Adding AI to a robot can take months to develop a field deployable solution, even for experienced software developers . There is a faster way.
The most expensive software in robotics is the software nobody wants to write.
Drivers. Data Management. AI Pipelines. User Interfaces. The foundational code that customers never see. The kind of work that requires a team of specialized embedded engineers and somewhere between six months and a year of their time. Every robotics team writes it. Every robotics team ships late because of it. Until recently, every team had no choice.
That last part is no longer true. The software layer between the hardware and the application now exists. NEPI AI and automation software takes care of much of the under-the-hood plumbing most robotic and automation solutions require. NEPI provides plug-and-play drivers for cameras, navigation sensors, motors, lights, and control systems, auto detection and orchestration of AI models, built-in automation applications, and an intuitive browser-based user interface for connecting from remote network connected PC’s. NEPI installs and runs as a Docker container on top of the edge AI chip’s naive operating system, allowing anyone to download and get working in minutes with no computer programming experience needed. NEPI also includes a simple pull, deploy, and build system for downloading and customizing the source-code from our NEPI Github repository.
Most robotics teams don’t know it yet, which is why most robotics teams are regularly behind on getting their AI automation into the field.
Here is what the new workflow looks like for the teams that have caught on.
Why It Used to Take Months
Until recently, adding AI to a robot meant building four things from scratch.
Drivers, for every sensor on the robot. Cameras, lidar, sonar, IMUs, GPS, pan-tilts, motors. Each one a different protocol, a different timing problem, a different reason for an engineer to spend a week debugging instead of building.
An AI orchestration. Loading models, connecting data streams, and processing the results. The plumbing that surrounds the model.
Automation logic. The model detects something. The robot has to do something about it. The connection between those two events used to be custom code, every time, on every project.
An operator interface. Robots do typically have mice, keyboards, and screens. Someone had to build a UI that ran somewhere else and talked to the robot over the network.
None of these are what makes the robot interesting. They are the floor it stands on. Building that floor used to require a team of specialized embedded engineers, six months to a year of their time, and a tolerance for shipping late. Every team that wanted a smarter robot had to lay their own floor.
What Quietly Changed
The same shift that opened the personal computer to non-specialists has reached edge AI. A small number of software platforms have shipped that handle those four layers. The team building the robot does not have to build them anymore.
At Numurus, we built NEPI for exactly this. It is a software platform that handles the layer between the hardware and the application. Plug-and-play drivers for cameras, sensors, motors, and control systems. AI model management. Built-in automation tools. A browser-based interface served from the device itself. It installs as a Docker container on standard edge hardware, including NVIDIA Jetsons, Raspberry Pis, and Linux laptops. The source code is available.
Other platforms exist in this category. What matters is that the layer is real. Here is what the workflow looks like in practice.
Step 1. Install the Platform
A single command pulls the container and starts the services. On a Jetson Orin or a Linux laptop with the required free space, the install completes in minutes. No specialized hardware purchase, no proprietary stack, no week of setup.
Step 2. Connect Your Sensors
Plug in a USB camera. The platform recognizes it. The data stream appears. Connect a supported lidar, sonar, or IMU. Same pattern. The drivers are already there. The part that used to take weeks per sensor takes seconds.
Step 3. Load Your AI Model
Drop your model file into a shared folder on the device, along with a small metadata file. The platform detects the model, makes it available to the running application, and connects it to live sensor streams. Common open frameworks like YOLO work out of the box, so you can be running real object detection without training anything yourself.
Step 4. Open the Interface
From a laptop on the same network, point a browser at the device’s IP address. The robot serves its own UI. Live sensor feeds. Model output. System status. Configuration controls. No app to install. No additional software on the laptop side. The same interface is what the operator will use once the robot is deployed.
Step 5. Add Automation
If you want the robot to act on what the AI sees, you write a short automation script or use one of the built-in templates. Detect a person, send an alert. Detect a defect, flag it. Detect an obstruction, stop the motor. This is a low-code step. You write the logic that is specific to your application, not the plumbing that connects everything.
What “Minutes” Actually Means
Worth being honest here.
Getting the platform installed, the sensors connected, an AI model loaded, and the browser interface working is a few minutes per step. A small system can be up and running in under an hour.
Building your specific application, the part that makes your robot do what it actually needs to do for your customer or your mission, takes longer than that. There is no platform that does that part for you. The work that is yours to do is still yours.
What changed is that you no longer spend the first six months on the infrastructure. You skip the plumbing and start with the application.
Who This Changes Things For
For experienced robotics teams, the math is simple. The months you would have spent on the infrastructure layer are now available for the differentiated work. Senior engineers stop maintaining drivers and start building product.
For new teams, the change is bigger. A team without a dedicated embedded software hire can now ship an AI-enabled robot. Capability is no longer gated by team size or hiring budget.
For students and educators, the change is the biggest. A middle school robotics team can put a real AI model on their competition robot. A university capstone can build a working autonomous system in a semester instead of two. The barrier to building edge AI used to be a senior engineer with a decade of embedded experience. That barrier is gone.
The Window Is Open
Adding AI to a robot used to be a six-month project. For teams paying attention to what has shipped, the setup is now minutes and the path to a working product is a fraction of what it used to be.
The teams that adopt this approach in the next twelve months are going to ship faster than the teams that do not. The infrastructure they were going to build is already built. The only question is whether they want to keep building it themselves.
If you want to see how this actually works on hardware you already own, download and try NEPI in minutes. With automated installation scripts, anyone can download and try NEPI in minutes. Check out the ‘Get Started’ page at www.nepi.com.
Jason Seawall is the founder and CEO of Numurus, makers of the NEPI edge AI platform. He previously founded BlueView Technologies, which was acquired by Teledyne, where he served as VP of Technology overseeing innovation across Teledyne’s marine technology group. Learn more at nepi.com.
