AIApplications Playbook: How Hardware Teams Can Put CopperPilot's Epsilon AI PCB Design Agent to Work

By David Nakamura | AI & Data News — AIApplications desk

Most coverage of AI in electronics stops at the funding line. This one starts where the work happens: an engineer holding a board brief, a stack of datasheets and a schedule. CopperPilot has raised a $390,000 pre-seed round led by Neon Fund, joined by U.S.-based angel investors, and the round matters mostly because of its timing — it arrives as version 1 of the company's AI design agent, codenamed Epsilon, is opened to hardware engineers.

That timing is the useful part for anyone deciding how AI gets used in a hardware process. It is the first moment when teams outside the founding circle can place an agent inside a real board cycle and judge what it contributes. The traction the company reports suggests the tool is being run rather than merely tried: 25-fold growth in weekly active users since August 2026, alongside more than 6,000 design tasks, or artifacts, run on its platform to date.

CopperPilot was founded in October 2025 by Aditya NG, Mohit Bagur, and Anish Harsha. It is building an AI agent that automates parts of the PCB design process: interpreting requirements, selecting components, drawing schematics, routing boards, and checking designs before manufacturing. The company's stated goal is to ensure that “no hardware team is ever blocked because a board is too hard to design.”

Pictured left to right: Mohit Bagur, Anish Harsha and Aditya NG, CopperPilot's co-founders.

The pain the agent is aimed at

Speaking in an exclusive interview with EE Times, co-founder and CEO Aditya NG traced the idea to his experience working on hardware projects, including autonomous vehicles. “On almost every autonomous vehicle team I worked with, someone would eventually say it: A compute unit for six cameras is too hard to design, or a drive-by-wire unit is too much for us. Many teams stopped right there. The ones that kept going spent months staring at datasheets and paying for mistakes on board re-spins,” he said. “I built CopperPilot so that no hardware team is ever blocked because a board is too hard to design,” he said.

Where the agent sits in your existing toolchain

Instead of requiring engineers to migrate their designs to a new environment, the startup automates workflows within the EDA tools they already use. KiCad, Altium, and LTspice are supported, with its strongest integration centered on PCB design and firmware workflows. CopperPilot also said Epsilon can be used through AI coding agents, including Claude Code, Cursor, and Codex, letting developers hand off board-design tasks from within those environments.

Talking to those tools is not one mechanism but several. To interact with EDA tools, CopperPilot uses a combination of official application programming interfaces (APIs), direct file editing, and other methods. “The method varies according to what works for a particular tool or task,” Aditya said.

Operation path: running Epsilon on a real task

The sequence below follows the capability set Aditya described, in the order a design move normally happens.

1. State the requirement in plain English. Aditya said the agent can take plain-English design requirements and read component datasheets and manufacturer specifications.
2. Choose parts from a set of options, not a single answer. It can identify suitable components and present alternatives for the engineer to choose from.
3. Generate the schematic layer. The agent can then create schematics and custom symbols or footprints.
4. Move onto the board. It can also proceed to PCB placement and routing.
5. Run the rule and manufacturing checks. It can check the design for electrical rule check (ERC) and design rule check (DRC) violations and manufacturing constraints.
6. Read the review report, then sign off. Engineers can review or intervene at individual stages, and for a design being readied for production the report splits blocking issues from everything else.

Two behaviours decide whether this reads as an assistant or an autopilot. Architectural decision-making is supported, he said, because the agent seeks clarification when requirements are incomplete or components do not meet design constraints. When requirements are sufficiently clear, the agent can execute a sequence of design tasks autonomously, while engineers can still review or intervene at individual stages.

Cross-tool context is where the leverage compounds

Cross-tool workflows are also being developed. In traditional work, an engineer may need to inspect several tools and pieces of information separately, including firmware code, PCB design, and communication protocols. By connecting these pieces of information, the company aims to identify possible interactions and help engineers reason through problems that span hardware and software. According to Aditya, the added value is not limited to the agent's ability to analyze a circuit board or write firmware; it also includes considering how different parts of an electronic system affect one another.

Validation is the hard part — and the most useful part

Producing a schematic or PCB layout is only part of the work. The design must additionally satisfy electrical requirements and the constraints of the manufacturer that will build it. According to Aditya, ERC and DRC checks provide a baseline, but they cannot catch every problem.

“While connecting two computers or two microcontrollers over a serial connection, you are supposed to connect the transmitter of one device to the receiver of the other, and the receiver of the first device to the transmitter of the other,” he explained. “It is very easy to reverse these connections, and it happens to many engineers. This is where CopperPilot really shines. It can catch mistakes that ERC and DRC checks cannot detect.”

Flagging problems alone is not enough. Engineers also need to know which issues matter most, what to fix first, and what remains unresolved. “For engineers preparing a design for manufacturing, the agent can produce a review report that separates blockers from non-blocking issues, prioritizes fixes, and explains the remaining problems,” Aditya said. “It may also identify design changes that could reduce cost.”

Because hardware design lacks a single equivalent to a software compiler or linter that can confirm whether an entire design is correct, he explained, the startup built internal testing and benchmarking infrastructure. “With CopperPilot, we have internally built out our own harness, benchmarks, and tooling to validate the design,” he said. “The agent can read the datasheet, manufacturer specifications, and design tools, with the startup testing how well this process works through its harness and multi-agent framework.”

