Built by engineers who got tired of demos.

AIOpsLab started with a single question: what happens when you apply real infrastructure rigor to AI applications — not notebook experiments, not prototypes, but systems that actually run and generate value?

Most AI tools in production are demos with a deploy button. They work on clean data, fail silently on edge cases, and get retired after a conference talk. We build the opposite: applications that ingest messy real-world data, fail loudly when something is wrong, and document every decision made along the way.

What we focus on

Our current research sits at three intersections:

• Security intelligence — applying ML to cyber security, specifically WAF, and adversarial traffic detection in ways that hold up against real attackers, not benchmark datasets.
• Market intelligence — using alternative data (satellite imagery, webcam feeds, AIS transponders, options flow) to build signals that traditional financial data misses.
• Operational AI — the infrastructure and engineering decisions behind deploying intelligence systems that run continuously at near-zero cost.

How we publish

Research is published in two forms: Field Notes (short, opinionated takes on specific problems) and Deep Dives (full technical breakdowns of architecture, data pipelines, and model decisions). Nothing is gated.

Who we are

AIOpsLab was founded by an engineer with a background in distributed systems, security infrastructure, and applied ML. The work is done in the open, with contributors credited fully on every project and article.

If you want to contribute, the collaboration page is the right place to start. If you want to follow the research, everything is here.