In Parallel MCP - Your context, available to every agent.
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You've explained your company to ChatGPT. Then to Claude. Then to Copilot.
Every time you open a new chat, you start from scratch. Paste the notes. Upload the document. Copy in the email thread. Summarize what your team decided two weeks ago — to a tool that could've just known it all along.
In Parallel's MCP server ends that. Connect it once, and whichever AI you open already knows your meetings, decisions, and context. Just ask the question. Less prose. More truth.
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every "single source of truth for the org" tool I've seen eventually turns into the thing everyone stops updating, then it's worse than no source at all because people trust a stale answer. how does In Parallel keep goals/ownership actually current without someone manually maintaining it, is it pulling straight from where the work happens (tickets, docs, calendars) or does it still rely on people logging updates
In Parallel
@omri_ben_shoham1 yes. that's the problem we're solving! I agree, no new interface or tool will help.
Question about the write path. Everything in the thread so far is about agents reading the shared picture, but keeping context current as work happens implies agents and integrations are also writing observations back. Once several agents can write, the trusted operational picture has a provenance problem: how do you label and weight agent-generated context against human decisions, so one confidently wrong summary does not become the context every other agent inherits? Is there a review gate, a provenance tag per entry, or a decay rule for unconfirmed observations? This is the part I would pressure-test before wiring it into every tool, and I could not find it discussed yet.
In Parallel
@cipherslice Yeah, great point. There's a gate for sure, we call it the change log. The balance however is how not to make it annoying to deal with the increasing amount of approvals. We set some rules for auto-approvals (on user consent, much like you do things in the ChatGPT/Claude sessions). For the provence: absolute key, we have reference on the origins for the observations at all times.
Decay too exists, although it is context-dependant, and that is something we're hoping to evolve further.
@kristian_luoma The change log as the gate is a tidy answer: one artifact doing double duty as audit trail and approval queue. Auto-approval on consent rules matches how agent sessions handle permissions, so that mental model will feel native to your users. Context-dependent decay is the part I will watch with interest; the builder in me wants the decay policy visible per entry, so an agent can weigh staleness itself instead of trusting a hidden clock. Appreciate the specific answer.
Congratulations on the launch! Solving the context problem for organizations with information moving faster than ever is a big deal! Heck, just figuring out how to manage my own personal context as a solo builder to make the most out of AI has it's challenges.
In Parallel
@derrek_pearson It's true!