AI that engineers silicon

Engineering has always been bounded by how much a team of experts can reason through. AI is changing that. We can now build systems that reason, plan, and carry out complex engineering work alongside the engineers who direct them. We founded Tayen because we believe this is the start of a new era in how things get built.

We started with advanced silicon. We rebuilt the design process around AI, end to end. The result is an AI-native design engine that spans the full stack, from architecture through digital, mixed-signal, and analog design. It produces chips an order of magnitude faster, using a fraction of the resources conventional flows require.

Covering the full stack matters because a chip is only as good as its architecture and only as fast to build as its slowest block. The most consequential decisions come first: what to compute, where data lives, and how it moves. Our engine embeds our own architectural innovations, so those decisions shape every circuit beneath them. Below the architecture, a modern chip combines compute, sensing, power delivery, and high-speed connectivity, and the hardest problems sit where those domains meet.

Chip design is among the most demanding problems in engineering, and among the most valuable. An advanced chip packs billions of transistors, patterned at the nanometer scale, that must work together within tight limits on power, timing, and noise. Once a design goes to fabrication, every decision is fixed, and a single mistake can cost months and millions of dollars. Every design can be checked, in simulation and finally in silicon, so an answer is either right or wrong. Getting it right takes experience. Our team brings decades of chip design and innovation, and many tapeouts. We know where designs fail, which results to trust, and what a working chip demands. That sets the standard our engine is held to.

It also takes true interdisciplinary depth. Chip design defines the problem, AI opens a new way to solve it, and software makes the solution reliable at scale. The breakthroughs come where these fields meet, so we built a team that works across all three, with people fluent in more than one. That judgment is what makes our AI trustworthy: it learns from signals grounded in how real chips succeed and fail.

Today, bringing a chip to market takes enormous capital, talent, and time. Our mission is to build AI that lowers that barrier, so more ambitious chips can be created, and built faster.

And we are putting that engine to its hardest test: silicon for the physical world, where machines must sense, reason, and act in real time.