Built for Azure teams that are done with half-loops

The problem with open-loop governance

Most Azure governance today is open-loop: a tool scans your environment, surfaces findings, and stops. Acting on those findings requires a separate workflow — manual remediation, change control, verification, and documentation. The gap between “finding” and “closed” is where compliance debt accumulates.

We built Stratoscope to close that loop. The platform continuously discovers your environment, assesses it against the Azure Well-Architected Framework, proposes specific remediations, executes them with your explicit approval, watches for regression, and verifies the outcome. Every step is logged. The loop actually closes.

Why AI, why now

The Azure Resource Manager API is comprehensive and queryable. Azure Advisor, Defender for Cloud, and Cost Management produce rich structured data. What's been missing is a layer that can reason over all of it together — correlating a permission change with a cost anomaly, or recognizing that a recurring misconfiguration pattern across resource groups is systemic, not incidental.

Modern AI makes that layer possible. Not as a replacement for human judgment — governance decisions need human approval — but as an amplifier that lets a small team manage Azure environments that would otherwise require a much larger staff.

Designed for enterprise from day one

Stratoscope is built for teams that operate in regulated environments, have change control requirements, and need clear separation of duties. That's why human-in-the-loop approval is an architectural invariant, not a configuration option. Why the platform uses least-privilege service principals and explicit delegation for elevated operations. Why every action produces a complete audit trail.

We're not building a tool for developers who want to click “fix all”. We're building a platform for Azure administrators who need governance that meets their organization's standards for control, auditability, and accountability.