README.md

---
title: Llewellyn Systems Inc
emoji: ⚜️
colorFrom: yellow
colorTo: red
sdk: static
pinned: false
---

<h1 align="center">⚜️ Llewellyn Systems Inc</h1>

<p align="center">
  <strong>The Operating System for Decision &amp; Enterprise.</strong><br/>
  Open datasets, open agent primitives, and open infrastructure for the agentic enterprise.
</p>

<p align="center">
  <a href="https://www.llewellynsystems.com">llewellynsystems.com</a>
  &nbsp;·&nbsp;
  <a href="https://www.llewellynsystems.com/.well-known/mcp.json">MCP discovery</a>
  &nbsp;·&nbsp;
  <a href="https://www.llewellynsystems.com/.well-known/agents.json">agents.json</a>
  &nbsp;·&nbsp;
  <a href="https://www.llewellynsystems.com/.well-known/a2a.json">A2A protocol</a>
</p>

---

## What we publish here

We use Hugging Face to ship **the open layer** of our stack — the artifacts an enterprise AI ecosystem can actually be built on.

- **Datasets** — labeled enterprise operations data, agent-routing corpora, MCP intent traces.
- **Models** — small, focused classifiers and routers for orchestration (rolling out).
- **Spaces** — interactive demos of ODE agents, governance probes, and benchmarks (rolling out).

If you train on our data, route through our schema, or extend our agent layer — credit us, ship the work, and tell us about it.

---

## ⭐ Featured: ODE Enterprise Use Case Dataset

A first-of-its-kind labeled corpus of **15,000 enterprise use cases** spanning the full operational surface area of a modern company — procurement, finance, ITSM, supply chain, AI mesh, governance, robotics, and more.

| | |
|---|---|
| **Rows** | 15,000 |
| **Modules** | 31 (Procurement → AI Policy → Robotics → MDM) |
| **Submodules** | 215 |
| **Verticals** | 8 (Manufacturing, Healthcare, FinServ, Public Sector, Retail, SaaS, Logistics, Creator) |
| **Channels** | 5 (Web, Mobile, API, Voice, CLI) |
| **Personas** | 12 |
| **License** | CC-BY-4.0 (attribution required) |

**Built for:** training AI orchestrators, intent classification across multi-agent systems, MCP server routing, business process mining, and benchmarking how well LLMs actually understand enterprise operations.

→ **[huggingface.co/datasets/LlewellynSystems/ode-enterprise-use-cases](https://huggingface.co/datasets/LlewellynSystems/ode-enterprise-use-cases)**

```python
from datasets import load_dataset

ds = load_dataset(
    "LlewellynSystems/ode-enterprise-use-cases",
    data_files="use_cases_universal.csv",
)

# Agent routing: map free-text intent → (module, submodule)
for row in ds["train"].select(range(3)):
    print(f"{row['title']}  →  {row['module']} / {row['submodule']}")
```

---

## What we&apos;re building

Llewellyn Systems builds **ODE** — an enterprise operating system designed for an agentic workforce, not a human-only one.

- **19 production MCP servers** powering domain agents (finance, procurement, security, ops, AI policy, data lineage).
- **55 AI agent skills** orchestrated under a 5-layer governance framework with constitutional AI guardrails.
- **Open well-known endpoints** — every ODE deployment publishes `mcp.json`, `agents.json`, and `a2a.json` so other agents can discover and negotiate with it.

The artifacts we publish here are the slices of that stack that work better in the open: schemas, datasets, routing models, and reference agents.

---

## Citation

If our work shows up in your model, paper, product, or service — cite us.

```bibtex
@misc{llewellyn_systems_hf_2026,
  title  = {Llewellyn Systems on Hugging Face: Open Datasets and Agent Primitives for the Enterprise},
  author = {Llewellyn Systems Inc},
  year   = {2026},
  url    = {https://huggingface.co/LlewellynSystems}
}
```

---

## Get in touch

- **Website** — [llewellynsystems.com](https://www.llewellynsystems.com)
- **Engineering / partnerships** — `llewellyn@llewellynsystems.com`
- **MCP &amp; A2A integrations** — start with the `.well-known/` endpoints linked above

<p align="center"><em>Open primitives. Sovereign infrastructure. Built to be built upon.</em></p>