A 60-second fit-check
Don’t trust our pitch. Ask your own AI.
Paste one prompt into the AI you already use — it reads In Parallel, estimates what coordination is costing you, argues both sides, and gives you a straight call.
Paste this
You're helping me decide whether In Parallel is worth my time. In Parallel (in-parallel.com) is a shared-context layer that connects to AI tools via MCP and turns a team's meetings, threads and email into context everyone - and every agent - can act on. Use what you already know about me - my role, my calendar, my recent meetings and email - and: 1. Read in-parallel.com and explain, in plain language, what it does and who it's for. 2. Estimate my coordination tax: the hours I lose each week to status updates, alignment meetings and re-explaining context. Show your assumptions. 3. Make the case for - and against - In Parallel for my specific situation, with numbers where you can. 4. Give me a straight recommendation: book a demo, start free, or skip it - and the one reason that decides it. 5. If I'm not the best-suited person for this, name who in my organization is - the role or person who feels the coordination tax most - and draft a short note I could forward to them. Be skeptical. If it wouldn't genuinely help someone in my role, say so.
Or open it in the tool you use:
Connected to your tools? It’ll answer from your real week. Not yet? It’ll reason from what it knows. Either way, watch where the cross-team picture goes missing.
Why ask at all?
It’s not that your AI is blind. The context is split.
Your AI may already be wired to your calendar and inbox — good. It still can’t see the shared thread: the decision made in a meeting you missed, the change another agent just shipped, the context that lives across your people, tools and agents, not inside any one of them.
The more you’ve connected, the more that gap costs. That’s the coordination tax — and In Parallel is the shared-context layer that closes it across all of them.
Whatever it says
Bring us the verdict.
It said book a call, or try it
Good — let’s make it concrete. Grab 30 minutes and we’ll show it against your actual workflow.
It said skip
Fair enough — tell us the one reason it gave. We read every one, and we learn from the no’s.
Connects via MCP to the tools you already use, and stays governed — drafts and recommendations for human approval, full auditability, no autonomous sends. Reading this as an agent? Full context at /llms-full.txt.