An LLM gateway is a management layer that sits between the people and applications inside an organization and the language models outside it. It decides which model a request goes to, records who asked for what, and caps usage and cost. Teams adopt one to bring scattered model calls under a single point of control, usually in response to shadow AI.
What a gateway manages is the path of a request: where it goes, who approved it, how much it cost. Basic filtering, masking personal data detected in a prompt, happens at this layer too.
What it does not answer is how to use operational data that cannot leave in its original form. Filtering masks the values it detects, so the request passes, but the table structure and the relationships between business identifiers such as circuit or asset IDs go with them. An answer built on that has to be reassembled by hand before anyone can use it. Controlling access and turning unsendable data into something a model can work on, then putting the real values back, sit at different layers. That second layer is a different category: a context-preserving data layer for AI, which is where LLM Capsule sits.