Compare to
Build it yourself
Plenty of teams have built it: Slack threads and meeting notes summarised by a model, dropped into a vector index, served to Claude over an MCP server. It works, it stays current on its own, and for one small team it may be all you need.
The hard part starts when the record has to last for years, cover more than chat and meetings, and give different people different answers. That is the part In Parallel is built for.
In Parallel isn't another meeting or project tool — it's the AI context layer: it gives the AI you already use your team's decisions, with the source behind every answer. Choosing an AI context layer →
What are the key differences?
A summary index, or a shared record.
Where a home-built pipeline works well
Give credit where it is due. A summary pipeline gets meetings and Slack in without anyone filing anything, it stays current by itself, and Claude can query it the same day you ship it. If one small team shares everything and has an engineer who enjoys running it, it is a good answer. The rest of this page is about what happens as the record grows and more people depend on it.
From summaries that pile up to memory that compounds
Summaries accumulate. After a year the index holds three versions of the pricing decision and no record of which one won, so the model blends them. In Parallel stores what was decided, by whom, and what it replaced. A decision from last March is still findable in 2028, and so is the fact that it was reversed in June. That long-term record is the value, so it should not be the part your pipeline is weakest at.
From one index for everyone to access by level
An MCP server over one index answers anyone who can reach it. Add the board, HR, or a customer project and you need rules: this manager sees status, that contractor sees only their workstream, the leadership team sees the meetings themselves. In Parallel grants access at four depths (status, storylines, observations, meetings and documents), by project, named person or role. Access expires unless renewed, revocation is immediate, and agents re-check their grant on every call.
From a summary of a summary to the source
When the model answers from a summary, you cannot tell what was actually said or who said it. In Parallel traces every answer to the meeting or thread it came from. That is what lets someone act on the answer, and what an auditor will ask for.
From a side project to a product
Every new source, model change, permission rule and retention policy lands on whoever built the pipeline. The question is not whether you can build it. You already did. It is whether running an access-controlled company memory is what you want that engineer doing for the next five years.
When to choose Build it yourself
- One small team shares everything, so there is no who-sees-what to enforce
- A rolling few months of memory is enough, and nobody needs last year’s decisions
- You have an engineer who wants to own the pipeline, permissions included, indefinitely
- Nobody will ask you to prove where an answer came from
When to choose In Parallel
- More than one team, client or level of the organisation draws on the same context
- Different people should get different answers to the same question
- The record needs to last for years and know which decision replaced which
- Answers need a source someone can check
- You would rather your engineers build your product than your memory layer
Frequently asked questions
We already summarise Slack and meetings into an index Claude can read. Why change?
You may not need to yet. A summary index is a sound first step. The reasons teams move are the ones that arrive with growth: the index fills with contradicting summaries, a second team or a client needs to be kept apart, or someone asks where an answer came from. If none of those apply, keep what you have.
Can't we add access control to our own MCP server?
You can. The moment you do, you are designing a permission model: which depth of context, for which projects, for whom, for how long, and what an agent may do with it. It has to be enforced before the model reads anything, and re-checked when access is revoked. That is most of what In Parallel is.
What does In Parallel store instead of summaries?
Structured signals: decisions, action items, risks, dependencies, escalations, opportunities, learnings, progress updates and obstacles, each linked to the meeting or thread it came from. The transcript itself can expire on a schedule you set while the extracted record is kept.
Does it still work with Claude?
Yes. In Parallel serves the same context over MCP to Claude, Copilot, ChatGPT, Gemini and your own agents, so swapping your pipeline out does not change how your team asks questions.
Can we keep our pipeline and use In Parallel for the governed part?
Talk to us. Some teams start with In Parallel for the meetings and projects that need access rules, and leave low-sensitivity channels where they are.
One price per seat. Pick how long memory is kept.
41% of the work week goes to coordination. In Parallel gives it back.
Data retention
30-day memory
€29
per user / month
Memory kept for 30 days.
1-year memory
€69
per user / month
Memory kept for 1 year.
10-year memory
€254
per user / month
Memory kept for 10 years.
Enterprise
Custom
A platform fee for support and custom integrations. For over 100 users, or SSO and SCIM.
Talk to sales →On-premises
10%
of licence value, per year
Plus a one-time installation fee of 10% of licence value. You provide the hardware, software and GPUs.
Talk to sales →- Billed monthly at list price, no commitment — or save 20% on an annual commitment, 30% prepaid.
- The same per-seat price whether you are five people or five thousand.
- Every plan is the full product. Only how long memory is kept changes.
Keep the part you built. Stop maintaining the rest.
See how In Parallel keeps a long-term, access-controlled record of your meetings and decisions, and serves it to the AI tools you already use. 30-minute demo.