Manufacturer-specific test suites are also developed by the company, which maintains component information and examples of design failures. The team's understanding of how generated designs can fail grows as the agent is used and tested.

How to read the evidence available today

Figures shared by Aditya showed that CopperPilot evaluated its system against approximately 49 test cases based on human-designed circuits. He said that the startup reported a task completion rate of 18% for its baseline system and 78% for CopperPilot. However, the baseline system was not specified in the information provided, and the results are based on a small, company-run benchmark that has not been independently verified.

The startup said in its press statement that it publishes a new open-source board design each day, with around 30 designs published so far. Engineers can inspect the designs in KiCad, giving them a way to examine the team's builds rather than relying only on product demonstrations.

Signal a team can inspect	What the company reports	What to verify before standardizing
Benchmark results	Task completion rate of 18% baseline versus 78% CopperPilot, over approximately 49 test cases	Which baseline, which task mix, and any independent replication
Open-source board library	A new design published each day, around 30 designs published so far, inspectable in KiCad	Whether those board classes match what your team ships
Usage momentum	25-fold growth in weekly active users since August 2026 after opening version 1	Whether usage survives contact with production deadlines
Work volume	more than 6,000 design tasks run on the platform	How much human correction each artifact still needs

Plans, team integration and where your data goes

As a B2B software-as-a-service (SaaS) company, CopperPilot offers plans for individual users, teams, and enterprise customers. For individual users, the offering includes a free Starter plan and paid Pro, Pro Plus, and Ultra plans. Paid team plans are available, while enterprise customers can contact the company about specific requirements and internal deployments.

Integration with existing tools and workflows is part of the proposition for enterprise customers. The company can also connect with GitHub and ticketing systems to draw on an organization's context and share knowledge. A reusable skill can be created by an engineer to check a design against company manufacturing standards and shared with colleagues, allowing teams to bring their accumulated engineering experience into the agent's workflow.

For teams, that reusable-skill mechanism is the practical difference between one person getting value and a department getting value: an internal standard becomes an executable check instead of a slide deck.

On data handling, CopperPilot said that, for organizations working with proprietary designs, design data and chat history are stored locally on the user's computer under its standard individual and paid plans, while information is sent to servers and a large language model (LLM) for processing. “CopperPilot has a zero data retention policy after an interaction,” Aditya said at the time of the interview. “We also offer on-premises hosting as an option, although it has not yet been deployed for a customer.”

Who it is sold to, and where the users sit

Individual engineers, startups, design service providers, and larger businesses are all served by the company. IoT, health and medical technology, battery technology, electric vehicles, and other automotive applications are among its customers and target markets. According to CopperPilot, customers are located in the U.S., U.K., and Europe, while users are also present in India and other countries. The company has dual headquarters in Bengaluru, India, and Houston, Texas. Consumer electronics, renewables, energy storage, and robotics are identified by the company as further sectors that could benefit from its technology.

What the new funding buys in practice

No revenue figure or user-to-paid-customer conversion rate was disclosed by CopperPilot. According to the company, the new funding will support product research and engineering, improve the agent's speed and reliability, and expand the engineering team.

Common misconceptions to drop before rollout

• “Rule-check clean means manufacturable.” ERC and DRC checks provide a baseline, but they cannot catch every problem; pairing-level mistakes still need either the agent's review or an engineer's eye.
• “The agent replaces the design owner.” The described workflow keeps engineers able to review or intervene at individual stages, and the agent asks for clarification when a requirement is incomplete.
• “A completion rate is a correctness rate.” The 78% figure is a task completion rate on a small, company-run benchmark that has not been independently verified.
• “Local storage means nothing leaves the machine.” Under standard individual and paid plans, design data and chat history stay on the user's computer, while information is still sent to servers and an LLM for processing.
• “Thirty minutes is any board.” The about 30 minutes figure refers to an AI camera board, one specific class of design rather than a universal claim.

Action checklist for a first agent-assisted PCB pilot

1. Pick one repetitive board your team has already shipped, so a correct result is easy to compare against.
2. Feed the requirement in plain English together with your datasheet set, and log every clarification question — those are your undocumented requirements.
3. Require the review report on each artifact, and route blocker issues to a named owner before any respin budget is approved.
4. Seed one standing test that rule checks miss, such as the transmitter-to-receiver pairing between two boards, and keep it in the suite.
5. Encode one internal manufacturing standard as a reusable skill and share it with colleagues instead of leaving it with a single engineer.
6. Confirm data handling against your own policy: local storage of design data and chat history, what is sent to servers and the LLM, retention after an interaction, and whether on-premises hosting is required.
7. Judge the pilot on whether it shortens the path from an idea to a manufacturable electronic product, not on how good the demo looked.

The next test is reliability

Making the agent reliable enough for engineers to use in their day-to-day work is CopperPilot's immediate challenge, while expanding adoption among individuals, startups, and larger design organizations. Shortening the path from an idea to a manufacturable electronic product is its longer-term goal.

As Aditya put it, “Today, CopperPilot designs an AI camera board in about 30 minutes.” Drawing on his experience designing electronics for a student race car, he concluded, “The racecars of the future will be designed with CopperPilot.”

For application teams the practical conclusion is narrower and easier to act on: treat Epsilon as a reviewable assistant inside the EDA tools you already own, use the published open-source boards as your own first test set, and let avoided respins — not headlines — justify the line item. For questions about this AIApplications piece, reach the AI & Data News desk at editorial@aidatanews.com.