Sep 9, 2026 · 24 min · 8 segments
HelmGuard just raised a $7.3m Seed round led by Infinity Ventures and Frontline. Its AI agents handle a company's security and compliance work automatically, checking everything all the time instead…
Jack MillerGuestSeb JohnsonHostWe have to rebuild everything"? How do you react and, you know, building the defensive capabilities then?
I think there's a philosophical position then at technical angle on that, so maybe I'll speak to the first, uh, and let Jack s- speak to the second.
I think we've built the company, uh, on the thesis that at the application layer, you can just ride The wave of improving model capability, um, over time.
Um, and we do a lot for our customers in terms of, of optimizing cost and, and performance.
Um, and we think that's just going to increase, and ultimately we're one of the beneficiaries of the hundreds of billions of dollars being spent at the frontier, plus the amount, uh, the enormous amount of open source innovation coming.
Now, at the same time, you need to, uh, make sure that as new models c-come out, um, they're performing consistently with, uh, uh, with expectation, and you're running evals.
So for example, you know, Astra, I haven't, uh, looked specifically at the benchmarks to see if it does in fact beat Fable five point one.
Um, but one is like improvements to the frontier, and there are some bits of our platform where that, that helps, you know, particularly long-running, um, tasks.
But it, it helps a lot obviously with internal stuff, you know, software engineering.
Um, we've got kind of a fleet of virtual machines that we use now for development where we just like basically parallelize everything, um, and have this, this long verification loop and, and new models are incredibly useful there.
And you know, you see every year this kind of ten X marginal decrease for the same level of intelligence.
Um, and so that helps us immensely in the cybersecurity domain, um, because, you know, kind of the point of our platform is to move from a world where you sample, you just take a, a small sliver of an organization and work out in this part are they doing what they're meant to be doing, to a population sample.
We have to rebuild everything"? How do you react and, you know, building the defensive capabilities then?
I think there's a philosophical position then at technical angle on that, so maybe I'll speak to the first, uh, and let Jack s- speak to the second.
I think we've built the company, uh, on the thesis that at the application layer, you can just ride The wave of improving model capability, um, over time.
Um, and we do a lot for our customers in terms of, of optimizing cost and, and performance.
Um, and we think that's just going to increase, and ultimately we're one of the beneficiaries of the hundreds of billions of dollars being spent at the frontier, plus the amount, uh, the enormous amount of open source innovation coming.
Now, at the same time, you need to, uh, make sure that as new models c-come out, um, they're performing consistently with, uh, uh, with expectation, and you're running evals.
So for example, you know, Astra, I haven't, uh, looked specifically at the benchmarks to see if it does in fact beat Fable five point one.
Um, but one is like improvements to the frontier, and there are some bits of our platform where that, that helps, you know, particularly long-running, um, tasks.
But it, it helps a lot obviously with internal stuff, you know, software engineering.
Um, we've got kind of a fleet of virtual machines that we use now for development where we just like basically parallelize everything, um, and have this, this long verification loop and, and new models are incredibly useful there.
And you know, you see every year this kind of ten X marginal decrease for the same level of intelligence.
Um, and so that helps us immensely in the cybersecurity domain, um, because, you know, kind of the point of our platform is to move from a world where you sample, you just take a, a small sliver of an organization and work out in this part are they doing what they're meant to be doing, to a population sample.
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