README.md

metadata

license: cc-by-4.0
task_categories:
  - text-classification
  - question-answering
language:
  - en
tags:
  - enterprise
  - agent-routing
  - orchestration
  - multi-agent
  - function-calling
  - mcp
  - model-context-protocol
  - business-process
  - erp
  - procurement
  - supply-chain
  - decision-intelligence
  - intent-classification
size_categories:
  - 10K<n<100K

ODE Enterprise Use Case Dataset

15,000 labeled enterprise use cases spanning 31 modules, 215 submodules, 8 industry verticals, 5 channels, and 12 business personas.

Published by Llewellyn Systems Inc — builders of ODE, the Operating System for Decision & Enterprise.

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Attribution Required

This dataset is licensed under CC-BY-4.0. You are free to use, share, and adapt this dataset for any purpose — including commercial — as long as you give appropriate credit.

How to Cite

@dataset{ode_enterprise_use_cases_2026,
  title={ODE Enterprise Use Case Dataset},
  author={Llewellyn Systems Inc},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/LlewellynSystems/ode-enterprise-use-cases},
  note={15,000 labeled enterprise use cases across 31 modules and 8 industry verticals}
}

Or in plain text:

ODE Enterprise Use Case Dataset by Llewellyn Systems Inc (2026). Available at https://huggingface.co/datasets/LlewellynSystems/ode-enterprise-use-cases. Licensed under CC-BY-4.0.

If you use this dataset in a model, paper, product, or service — cite Llewellyn Systems Inc.

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Dataset Description

This dataset captures the full breadth of enterprise operations at the task level — from procurement requisitions to AI agent orchestration, from warehouse management to financial close.

Every row represents a real enterprise use case with structured labels for module, function, industry, interaction channel, user persona, development priority, and success metric.

Why This Dataset Exists

Every AI company says "enterprise-ready" but nobody publishes what enterprise actually looks like at the task level. This dataset changes that.

Use cases include:

• Training AI orchestrators to route requests to the correct specialist agent
• Intent classification for multi-agent enterprise systems
• Business process mining and coverage analysis
• Benchmarking LLM understanding of enterprise operations
• MCP (Model Context Protocol) server routing decisions

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Dataset Structure

Fields

Column	Type	Description	Example
id	string	Unique use case ID	UC-00001
title	string	Human-readable use case description	"Create Requisitions in Procurement via Web for Buyer (Manufacturing)"
module	string	Enterprise module (31 unique)	Procurement
submodule	string	Function within module (215 unique)	Requisitions
vertical	string	Industry vertical (8 unique)	Manufacturing
channel	string	Interaction channel (5 unique)	Web
persona	string	User role (12 unique)	Buyer
status	string	Development maturity	GA, In Dev, Planned
complexity	string	Priority tier	P0, P1, P2, P3
kpi_metric	string	Success metric	Touchless Rate

Modules (31)

Procurement, Contracts, Finance, Payments, ERP, SupplyChain, Inventory, WMS, MRP, MES, Quality, ITSM, ITAM, FMS, Workforce, Academy, AI Mesh, AI Memory, AI Lab, AI Policy, Data Lake, Data Lineage, Marketplace, Robotics, Analytics, Exchange, Governance, Integrations, MDM, Predicts, Support

Industry Verticals (8)

Manufacturing, Healthcare, Financial Services, Public Sector, Retail, SaaS, Logistics, Creator

Channels (5)

Web, Mobile, API, Voice, CLI

Personas (12)

Buyer, Approver, Auditor, Engineer, Executive, Finance Manager, IT Admin, Operations, Support Agent, Vendor, System, AP Clerk

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Quick Start

from datasets import load_dataset

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

# Agent routing: map user intent to target module
for row in dataset["train"].select(range(5)):
    print(f"Intent: {row['title']}")
    print(f"Route to: {row['module']} > {row['submodule']}")
    print(f"KPI: {row['kpi_metric']}")
    print()

# Filter by industry
healthcare = dataset["train"].filter(lambda x: x["vertical"] == "Healthcare")
print(f"Healthcare use cases: {len(healthcare)}")

# Filter by module
procurement = dataset["train"].filter(lambda x: x["module"] == "Procurement")
print(f"Procurement use cases: {len(procurement)}")

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Files

File	Size	Format
use_cases_universal.csv	1.8 MB	CSV (15,000 rows x 10 columns)
use_cases_universal.json	4.5 MB	JSON array

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About Llewellyn Systems Inc

Llewellyn Systems Inc builds ODE — the Operating System for Decision & Enterprise.

• 19 production MCP servers for AI agent orchestration
• 55 AI agent skills across sales, finance, compliance, security, and operations
• 5-layer governance framework for autonomous enterprise AI
• Multi-agent orchestration with constitutional AI guardrails

Website: llewellynsystems.com MCP Discovery: llewellynsystems.com/.well-known/mcp.json Agent Directory: llewellynsystems.com/.well-known/agents.json A2A Protocol: llewellynsystems.com/.well-known/a2a.json

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License

CC-BY-4.0 — Creative Commons Attribution 4.0 International

You may use this dataset for any purpose. You MUST give credit to Llewellyn Systems Inc.