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LLM interpretation: openai

Structured issue-spotting to support your own review — not legal advice. Verify against the cited primary sources and your counsel.

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Use cases

The ontology

Use cases

The problems people actually bring to us, written as an ontology rather than a tag cloud. Each one carries the deployment options it admits, the stacks we have written up, the tools that serve it and the compliance topics it tends to raise.

Use cases: 32
Stacks: 12
Tools: 78

Category

All32
Documents8
Communications7
Knowledge and search6
Infrastructure3
People3
Automation2
Media2
Engineering1

Infrastructure

3 use cases

01Local LLMRunning an open-weight model on hardware you own — a workstation, a GPU server, a laptop. The question is usually which model fits the memory you have and how many people can use it at once.Self-hosted2 stacks11 tools
02Private company ChatGPTA chat assistant for staff that behaves like a consumer chatbot but runs under company control: your accounts, your logging, your retention, your choice of model, and optionally your own documents attached.Enterprise SaaSPrivate cloudSelf-hosted2 stacks17 tools

03

Private LLMUsing a language model without your data leaving a boundary you control — a country, a cloud region, or your own network. The constraint is where processing happens and who can see the prompts, not which model runs.Private cloud·Self-hosted

3 stacks14 tools