Compare to
Build it yourself
You can put every internal doc in Git and point a model at it. That gives the model a library -- a pile it re-reads from scratch on every question, for every person.
In Parallel gives it a memory: preprocessed, access-governed, and always current -- so it answers from what is true now, only to the people allowed to know it.
What are the key differences?
A pile of docs, or a context layer.
From re-reading the corpus to a preprocessed model
Dump your docs in a repo and the current state of the business is never actually computed -- it is re-inferred on every prompt, by every person, every time. In Parallel preprocesses that raw material once into a maintained model of decisions, goals, and status. The expensive synthesis happens a single time and is kept live, so each query reads a small, clean, current slice instead of wading through the whole noisy corpus. You stop paying to re-discover the same answer a hundred times a day.
From repo access to object-level governance
A Git repo or shared drive is binary: you have access or you do not. Point a model at that flat pile and it can surface anything in it to anyone who can ask -- sales stumbling into comp discussions, a contractor's agent reading M&A notes. In Parallel governs context at the object level, enforcing who sees which decision, goal, or document before the model ever sees the data. The same question returns different answers depending on who is asking.
From docs that rot to context that stays current
A DIY pile is only as good as the discipline of whoever maintains it. Nobody keeps the "current status" doc current, so the model answers confidently from stale information. In Parallel stays current as a byproduct of work -- the meetings and decisions feed the model continuously. Nobody maintains the context. The meetings do.
From no trail to full provenance
A pile of docs has no record of who saw what, when, or which source an answer came from. Regulated industries -- pharma, finance, government -- need provenance and access logs as table stakes. In Parallel traces every answer back to its source decision or meeting, logs access, and keeps data in the EU. See the Security page for how access control and provenance are enforced.
From a project to a product
Building an internal context layer is not a one-time build -- it is running a context operation forever: extraction pipelines, access rules, freshness, audit. The honest question is not build-vs-buy a document store; it is whether you want to operate a context system indefinitely, or use one that is maintained for you.
When to choose Build it yourself
- Your context is almost entirely code, and a coding assistant reading the repo is the whole job
- One small team shares everything — there is no who-sees-what to enforce
- You have engineers to build and run extraction, access control, and freshness pipelines indefinitely
- Regulatory audit and provenance are not requirements you will be asked to prove
- You are comfortable that every query re-reads the full corpus and pays for it
When to choose In Parallel
- Context spans sales, CS, operations, and leadership — not just the codebase
- Different people should get different answers to the same question
- You are in a regulated industry that needs provenance and access logs
- You would rather context stay current as a byproduct of work than assign someone to maintain it
- You want to spend tokens once on synthesis, not on every person re-deriving the state
Frequently asked questions
Doesn't putting our docs in Git already make the model's answers better?
Yes -- and that is the floor, not the ceiling. Giving a coding assistant the repo to read genuinely lifts its answers. But that is a single team, working over code, where everyone is allowed to see everything. It does not generalise to cross-functional coordination, where the hard parts are governing who sees what, staying current without manual upkeep, and not paying full re-analysis on every query. The better your internal setup gets, the more sharply you feel the three things a flat pile structurally cannot do.
What exactly does 'preprocessing' the context do?
It distils a sprawling, noisy corpus -- drafts, dead threads, superseded decisions -- down to the live state of the business, once, and keeps it current. Without it, every person's every question re-feeds the whole corpus through the model and pays tokens for it to wade through everything to reach one current fact. With it, each query reads a small, clean, current context. The cost stops scaling with docs × people × queries.
Can't we just add access control to our own setup?
You can -- but the moment you do, you have started building a context layer, and the cost is running it forever. Object-level governance (this exec sees board context, this contractor sees only their workstream) is the genuinely hard part, and it has to be enforced before the model sees the data, not bolted onto a folder afterwards.
Is In Parallel a replacement for our wiki or Git?
No. It sits on top of where your work already happens. It is not another place to store documents -- it is the layer that keeps a model in sync with reality, governs who can see what, and answers from the current state. Your repos and docs stay where they are.
How does this handle regulated data?
Access is enforced at the object level before the model ever reads the data, every answer carries provenance back to its source, access is logged, and data stays in the EU. A flat pile of docs gives you none of that -- which is exactly what an auditor will ask for.
Priced per user. Volume and duration earn the discount.
Cheaper than the coordination tax. 8 hours a week back per manager.
Plans + everything
€69
per user / month
- Notes free for 20 days — unlimited workspaces.
- Volume + duration discounts apply — down to €39 at 10,000 paid users on multi-year.
- Passive users (no usage) not invoiced.
- Enterprise terms available for over 100 users — SSO, SCIM, custom retention.
Stop operating a context layer. Start using one.
See how In Parallel turns your meetings and decisions into a governed, always-current model -- without a pipeline to maintain or a corpus to re-read. 30-minute demo.