Shared context layer
Capture organizational memory from meetings and surface it as live context for AI assistants. The product is positioned as a shared context layer rather than a standalone chatbot.
In Parallel
RevendiquerIn Parallel is an AI context layer for teams that captures shared meeting memory and delivers current workspace context to Claude, Copilot, and ChatGPT.
What In Parallel is
In Parallel is an AI context layer for teams. It captures shared organizational memory from meetings and connected work sources, then exposes that context to AI tools through MCP so assistants can respond from current business reality instead of stale prompts or isolated documents.
The product is built around workspaces, which act as permission boundaries for teams, projects, or other contexts. Each workspace gets its own MCP endpoint, so tools like Claude, Copilot, ChatGPT, and other MCP-capable systems can read the right context without leaking information across boundaries.
Core capabilities
MCP access to live plan state
Expose workspace-specific context through MCP so agents can query current decisions, commitments, owners, and drift signals instead of relying on stale documents or chat history.
Workspace-scoped permissions
Use separate workspaces as trust and permission boundaries. Each workspace has its own MCP endpoint, audit log, and access scope.
Cross-tool compatibility
Connect to MCP-capable tools and agent platforms such as Claude, Copilot, ChatGPT, Cursor, and other compatible systems without building a custom integration for each one.
Provenance and governed write-back
Support cited answers and governed write-back. The build page says every answer carries its source and that write-back into the shared record is proposal-and-approval based.
Meeting and work-source capture
Start from captured meetings and connected work tools. The source mentions calendar, email, and meeting tools as inputs used to build shared context.
Practical use cases
Team status grounded in current decisions
A product, engineering, or leadership team can ask an assistant for the current state of projects and get answers grounded in decisions, scope changes, owners, and drift captured from recent meetings.
Role-specific AI alignment
Sales, executive, and engineering users can keep their AI assistants aligned to their role’s current context, so prompts about priorities, trade-offs, blockers, or architecture reflect what was actually decided.
Agent development on shared context
Teams building agents can use In Parallel as the memory backbone for vertical copilots, onboarding assistants, meeting agents, or internal automation without building capture, permissions, and provenance from scratch.
Scoped context for distinct workspaces
Organizations can separate context by workspace for customers, projects, board discussions, or exec sessions so one assistant does not mix confidential or unrelated information into a response.
Product roadmap and commitment tracking
Product teams can keep roadmaps, commitments, and meeting outcomes in sync so updates from standups or planning meetings are reflected in the shared record instead of being lost in the wiki.
Pros and Cons
Pros
- Provides a shared live context layer for multiple AI tools instead of separate integrations per assistant.
- Uses MCP so context can be consumed by agents across different platforms and stacks.
- Keeps access scoped to workspaces, helping avoid accidental cross-talk between teams or projects.
- Adds provenance and cited answers, which can make agent output easier to trust and review.
- Includes a self-serve free trial and does not require a credit card to start.
Cons
- The source does not show a permanently free plan; the free offer is a 20-day trial.
- Workspace-scoped setup means teams need to think about where context boundaries should live before connecting agents.
- The product does not replace project tools; the source explicitly says it will not make a badly run programme well run or replace your project tools.
FAQ
Does In Parallel require a custom integration for each AI tool?
No. The product is designed to plug into MCP-capable tools such as Claude, Copilot, ChatGPT, Cursor, and other compatible agents. The source also notes support for custom connectors and agent platforms, but the exact setup depends on the client you use.
Is there a free trial?
The pricing page says the full product is free for 20 days, with no credit card required. After that, you choose a paid plan.
How is context separated between teams or projects?
The source says workspaces are the unit of trust, and each workspace has its own MCP endpoint and permissions boundary. That keeps context scoped to the right team or project.
Can agents write information back into In Parallel?
The build page describes read access over MCP or the SDK, provenance on each answer, and governed write-back. The source also says write-back is proposal-and-approval rather than autonomous sending.
What problem does In Parallel solve?
The source positions the product as an AI context layer for teams that captures meeting signals, decisions, commitments, owners, and drift, then exposes them to AI tools through MCP.
Quick Facts
- Category
- AI context layer / developer tool
- Platform
- MCP-compatible AI assistants and agent platforms
- Primary users
- Teams using Claude, Copilot, ChatGPT, Cursor, and related tools
- Deployment model
- Workspace-based SaaS with separate MCP endpoints
- Pricing
- Paid product with a 20-day free trial; pricing page lists €69 per active user per month and enterprise terms for larger teams
- Website
- in-parallel.com
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