An AI Company That Wants Ordinary People to "Hard Control" Shenzhen's Electronics Hub Raises Nearly 100 Million Yuan

"Another kind of equalization." By Zhiyan Chen

While software engineers have grown accustomed to pair-programming with Cursor, the world of hardware engineering remains decidedly classical —

If you want to build an app, AI can now spit out a runnable code framework in minutes. But if you want to design a simple piece of smart hardware — say, an eye-protection lamp or an electronic toy — you still face a grueling process: component selection, schematic drawing, PCB design, prototyping, soldering, debugging, and more.

"A consumer electronics product of moderate complexity needs a circuit board with up to 300 components. For an experienced engineer, going from zero to finished product typically takes 20 to 30 days," Qunsong Ye, founder of Index Technology, told Waves. What his company does "can compress that to one to two days."

Waves has learned exclusively that AI hardware design generation platform Index Technology recently completed a nearly 100 million yuan Pre-A funding round, led by Yunqi Capital, with participation from Yuzun Capital and Shangshi Capital. This follows its 2024 angel round, completed shortly after the company's founding when it had only a demo, co-led by Qifu Capital and Huagai Capital.

What Index Technology aims to do can be summed up in one sentence: use AI to generate electronic circuit design schematics and accompanying embedded code, achieving "unattended" hardware R&D.

Founder Qunsong Ye graduated from the University of Electronic Science and Technology of China in 2008. Early in his career, he worked at Rigol Technologies making instruments and meters; he later joined Honeywell, where he led its innovation lab seeking novel electronics solutions nationwide.

In 2019, Ye chose to start his own company, founding Miji Technology, focused on AI education hardware and "AI companion reading" products. It was once a glamorous track — the team secured partnerships with leading customers like Zuoyebang and New Oriental. But just two years later, the "double reduction" policy landed.

"The education clients we'd ranked in our top tier basically all collapsed. What survived were those we'd considered third-tier — companies doing traditional hardware upgrades." Correspondingly, Miji Technology's incoming demands became excruciatingly fragmented and technically trivial — a flood of repetitive tasks like "make the light turn on" or "add a button interaction" poured onto the "ambitious" R&D engineers in Ye's team.

"People from R&D backgrounds mostly don't defer to each other — nobody wants to do boring work." This extreme hunger for efficiency, combined with the pressure to find a way out amid "low-end repetition," drove Ye to begin exploring automation internally. He divided employees' time into three parts: one chunk for survival business, one for repetitive labor, and whatever remained had to yield two hours daily for "crazy experiments" in automated generation.

It was this existential crisis that unexpectedly incubated the embryo of Index Technology in 2023. Ye chose a path of "changing lanes to overtake" — rather than trying to rebuild an EDA (electronic design automation) within the existing rules, he would use AI large models and a black-box approach to directly generate results.

Simply put, what he wants to build is an AI company that lets ordinary people "hard-control" Huaqiangbei.

In Ye's endgame vision, everyone in the future will have something like J.A.R.V.I.S. from Iron Man — an intelligent steward that transforms personal needs and ideas into real hardware products. Ye believes that when AI drives the barrier to hardware design close to zero, human creativity will no longer be shackled by tedious engineering details.

Recently, Waves sat down with Qunsong Ye —

Part 01

The "New Energy Vehicle" of Hardware Design

Waves: EDA (Electronic Design Automation) has existed and evolved for decades. How does what Index Technology is doing differ?

Ye: The current EDA "big three" (Synopsys, Cadence, Siemens) control 80% of the global market. They've built an extraordinarily complex rule system. If you try to make a "better automation tool" within their rules, that's a dead end. Because they wrote the rules — if automation could be solved within that framework, they'd have done it decades ago.

Yet engineers' pain points remain unaddressed. Existing EDA tools are essentially still "drawing-assistance" software. Engineers still spend 80% of their time on low-value repetitive labor like "make the light turn on" or "connect this wire" — tasks with no intellectual challenge.

Competing along traditional EDA lines offers absolutely no chance. So we chose to "change lanes and overtake." After founding Index Technology, we proposed AI For Hardware (AFH) — not relying on traditional EDA rules, but using AI large models and a black-box approach to directly generate results.

Waves: So you want to use AI to disrupt the traditional EDA giants?

Ye: I wouldn't say disrupt. Let me use the auto industry as an analogy: if China's auto industry tried to catch up with Germany and Japan on internal combustion engines, that would be extremely difficult. But "new energy vehicles" created an opportunity to overtake on a curve — completely switching tracks, not competing on engines but on batteries and intelligence. You could say we're building the "new energy vehicle" of electronic design.

