Live context for agents
The MCP server exposes shared organizational memory as queryable context, so an assistant can answer from the current plan rather than from stale docs or isolated chat history.
In Parallel is an AI context layer for teams that captures meeting signals and shares plan context with Claude, Copilot, and ChatGPT via MCP.
In Parallel is an AI context layer for teams built around MCP. It captures shared organizational memory from meetings and makes that context available to AI tools such as Claude, Copilot, and ChatGPT so they can answer from the same live business reality.
The product is aimed at teams that need their plans, decisions, owners, and drift signals kept in sync across tools. Instead of relying on stale docs or separate status updates, it exposes workspace-specific context through a dedicated MCP endpoint and keeps that context scoped to the right people and projects.
The MCP server exposes shared organizational memory as queryable context, so an assistant can answer from the current plan rather than from stale docs or isolated chat history.
Each workspace gets its own MCP endpoint and data perimeter, which keeps customer, project, board, or executive context separate.
The product captures meeting signals and uses them to update plans, decisions, owners, and drift indicators without manual re-entry into a second system.
It is designed to connect to MCP-capable assistants, coding agents, and automation platforms across multiple tools, instead of binding the workflow to one vendor.
The site says workspaces have separate permissions and audit logs, and that permissions are granted to people rather than to models.
The setup flow connects calendar, email, and meeting tools, then points an AI connector at the workspace MCP URL.
Product teams can keep roadmaps aligned with what was actually decided in meetings, while drift and missing owners surface before a sprint slips.
Executives can query a workspace-specific context layer for a current view of strategy, execution, and divergence without reconstructing the story from multiple docs.
Finance teams can expose commitments and divergence signals in context so variance is visible when it appears in a meeting, not only in a later report.
Marketing teams can spot when a campaign brief or launch plan has drifted during collaboration, before the work reaches launch.
Internal agents and automations can read the same plan-grounded context through MCP, making status drafts and other workflow actions more consistent.
In Parallel captures meeting signals and exposes the resulting plan state through an MCP server, so assistants like Claude, Copilot, ChatGPT, and other MCP-capable tools can query the same live context.
Yes. The site says setup takes about five minutes: install In Parallel, connect sources such as calendar, email, and meeting tools, then add your workspace MCP URL in the AI tool’s connector settings.
The product is scoped by workspace. Each workspace has its own MCP endpoint, permissions, and audit log, and the site says the AI only sees that workspace’s plan state, decisions, owners, and drift signals.
The pricing page says the product is free for 20 days, with the full product included in the trial. After that, paid plans start at €69 per active user per month, with annual and multi-year discounts available.
The site lists Claude, Copilot, ChatGPT, Cursor, Gemini, Perplexity, Claude Code, Windsurf, GitHub Copilot, Zed, VS Code, n8n, Zapier, Goose, Continue, Cline, and custom SDK-based agents as supported contexts or examples.
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