CopperPilot raises $390K to launch AI agent for electronics hardware design

Automating the hardware loop

Designing the electronics for a hardware product is currently a labor-intensive manual process. Engineers must spend weeks reading component datasheets, choosing specific parts, and drawing out the schematics for a printed circuit board (PCB). This manual workflow often leads to mistakes that are only discovered after the boards are manufactured, resulting in a costly redesign known as a re-spin.

CopperPilot is attempting to automate this entire pipeline with its new design agent, Epsilon. The startup claims that while traditional electronics work can take between 6 and 15 weeks and cost up to $30,000, their agent can bring that timeline down to hours. The tool is designed to handle the heavy lifting of researching requirements, selecting components, and placing the connections on the board.

The company recently secured $390,000 in pre-seed funding to accelerate this goal. This capital will be used to expand their engineering team and improve the speed and reliability of the agent. As Indian Startup News reported, the startup already sees significant traction, with over 6,000 design tasks, or 'artefacts,' completed on the platform to date.

How the AI agent functions

Epsilon works by taking a plain-English description of a desired hardware component and turning it into a manufacturing-ready design. It doesn't just draw lines; it researches the necessary requirements and checks its own work for electrical and design-rule errors. For example, it can identify common mistakes like reversing the transmitter and receiver connections on a serial link between two microcontrollers.

The technology is built to live where engineers already work. It currently functions inside the open-source tool KiCad, with early support for Altium and LTspice. It also plugs into popular AI coding agents like Claude Code and Cursor. This allows developers to hand off hardware design tasks directly from their existing software development environments.

The startup has benchmarked the effectiveness of its underlying model against the Epsilon agent. In tests across 49 cases, the base language model scored 18.6% on its own, while the score jumped to 78% when paired with the Epsilon agent. To prove its capabilities, the team recently designed an eight-layer PCB for a Raspberry Pi AI camera in approximately 30 minutes.

Impact on robotics and IoT

The founders of CopperPilot built the startup based on their experiences in autonomous vehicles, robotics, and quantum photonics. They observed that many hardware teams were blocked from progress because designing complex units, such as drive-by-wire systems or multi-camera compute units, was too difficult. These teams often spent months staring at datasheets or lost money on failed board re-spins.

By lowering the barrier to entry, CopperPilot aims to help teams in the robotics and IoT sectors iterate faster. For instance, Dexory, a UK-based company building AI warehouse robots, noted that robotics companies depend on how quickly they can improve hardware. They suggest that tools like CopperPilot could move the hardware iteration loop from months to a matter of days.

The startup identifies several key industries that will benefit most from this automation. These include consumer electronics, renewable energy, and electric vehicles. By making it easier to design complex boards, the company hopes to ensure that hardware teams are no longer stalled by the difficulty of the physical design phase.

Market reach and roadmap

CopperPilot is targeting the electronic design automation (EDA) market, which is valued at $20.93 billion. The company operates on a software-as-a-service (SaaS) model, offering different tiers for individuals and enterprises. These plans range from a free Starter version to paid Pro, Pro Plus, and Ultra plans, as well as dedicated team and enterprise options.

The startup already has a global footprint with paying customers in the United States, United Kingdom, and Europe, alongside users in India. To build community trust, the team publishes a new open-source board design every day on LinkedIn. So far, they have published about 30 of these designs, which anyone can inspect using the KiCad software.

Looking forward, the company plans to use its new funding to strengthen product research and engineering. The goal is to make the agent more intelligent and reliable for large-scale industrial use. The founders believe that the next generation of hardware will be built using these types of AI agents to bridge the gap between a plain-English idea and a finished product.