Waves: Specifically, what is Index Technology's current product? What level can it achieve?

Ye: Our core product is an AI-based automated design platform that can directly generate circuit board design schematics and accompanying embedded code from requirements. Currently, we mainly focus on circuit boards of four layers or fewer — this already covers the vast majority of consumer electronics products.

To give a concrete example: a board with under 300 components. If a human engineer does it, from receiving requirements to component selection, schematic drawing, and PCB layout, it takes about 20 to 30 days. Using our platform, it now takes one to two days.

More critically, hardware R&D has one massive pain point: EMC testing (note: a comprehensive evaluation of electronic products' electromagnetic interference and immunity, one of the most important quality indicators for hardware). It's the nightmare of every electronics designer. A human-drawn board might function fine, but fail radiation testing at the lab — then it's back to revise layout, revise traces, another half-month gone. Typically a product needs three to five iterations to pass. But our product, trained on massive amounts of historical success data, generates boards with very high first-pass rates on radiation tests — usually fewer than two iterations.

Waves: This sounds quite similar to Vibe Coding, which has been very hot in software design recently.

Ye: Exactly. Vibe Coding dramatically accelerates software development through a rule-free, feel-based conversational approach. I think hardware should have the concept of Vibe Design or Vibe PCB too. Right now engineers' R&D processes are very rhythmic and bounded, but that also limits the speed of innovation. We want to reconstruct the entire electronics design R&D process, letting engineers become true decision-makers without spending excessive time on tedious procedures.

Waves: Many vertical platforms built around AI face the question of whether they'll still exist after general-purpose large models iterate. Do you think what you're doing has high enough barriers?

Ye: Current general-purpose large language models are essentially probability-based "replication" and "assembly" — they don't truly understand the intent and physical rules of circuit design.

At the intersection of AI and electronics, two enormous barriers already exist:

First, data is extremely fragmented and dirty. After decades of rapid development, the electronics industry has matured considerably, but there aren't many high-quality open-source schematics and PCB source files online. Some leading electronics manufacturers have accumulated data files for single or multiple product categories through years of沉淀, but because AI and electronics technology developed relatively independently for many years, this data isn't "palatable" to AI. The foundational corpus we've built for the electronics industry is specifically designed to "feed" AI — requiring massive cleaning and synthesis work.

Second, there's a lack of evaluation baselines. GitHub has海量 code, but there's a dearth of hardware-strongly-related embedded code沉淀. For pure software development — say, AI writing webpage code — you run it and know if it's right or wrong. But embedded code errors are burned into chips; you can't directly evaluate them in the cloud. We're building evaluation benchmarks for embedded code, which is a blank space in the industry. Our current architecture is based on reinforcement learning plus MoE, combined with knowledge graphs. It's not just about drawing the picture — it's about understanding the constraints of the physical world.

Waves: If AI dramatically improving efficiency becomes reality, would hardware giants like DJI or Bambu Lab do what Index Technology is currently doing?

Ye: Companies like DJI and Bambu Lab, with their extremely strong product definition capabilities, are actually our best potential customers. They use traditional EDA tools to draw boards in order to quickly get products to market. They won't build their own EDA just because they find the tools unpleasant to use — that doesn't make business sense; it's a different track entirely.

I believe that when our product has sufficient validation, compared to building their own tools, they'll prefer to partner with us.

Waves: Index Technology isn't your first startup. Has your previous experience influenced this one?

Ye: My previous company was called "Miji Technology," doing AI companion reading hardware for education. In 2021 things were looking up — we'd secured意向 from major clients like Zuoyebang and New Oriental. But on July 24, 2021, when the "double reduction" policy dropped, these top-tier clients basically collapsed instantly, all business收缩. What rose instead were some third-tier clients — traditional hardware manufacturers looking to transform.

To survive, we started taking their orders. But the problem was, most of this business was extremely繁琐, technically trivial "dirty work." The team started getting emotional. Everyone was from R&D backgrounds, coming from big corporate labs wanting to do cool things — instead they were spending days drawing simple boards, tweaking simple functions. Everyone was憋了一口气.

At the time, I told the team: since everyone thinks this is simple, why can't we solve these uninteresting tasks through automation? So we made a rule: divide each day into three parts — one for core business survival, one for boring杂活, and whatever time remained must yield two hours dedicated to "碰撞" in the direction of automated generation.

What Index Technology is doing now was essentially "forced out" by this.

