BoolSi announces its $6M seed round
$6M Seed<br>Boston, MA<br>Private Beta Q3 2026
BoolSi announces its $6M seed round.
By Mihailo Isakov<br>Founder & CEO
§ 00TL;DR
We enable software developers to compile hotspots into custom hardware accelerators.
Software engineers can already get 100× speedups for their workloads. They just have to spend a decade learning to design digital logic first. We're building the compiler that removes that prerequisite.
Feed BoolSi a hotspot in C, C++, or any other high-level language, and out comes a custom-generated circuit and driver, in minutes instead of months. The target is reconfigurable silicon (FPGAs) sitting next to the CPU, and our initial focus is embedded developers in robotics, the people who feel every wasted microsecond in their product.
The core idea: we train machine learning (ML) models to learn a program's behavior, not its implementation. We've developed an architecture in which neural networks naturally converge into 100% accurate digital circuits, analyzable and optimizable with the existing chip design toolchain.
The AI isn't designing a chip. The AI gets solidified into a chip.
We've raised $6M led by Fine Structure Ventures (an F-Prime fund), with participation from Pillar VC, Fifth Quarter Ventures, and Coalition Ventures, to put this in the hands of developers who need better performance, lower latency, and faster time to market.
We're opening a private beta in Q3 2026. If your product is bottlenecked on a CPU or a microcontroller, we want to hear from you.
§ 01Problem
Chip design is closer to watchmaking than to writing software.
General-purpose CPUs are remarkably wasteful on any single workload. Every instruction has to be fetched, cached, decoded, and pushed through a pipeline designed to be okay at everything and great at nothing.
CPUs have to serially work around architectural limitations.
Custom hardware sidesteps all of that by trading silicon area for time. Instead of streaming a workload serially through a generalist pipeline, a purpose-built chip spreads the computation out in space: every operation gets its own gates, every data path its own wires, all running in parallel. The narrower the workload it has to support, the more aggressively it can be specialized, which is why purpose-built chips routinely run fixed workloads orders of magnitude faster than CPUs.
Field Programmable Gate Arrays (FPGAs) adapt their architecture to fit the problem.
So why isn't everyone using custom hardware? Because designing chips is closer to watchmaking than to programming.
In software, mistakes degrade gracefully. Bugs get skipped on most runs, exceptions get caught, stack state clears every time you exit a function, and you can reason about a single point of execution at a time. Chips on the other hand are extremely complex distributed systems with millions of subcircuits all talking to each other a billion times a second, often for years without downtime. Every part of a chip is tailor-made for its neighbors, more like cogs in a mechanical watch than functions in a library. You can't plug in a module the way you import a library, because you also have to know exactly when it does what it does. Does this multiplier return a result in one cycle or four? Does it finish faster if one of the inputs is a zero?
The result is that hardware is painful to build, worse to debug, and treated by the companies that build it as cherished IP that mustn't be shared. Open-source resources are thin, the cost of entry is enormous, and time to market is measured in years.
The vast majority of software engineers have no viable path into hardware, even when their applications would massively benefit from it.
§ 02Approach
Introducing the hardware glue layer.
Think of the scene in Episode IV where Chewbacca fixes C-3PO by jury-rigging a head onto a neck and splicing a couple of (maybe random?!) wires. That is what hardware engineering should feel like. You should be able to grab the parts, wire them up, and iterate until it works.
I started BoolSi because I wanted to live in a world where I can build chips at the same speed I write software. In software I can pull in a few libraries, glue them together, and solve my problem in an afternoon. LLMs have further collapsed the distance between an idea and working code, and the pool of people who can ship code has never been larger. We're nowhere close to that in hardware, and the gap is growing. General-purpose code is now reappearing as the slow part of the stack, and the next compression is from working code to working silicon.
Today, hardware is specified in prose documents and then manually translated into digital logic by an engineer who interprets the spec. But a program that exercises the desired behavior is the best kind of spec there is: exact, executable, exhaustive, and already machine-readable. What's needed is a second compiler stage that takes that code and turns it into circuits, closing the...