Practical Answers to Hard Data Challenges

SysML Repo Modeler is now open source and free to use. It turns one GitHub repository—or many—into an explorable SysML v2 system model. Get the code on GitHub.

It gives us a coherent way to visualize our own repositories, services, APIs, and layers of institutional knowledge in an exploratory systems view. And now it’s open source for anyone to freely use.

Explore a Live Model

Want to see the result before getting into the details? Start with the live Supabase Platform model, which maps five repositories and four languages as one system. You can also explore the OpenClaw model, the n8n model and the Ollama model. All four sit side by side in the SysML Repo Modeler gallery, with the repository, part and connection counts for each.

Open the live Supabase Platform model — click the image to search, filter, and explore the system.

The Problem: Architecture Lives in Too Many Places

The reality is that modern systems are scattered across multiple repositories, services, APIs, and layers of institutional knowledge. The challenge is not that teams lack documentation; it is that documentation struggles to keep up with what the code does.

Traditional Model-Based Systems Engineering (MBSE) documentation can be useful, but it is often manually maintained across disparate software platforms that require significant user knowledge and training, with limited ability to transfer data between tools. As a system evolves, the diagram becomes a snapshot of what people thought a system looked like but not necessarily what exists now.

During this live webinar we learned how appropriately designed agentic data engineering changes the equation. We also discussed autonomous AI agents can design, build, validate and govern production-grade data pipelines while keeping every decision auditable, every transformation explainable and every cost dramatically lower than AI or manual approaches.

What was covered during this webinar:

• Why chatbot-style AI fails at scale for mission-critical data work

• How Belvedere is a force multiplier for your existing data engineers and IT investments

• Real-world metrics: 5-10x effort and time reduction in pipeline development

• How to maintain IC-compliant auditability, human oversight and policy enforcement while accelerating delivery

• Live demonstration of an agent building and deploying a trusted intelligence data pipeline from raw sources to governed outputs in minutes

FOR IMMEDIATE RELEASE

Vienna, VA — July 27, 2026 — Clear Fracture LLC, developer of Belvedere™, the Agentic Data Manager, today announced that it has achieved “Awardable” status through the Chief Digital and Artificial Intelligence Office’s (CDAO) Tradewinds Solutions Marketplace.

The Tradewinds Solutions Marketplace is the premier offering of Tradewinds, the Department of War’s (DoW’s) suite of tools and services designed to accelerate the procurement and adoption of Artificial Intelligence (AI)/Machine Learning (ML), data, and analytics capabilities.

Belvedere puts AI agents to work as data engineers. Analysts and mission owners describe what they need in plain language; Belvedere’s agents discover the source data, design the transformations, and compile them into governed, production-ready pipelines that run on the organization’s existing infrastructure. The agents build the pipeline; they are not the pipeline. Every pipeline they produce is transparent, auditable, and repeatable, and it runs as ordinary code, keeping operations cost-efficient at mission scale.

“Mission teams lose too much time wiring data together by hand, and the systems that result are hard to trust and hard to maintain,” said Brian Frutchey, Chief Technology Officer of Clear Fracture. “Belvedere’s agents do that engineering work in the open. Every pipeline they build can be inspected, audited, and run again tomorrow. Awardable status through Tradewinds gives DoW customers a direct path to put that capability on contract.”