Segue: Share AI context between models using short handles

What it does

Segue is a tool for storing and sharing AI context through short text handles. Users create "briefs"—structured documents that capture decisions, evidence, and next steps from work with multiple AI models. A brief can be loaded into different AI tools (Claude, ChatGPT, Cursor, Codex) by using its short handle as a reference. The tool tracks which information needs rechecking, maintains a dated history of changes, and allows team members to propose updates that maintainers review before applying.

Who it is for

Segue targets teams working across multiple AI tools who need to maintain a single source of truth. The product is built for collaborative work where context changes—datasets get updated, experiments run, conclusions need revalidation. It appeals to research teams, software development groups, and organizations that use AI assistants but currently lose context when switching between tools or team members.

Pricing

Personal briefs are free with no credit card required. Workspace collaboration requires paid seats for members who can create and propose changes. The site does not show specific pricing amounts.

How it stands out

Most AI tools treat conversations as isolated sessions that disappear or stay trapped in one platform. Segue bridges this gap by making context portable and reusable across different AI models through a handle system. The freshness-checking feature—flagging answers when dependencies change or time windows pass—addresses a real problem: knowing which conclusions still hold as underlying conditions shift. The review workflow for team updates adds accountability and attribution to brief changes, which matters in collaborative research or development work where decisions need to be traceable.

What a founder should check

First, verify switching costs. Users already have workflows built around ChatGPT, Claude, or Cursor. Why would they adopt a separate tool to manage context instead of using built-in conversation history or note-taking they already own? Second, test whether the handle-based loading actually works smoothly across different AI APIs and whether there are latency or formatting issues when briefs move between tools. Third, examine whether teams actually maintain these briefs over time or whether they become stale—the product assumes users will return and update, but many collaborative tools fail when maintenance burden exceeds perceived value.

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