The studio
Signal Foundry is a Toronto studio with an unusual org chart: a small human team, and a fleet of AI agents that never sleep. All of it points at one thing: verification work that has to be right.
The bet is simple: AI can now generate work at almost any scale, but the judgment that says which of it is right lives in people, and it’s scarce. So we build systems around the experts who have it. They do their job inside a working app; every accept, fix, and rejection becomes ground truth; and a crew of agents turns that judgment into a measurably better version overnight.
We run our own delivery through the same judgment loop we sell. The daily updates, the accuracy gates, the plain-language changelog: that’s how this studio operates, not just what it builds.
How we build
Agentic engineering
Autonomous coding agents implement, test, and iterate around the clock while humans direct. That's what makes a daily improvement cadence affordable in the first place.
Measurement first
Your team already knows what a right answer looks like. We build software that learns from them, keeps score on itself, and tells you plainly which work it can handle and which still needs a person.
Daily loops
Web-first delivery means we ship, measure, and improve every day: the feedback your experts give in the afternoon shows up in the product next morning.
Twenty-five years building ML systems for media companies, Pandora and SiriusXM among them. When the tool I need doesn’t exist, I build it: seven issued US patents in personalization are the paper trail. Every system I’ve built promised to learn from its users. The difference now is the loop runs daily instead of quarterly, and it scales with dollars, not engineering time.
Critical workflows, where a demo that works 80% of the time is worthless. I design the systems and the evaluation loops that close the last 20%.