What We Build and Why It Has to Work
Most AI demos are built to impress. Ours has to work.
We're Aegis Tech Ventures — a small team that builds production AI infrastructure for regulated environments. Healthcare, compliance-heavy platforms, multi-agent systems that have to be auditable and reliable before they're impressive.
The patterns we use come from three years of building Watch Our Own — a HIPAA-compliant, FHIR-native care coordination platform running multi-agent AI in production. Not a prototype. Not a pilot. Production, with real patients and real care teams depending on it.
That experience taught us a few things that most AI consulting skips over:
HIPAA isn't a checkbox. It's a design constraint that touches every layer — data models, audit trails, agent boundaries, LLM selection, and how you handle tool call outputs. You can't bolt it on afterward.
Multi-agent systems are only as good as their coordination layer. Agents that can't hand off state reliably, can't reason about what they don't know, or can't surface decisions to humans at the right moment aren't production-ready. They're demos.
The hard part is usually not the AI. It's the data underneath it. Getting clinical context — the right information, in the right shape, available at inference time — is where most healthcare AI efforts stall.
We write about what we're actually building here. Real problems, real decisions, real tradeoffs. If you're trying to ship AI into a regulated environment and you're tired of content that lives at the demo layer, this is the feed to follow.
Aegis Tech Ventures — production AI for regulated environments. Built by Peter Borodich and Giancarlo Paolillo.