Conduit

The problem

AI tools become far more useful when they understand the context you work in. In practice, that context is scattered across documents, notes, folders, and internal references. The usual workaround is to keep uploading files and pasting snippets, which becomes messy and unreliable as the pile grows.

At the same time, connecting AI tools to real systems is still too manual. You install a connector, chase dependencies, edit configs, and repeat the process for each client. This is the kind of undifferentiated setup work that drains momentum.

What Conduit changes

Conduit is designed to make your AI tools feel more personal and more capable without increasing your risk. It helps you manage MCP servers as a guided, secure experience, and it supports a private knowledge base that turns your documents into usable personal context.

Instead of sending every file to every AI tool, Conduit builds a knowledge layer from what you choose to add. That layer is designed to return precise, minimal context so your AI tool gets what it needs without being flooded by irrelevant material.

What you can do

• Add files and folders to build a private knowledge base that stays under your control.
• Bring personal context into AI tools through a first-party MCP server that Conduit runs and maintains.
• Install and configure MCP servers with guidance, safety checks, and sensible defaults.
• Configure once and reuse across multiple clients without repeating setup steps.

Safety by default

Conduit treats third-party servers as untrusted by default. It runs them in isolated containers, stores secrets locally in your OS keychain, and requires explicit permission before granting access to sensitive resources. This approach is designed to reduce the risk of running connector code you did not write yourself.

FAQ