Context Layer Platform for Regulated Firms Curate your firm’s context.
Deploy reliable agents.
Do more with fewer tokens.

A context layer platform gives AI agents a working model of the firm they operate in. It connects the firm’s data and documents, the definitions and relationships that give them meaning, the tools an agent may call, and the rules for who can reach what, and it records what every agent run did and which sources it drew on. Port of Context is a context layer platform for regulated industries. It runs in your cloud, on-prem, or air-gapped, and works with any model.

A semantic layer defines business terms and metrics over a firm’s data so that every report computes them the same way. A context layer keeps those definitions and adds how the firm’s people, systems, documents, and decisions relate to one another, along with a running record of what each agent did and the sources it used. An AI agent needs that fuller picture before it can act on the firm’s data.

A self-hosted AI agent platform lets you deploy and run AI agents inside your own infrastructure instead of a vendor’s cloud. Port of Context runs in your cloud, on-prem, or air-gapped environment, so your data and your agents’ tool calls stay in your environment, and you control what leaves.

Yes. The same agent runs on Claude, GPT, Gemini, or open-weight models, and you can switch between them without a rewrite. The model layer is abstracted, so a provider change, outage, or price move never forces you to rebuild your agents.

Yes. Port of Context is built to run entirely inside your own infrastructure, including on-prem and fully air-gapped networks. With local or open-weight models, an agent can run end to end with no external network calls. Nothing leaves the environment. This is why teams in finance, insurance, legal, healthcare, and government deploy it on their own hardware, where sensitive data stays in their environment and you control what leaves.

Every tool call runs in an isolated environment by default, rather than reaching directly into your systems. Nothing touches your data or services unless you allow it, which contains the agent and removes a common path for data leaks.

Every tool call, retrieval, and reasoning step is captured live. You can replay any session, compare runs side by side, and trace each output back to the source it came from, along with what the run did, why, and what it cost. Observability is built on AVP, our open agent-observability spec.

Code mode is how Port of Context runs an agent’s tool calls: as code, with the heavy data handled outside the model’s context. The result is faster, cheaper runs that stay reliable even when an agent uses many tools.

Those tools run agents or workflows in a hosted environment you don’t control. Port of Context is self-hostable, model-agnostic, and isolated by default, with full observability of every run. It is built for teams that need agents in production on their own infrastructure, not for vendor-hosted automation.

No. Operators describe an agent and deploy it without standing up a stack. Engineers can still open every layer when they want to: model choice, tool whitelists, isolation policy, and the eval harness. Same platform, progressive disclosure.

Yes. The core is open source under the MIT license, so you can read it, run it, and self-host it without a commercial agreement. Engineers can inspect every layer, from model choice to isolation policy to the eval harness.

Yes. Any existing MCP server works unchanged. Port of Context aggregates your MCP servers behind one endpoint and exposes their tools to the agent as code, and your auth secrets never reach the model.

Only when you allow it. Port of Context runs inside your boundary and is not in your data path. An agent that calls a hosted model sends that request to the provider, and you control which calls are allowed, to which providers, and for which data. Run local or open-weight models air-gapped and nothing leaves your network.