Insights

Lessons from the lab.

The methods, economics, and lessons behind AI that lasts in production — written by the engineers who build it, for anyone who wants to learn. Read online, or take the full paper with you.

Flagship
AppsTech Labs · Security

AppsTech Aegis

Security AI that runs inside your own network.

General AI tools try to do a bit of everything. Aegis does one thing: understand your security environment — vulnerabilities, threats, suspicious code, alerts — and get the details right, where general chatbots guess or invent. It runs entirely on your own infrastructure, so sensitive data never leaves your network, and it’s built to be efficient enough to run every day without runaway costs.

a.
White Paper

Demystifying AI Agents

Everyone is talking about AI agents; few can say what actually separates one from a chatbot — or what it takes to run one safely. In plain language: how agents perceive, reason, and act on their own, where they are already earning their keep in businesses large and small, and the five questions to ask before you let one touch your operations.

PDF · 6 pages · English & French
a.
White Paper

The Economics of AI in Production

Most AI projects don’t fail in the demo. They fail on the invoice six months later. This paper explains why AI costs spiral once a system goes live — and the four engineering disciplines that keep them from doing so. If you have ever wondered what an AI system will really cost in month twelve, start here.

PDF · 6 pages · English & French
a.
Research Paper

Cybersecurity Intelligence, Built From the Ground Up

Most “security AI” is a general model with a thin security coat of paint. We take a different view — and built the alternative. The case for cybersecurity models designed for the domain from the first token: specialized, efficient enough to run on your own infrastructure, and governed for dual-use from day one.

PDF · 5 pages · English & French
Interested in AppsTech Aegis™ for your organization? Start the conversation →