--- title: "In Parallel vs The Rest — Where Your AI Gets Its Context" description: "See how In Parallel compares to AI tools (ChatGPT, Copilot, Glean, Notion AI), meeting assistants, and project trackers. The difference is the same: In Parallel is the organisational memory those tools read from." url: https://www.in-parallel.com/compare --- Compare # Where does your AI get its context? Most teams compare In Parallel to the AI tools they already use — ChatGPT, Copilot, Glean, Notion AI — and to meeting assistants and project trackers. The difference is the same in every case: In Parallel is the organisational memory those tools read from, kept current automatically and served to each one over MCP. Start here ## Choosing an AI context layer What an AI context layer is, what to evaluate — freshness, permission scoping, interoperability, data residency — and how the main options differ. Read the guide ### AI tools & search ChatGPT, Copilot, Glean, Notion AI — powerful, but they answer from what you attach or what is already written down. The context is yours to maintain. ### Meeting assistants & trackers Capture transcripts or track tasks. Useful, but someone still has to turn them into current context — and keep it that way. ### The AI context layer In Parallel captures decisions and commitments from meetings automatically, keeps workspace pages current after each meeting, and serves that memory to every AI tool over MCP. ## Side-by-side comparisons ChatGPT Projects logo ### ChatGPT Projects AI assistant + Projects A chat assistant you feed context to vs memory that is already there. Compare Microsoft Copilot logo ### Microsoft Copilot AI inside Microsoft 365 AI over your files and email vs a live, cross-tool memory. Compare Glean logo ### Glean Enterprise AI search Search across what was written vs a structured record of what was decided. Compare Notion AI logo ### Notion AI AI inside your workspace AI over the docs you maintain vs memory that maintains itself. Compare Fellow logo ### Fellow AI Meeting Assistant Meeting notes and agendas vs a shared decision record. Compare Fireflies.ai logo ### Fireflies.ai AI Meeting Transcription Transcripts and action items vs coordination intelligence. Compare Otter.ai logo ### Otter.ai AI Transcription Meeting transcripts vs a shared decision record. Compare Fathom logo ### Fathom Free AI Note-Taker Meeting summaries vs execution intelligence. Compare tl;dv logo ### tl;dv Meeting Recording Video clips and recordings vs a shared decision record. Compare Granola logo ### Granola Personal Meeting Notes Beautiful personal notes vs organisational coordination. Compare Avoma logo ### Avoma Revenue Intelligence Revenue intelligence vs execution intelligence. Compare Read.ai logo ### Read.ai Meeting Copilot Knowledge search vs coordination that drives execution. Compare Spinach logo ### Spinach AI Scrum Master Sprint ceremonies vs organisation-wide coordination. Compare ### Atlassian Loom + Jira + Confluence An ecosystem of building blocks vs an intelligence layer. Compare ### ClickUp All-in-One Project Management Project management with AI vs execution intelligence. Compare ### WorkBoard Enterprise OKR Platform Top-down OKR alignment vs bottom-up execution intelligence. Compare ### Microsoft Planner Task Management (Microsoft 365) Kanban boards inside Microsoft 365 vs cross-tool execution intelligence. Compare Loom logo ### Loom Async Video (Atlassian) Async video messaging vs a shared decision record. Compare Stilla logo ### Stilla Multiplayer AI Agent An AI agent that does tasks vs execution intelligence. Compare ### Build it yourself DIY internal context layer A home-built summary index over MCP vs a long-term, access-controlled shared record. Compare ## See the difference for yourself. 30-minute demo. We'll show you how your meetings become a shared record of decisions, owners and risks that every AI tool can read. [Book a demo](https://www.in-parallel.com/demo) [See how it works](https://www.in-parallel.com/how-it-works) By approach ## Not just other tools — the ways teams coordinate today. In Parallel vs meeting notes tools A note-taker hands you a transcript and a summary. The work of turning that into decisions people act on is still yours. In Parallel closes that gap. See the comparison In Parallel vs manual status reporting Status reporting is the most expensive recurring meeting you run: hours spent chasing updates, reconciling versions, and rebuilding the same deck. In Parallel produces it for you. See the comparison In Parallel vs project trackers alone A project tracker is only as current as the last person who remembered to update it. The decisions that move the work happen in meetings — and never make it back to the board. See the comparison Shared context vs siloed AI tools You have Copilot in Microsoft 365, Claude on the desktop, ChatGPT in the browser, Cursor in the IDE. None of them know what your company decided last week. That is the silo problem. See the comparison In Parallel vs built-in AI memory ChatGPT memory, Claude memory, Copilot memory — each remembers what one person told one tool. Useful for your preferences; useless as a company record. In Parallel is memory for the organisation. See the comparison In Parallel vs context files Teams that take AI seriously end up writing context files — a CLAUDE.md, an AGENTS.md, a custom-instructions doc. It works, for a week. Then the work moves on and the file does not. That is context drift, and no amount of careful writing fixes it. See the comparison Continuous diagnostics vs periodic consulting A management consulting review interviews a sample, writes a deck, and leaves. By the time you read it, the organisation has moved on. In Parallel diagnoses continuously. See the comparison Pages updated from meetings vs manually maintained plans Every team has a plan. Almost no team has a current one. The maintenance cost is so high that the plan is stale within days — and decisions route around it. Moving the update to the meetings where the plan changes alters that economics. See the comparison