Part 02

Achieving Per-Capita Revenue of Hundreds of Millions of Dollars

Waves: Who are the customers actually using your product now?

Ye: Primarily B-end customers. Because our core advantage is "fast." Many clients come to us when product launch timelines are tight or their original方案 has problems. Even if we quote above industry average, they're willing to pay because we solve urgent needs. For example, we have an eye-protection lamp client who initially gave us just one or two products to test; now 80% of their products are produced through us. Their R&D team basically only needs to handle验收. To date, we have about forty to fifty enterprise users, with annual orders exceeding 80 million yuan.

Waves: From a capital perspective, doing toB in China isn't considered very "sexy." You're either stuck on payment willingness or frozen in customized services. How do you solve this?

Ye: Doing to B is fundamentally about accumulating data — B-end customers provide large volumes of real data. Next step, in the coming 2026, we'll extend customer reach to large numbers of engineers, letting them dramatically improve efficiency and focus more energy on innovation.

We plan to切入 through maker communities, like Elecfans, JLCPCB community, and overseas open-source hardware communities. These people love to tinker and have the highest tolerance for tools. Get them using it to generate simple modules — filters, small appliance control boards. Through these makers, gradually penetrate to professional engineers, system architects. When an engineer discovers our tool can compress two weeks of work into two hours, he'll naturally bring this tool into his company.

Ultimately, we hope this platform can go to C. After models have沉淀 data from enterprise and professional customers, error rates will further decrease — then ordinary people can directly generate a hardware product through conversation.

Waves: Will there really be that many ordinary people who want to design hardware themselves?

Ye: I don't think everyone wants to become an inventor — that's too hard. But when I was doing innovation tree at Honeywell in 2016, I'd receive innovative ideas from all over the country every day.

Put simply, everyone has an idea — "I want to do..." This idea ultimately lands on clothing, food, housing, transportation, or software/hardware products. Software is already convenient; hardware is slow because the barrier is too high. Many innovative ideas are subconsciously strangled. I believe that if the barrier is low enough, creative demand will emerge.

Waves: A kind of technological equalization?

Ye: I believe so.

Waves: Hardware is very different from software — software just needs to run on a computer or phone, but hardware has to be a physical object.

Ye: We're seeing companies like Bambu Lab in 3D printing, and even printed circuit board technology, maturing. And on our platform, users don't need to worry about these. Through our downstream supply chain布局, what we can definitely achieve is: user inputs requirements, drawings are automatically generated, after refinement they input their shipping address, and the rest is waiting for the delivery.

Waves: Whether for professional users like engineers or C-end ordinary people, the generated drawings could be all over the map. Can today's manufacturing produce so many non-standard products?

Ye: Decades of smart manufacturing development have basically solved this problem — rapid production according to design drawings is fundamental capability for many manufacturing enterprises.

Take JLCPCB and HQPCB, with whom we have deep partnerships — they're already very leading in "produce-to-drawing," and thanks to their AI panelization technology, they can complete production of thousands or even tens of thousands of drawings from different customers in 24 hours. So at this stage, manufacturing is not the key factor limiting electronics product birth efficiency (though it once was).

Only when technologies or platforms like AFH solve the efficiency problem in the R&D环节 can electronics product birth speed potentially be constrained by manufacturing — but I believe by then there will certainly be faster ways to complete manufacturing. We've also discovered overseas companies researching how to complete PCB bare board fabrication within two hours based on entirely new drawings, and complete PCBA soldering and testing.

Waves: What's the current team size? If you develop toward to C in the future, will you become a labor-intensive company?

Ye: We're currently under 50 people. I've set a strategic rule for the company: headcount never exceeds 100. I truly believe that with AI加持, a company where per-capita revenue reaches hundreds of millions of dollars is possible. I still write code myself — tools I can build in a day might have taken months in the past. Company manpower is mainly invested in R&D and tool optimization, not linearly scaling headcount with order volume.

Waves: Adding manual optimization on top of AI-generated drawings is one pricing strategy. Wouldn't more users increase manpower needs?

Ye: This really depends on business形态. If we only do to B, we would indeed need more people for delivery and communication — but this is precisely what we'll strategically "retreat" from. To B business at this stage is for cash flow and data, but we won't无限堆 people just to grow revenue.

For to C or to Professional business, manpower needs won't grow linearly with user volume. Because reviewing drawings requires understanding the other party's design logic from scratch — very time-consuming. As model capabilities improve, the proportion requiring human intervention will keep decreasing.

Layout by Nan Yao | Images from Unsplash

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