Own your context — In Parallel

Own your context

Rent the model. Own the context.

The AI your company uses will change several times before this year’s plans are finished. Most teams choosing AI tools now are choosing for that: keep the options open, avoid the lock-in. The part worth protecting is not the model. It is what your company knows — and that should not have to start over every time the tools do.

The lock-in moved

Software was easy to leave. Memory is not.

Every AI suite now builds a memory of your company: what was decided, who owns what, how the work is going. It is genuinely useful. It also lives inside that vendor’s product, readable by that vendor’s assistant, priced by that vendor’s seats.

Switching a model takes an afternoon. Rebuilding two years of context takes two years. That asymmetry is the new lock-in, and it is easier to avoid at the start than to undo later: keep the context in a layer you own, and let every tool read from it.

Replaceable

  • The model behind the answer
  • The assistant your people prefer this year
  • The agent platform running your workflows
  • The suite your company buys its seats from

Yours to keep

  • What was decided, and why
  • Who committed to what, by when
  • The risks and open questions still in play
  • What each team knows that the others need

Four tests

What “own” has to mean. And how we answer.

“Your data belongs to you” is in every vendor’s terms. Ownership you can use comes down to four questions. Ask them of anyone, us included.

01 · Rights

Who owns what the system learns from our work?

You do. In Parallel never trains models on your data, and your context is not pooled with any other company’s. Each company gets its own database schema.

02 · Portability

Can we take all of it with us, in a format another tool can read?

Yes. Our terms say the title to your data is yours, and on request we help move it to you or to another provider you name. The record itself is plain language — decisions, risks, questions, commitments, pages — not embeddings only we can read, and every part of it can be read out over MCP.

03 · Location

Where does it live, and who else processes it?

In the EU. Hosting and model inference run in Frankfurt, on Amazon Web Services and Amazon Bedrock, and every subprocessor we use processes data in the EU.

04 · Control

Do we decide which tools and agents read what?

Yes. Each share is scoped to chosen workspaces and a level of detail. Revoke it and the next request returns nothing — for people and for AI. Owners see what was read through their shares.

Switch without starting over

One context. Whichever AI you pick next.

In Parallel serves your context over MCP, the open protocol that AI assistants and agents already speak. The same decisions, with the same sources and the same permissions, reach whichever tool a person or a team prefers. Change the tool and the context is already there.

AI agents need no seat. Adding an agent, or replacing one, does not change the bill — so trying the next tool is a decision about the tool, not about the contract.

  • Claude reads the same context
  • ChatGPT reads the same context
  • Microsoft Copilot reads the same context
  • Cursor reads the same context
  • Your own agents reads the same context

Before you sign

Five questions for any AI vendor.

  1. 1 If we cancel tomorrow, what do we get back, and in what format?
  2. 2 If we switch AI models next year, does the context come with us or start from zero?
  3. 3 Can a tool from a different vendor read our context, or only yours?
  4. 4 Is our data used to train anything, including “improving the service”?
  5. 5 Do we pay per seat for every agent that reads it?

Honestly

What this does not mean.

In Parallel is a hosted service in the EU. Owning your context does not mean running it yourself; it means the rights, the location and the access stay yours, and the way out is written down. There is no one-click export of everything yet — today it is a request we act on, and a protocol any tool can read. It does not make every AI tool equally good; it means you can choose the one that is, and change your mind. And a layer you own is still a vendor you rely on. That is why we would rather you could leave easily: it is a better reason to stay.

Keep the options. Keep what you know.

Connect it to the AI tool you use today, and the one you might use next. Every plan is billed monthly, with no commitment.

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