Why we built Pi

I spent my career inside some of the most advanced product security and vulnerability research teams in the industry. We were good at what we did. Finding a critical vulnerability is a trophy, and I won't pretend it wasn't a thrill every single time.

But the longer I did the work, the more one pattern bothered me. We kept finding the same issues. Not similar issues. The same ones. We shifted left. We educated. We wrote the secure coding guidelines and ran the trainings. And a few months later, the same class of bug would surface again, in a different service, written by a different team. The trophy was finding the vulnerability. The real goal was making sure it never came back. We were excellent at the first and, like everyone else, struggling with the second.

The trophy was finding the vulnerability. The real goal was making sure it never came back.

Then developers started using AI to write code, and the distance between finding and preventing stopped being a frustration and became a different problem entirely. Patterns that used to resurface a few times a year now propagate in days, across more code, written by more people. That was the moment I left to build Pi.

But before I tell you what we built, it's worth looking at the path most organizations are taking instead. Because the most natural answer to this problem is also the one that quietly fails.

The broken assumption

The natural answer goes like this, and I would have reached for it too: take the traditional application security tools you already trust, now "powered by AI," and let them handle it. Faster detection, better analysis of complex vulnerabilities, even proposed remediations. On paper, problem solved.

In practice, it falls short, and the reason is simple: these tools are generic. They were built to scan any codebase, which means they truly know none. They see your code the way they see everyone's code, as a single snapshot, with no real knowledge of your architecture, your components, your infrastructure, or the decisions that shaped them.

That gap shows up in two expensive ways.

01 They can't reliably tell whether a finding is a real issue for you, because you can't judge that from one codebase in isolation. The same pattern can be critical in one organization and a non-issue in another.

02 The fixes they propose don't fit the way you actually build, so your engineers end up translating generic remediation into something that works in your environment.

And all that translation lands on people who are busy shipping. Developers use AI to move ultra fast, then spend that speed triaging findings that may not matter and reworking fixes that don't fit. They feel 10x. In practice, they're closer to 2x. The tools got smarter, but they didn't get any closer to your organization.

They feel 10x. In practice, they're closer to 2x.

If that were the whole story, it would be a productivity leak. Frustrating, but survivable. What makes it urgent is the timing: two shifts are unfolding right now, at once, and each one raises the cost of that gap.

Why now

The first shift is on the building side. The people shipping code are no longer only engineers. Product managers prototype. Analysts automate. Designers ship working features. Inside most companies, anyone with an idea can now turn it into running software, and many of them will never read the code they ship, let alone review it for security. That's not a criticism of them. It's simply the new shape of building.

The second shift is on the finding side. Frontier models like Mythos can now uncover real vulnerabilities, and widely available LLMs mean anyone can hunt for them and disclose what they find. That sounds like good news, and partly it is. But AI hallucinates, confidently, and today there is no efficient way to verify, validate, and remediate at the volume it produces. The result is an inflation of reports, where real findings hide inside piles of plausible-sounding noise. The industry already has a word for most of it: slop. Finding the needle was always the job. Now the haystack grows on its own, and every new model makes it grow faster.

Finding the needle was always the job. Now the haystack grows on its own.

Put the two shifts together and the math gets uncomfortable. More code, written by more people, reviewed less. More reports, filed by more humans and machines, verified less. And in the middle, security teams working with tools that don't actually know the organization they're defending. The surface is growing faster than any team can hire, and it isn't going to shrink back.

But there's a thread running through all of it, and it points somewhere. What's missing isn't intelligence. The models are plenty smart. It isn't speed. Everything moves faster than it ever has. What's missing, on the building side, the finding side, and the fixing side, is understanding. Specifically, an understanding of you.

What we believe

We believe that missing understanding can be built. And we believe that building it, not slowing anyone down, is how security keeps pace with everything else.

AI lets organizations build genuinely fast. Security can run at exactly that pace, not a sprint behind it. But that takes the one thing most tools were never designed to have: a deep understanding of how your company builds and breaks its own software. Your architecture. Your flavor of code. How an idea travels from a document to production, and where decisions get made along the way.

That understanding is exactly what AI agents need, whether they're validating a report, remediating an issue, or writing a brand new feature. With it, they can tell which incoming findings describe real risk in your environment. With it, they follow your guidelines, reuse the security components your teams already built, deploy on the right infrastructure, and stay true to the original design. Without it, they're fast strangers in your codebase.

We looked for that understanding in every tool we had used across our careers. It didn't exist anywhere. So we decided it should.

So we built Pi

Pi is that understanding, built into a system.

Pi learns how your organization writes, reviews, and ships software, then puts that knowledge to work at every step where generic tools fall short. When reports come in, Pi knows enough about your environment to separate real issues from noise, so your people only spend time on what's true. When a vulnerability is confirmed, Pi doesn't just patch the instance. It traces the root cause, finds every variant of that pattern across your codebase, and closes the entire class. And what your organization learns once becomes something it can't accidentally unlearn. Those lessons show up as guardrails in the IDE, in pull requests, and in the next feature an agent writes, so known issues stop coming back.

That's the loop I went looking for all those years ago: find it once, fix it everywhere, never meet it again. The outcome is simple to say and hard to fake: security that moves as fast as your development, and gets faster as it learns. Your teams stop re-triaging and re-fixing the same classes of issues. Your security function scales with your output, not your headcount. And speed stops being something you trade away for safety.

Find it once, fix it everywhere, never meet it again.

An invitation

We could keep describing Pi, but claims like these deserve proof, not paragraphs. If any of this sounds familiar — the recurring fixes, the noise, the quiet tax on your velocity — we'd love to show you Pi on your own code. Not slides. Your code, your patterns, your fixes, closed for good.

Reach out at contact@pi.security. We'll bring Pi. You bring the codebase.

We'll bring Pi. You bring the codebase.