business decision-makers. If you’re interested in the technical details, you can explore the open-source simulation we ran using the DeepWeeds dataset.

AI can be powerful, but only when it works in the real world.

If you’re a hardware company shipping devices into complex environments, fields, factories, and coastlines, chances are your “AI solution” looked great in a lab but breaks down in the wild. Why? Because most AI models are trained on static datasets. And the real world changes.

At Blase Labs, we solve this by giving your AI the ability to improve itself on-device, offline, and without an engineer in the loop.

The Problem with One-Size-Fits-All AI

Most machine learning deployments assume that the data at inference time will look like the data the model was trained on. But that’s rarely true in practice.

Cameras differ. Lighting changes. If you have a model for crop health, fields look different from one region to another, and even the crops vary. So a model trained in Iowa might underperform in Texas, or on a different drone. That performance drop directly impacts your product’s value.

Traditionally, the fix has been slow and expensive:

• Collect new data

• Upload it to the cloud

• Have a data science team retrain the model

• Redeploy it to the device

That’s where Blase Labs comes in.

The Blase Labs Difference

We designed Blase for edge hardware. It enables your devices to keep learning without ever needing to phone home.

We use techniques like semi-supervised learning to fine-tune your AI models directly on the device using new data collected in the field. We also support active learning loops that let users review and improve predictions via an optional UI, without needing ML expertise.

The result?

Your product continues to improve after delivery. Your customers see better results. And you save on cloud costs and engineering overhead.

Real-World Example: DeepWeeds Simulation

To demonstrate this, we ran a simulation using the DeepWeeds dataset, real images of plants taken across different locations in Australia.

When we trained a model on data from one camera and tested it on another, performance dropped drastically, just like what happens when a drone sees a new field. Using Blase’s self-learning system, we improved performance automatically, without needing any labels or cloud access.

You can read more about this simulation in our open source repo.

If you want to integrate offline, self-improving AI models on your hardware so your customers have the best intelligence, then send us a message and we'll discuss how we can help.