Compare to
Read.ai
Instead of helping you search past decisions, In Parallel ensures decisions drive what happens next.
Where Read.ai helps you find what was said, In Parallel makes sure it gets done.
In short
Read.ai suits organisations that want enterprise-wide search across past meetings and documents, plus engagement analytics for improving meeting culture. Read.ai helps people find what was said, while In Parallel keeps decisions, owners and risks as a structured record with a status and a source, so teams whose decisions get lost between meetings should choose In Parallel.
Updated
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?
From remembering to delivering.
From knowledge layer to coordination layer
Read.ai builds a knowledge layer: searchable meeting summaries, enterprise-wide content search, and analytics about how meetings are run. In Parallel builds a coordination layer: a shared record of decisions, owners and risks, and workspace pages that update after each meeting, so decisions translate into action. These are fundamentally different approaches to the meeting intelligence problem.
From searching for decisions to tracking decisions
With Read.ai, you can search across meetings to find when a decision was made or what was discussed. With In Parallel, the decision is already a record: it has an owner, a status and a link to the meeting it came from, and it stays open until someone settles it. The information is kept rather than retrieved after the fact.
From meeting engagement to execution outcomes
Read.ai provides meeting engagement metrics -- who spoke, for how long, sentiment analysis. These are useful for improving meeting culture. In Parallel focuses on execution outcomes -- what was decided, who owns what, and what is still open. Engagement metrics tell you about the meeting. Execution intelligence tells you about the work.
From backward-looking to forward-driving
Read.ai is primarily a backward-looking tool -- it helps you understand what happened in past meetings. In Parallel is forward-driving -- it turns what happens in meetings into decisions, owners and open risks the organization can act on. One creates a record. The other creates momentum.
When to choose Read.ai
- You need enterprise-wide search across meeting content and documents
- Meeting engagement analytics are important for improving team culture
- Your primary problem is finding information across past meetings
- You want a copilot for individual meeting productivity
When to choose In Parallel
- Decisions made in meetings need an owner and a status, not just a summary
- Decisions get lost or relitigated between meetings
- You want Claude, ChatGPT or Copilot to answer from what your meetings decided
- Managers spend too much time on status updates and alignment instead of decisions
Frequently asked questions
Can In Parallel replace Read.ai for meeting analytics?
In Parallel focuses on execution coordination rather than meeting analytics. If your primary need is understanding meeting engagement metrics and participant analytics, Read.ai offers specialized tools for that. If your goal is turning meeting decisions into a shared record with owners and status, In Parallel is purpose-built for it.
Does In Parallel offer enterprise search like Read.ai?
In Parallel does not position itself as an enterprise search tool. Instead, it keeps a structured record of decisions, owners and risks from meeting conversations, and its chat answers questions from that record with sources. Rather than searching transcripts for past decisions, you ask what was decided and get the record.
How do In Parallel and Read.ai differ in their approach to meetings?
Read.ai treats meetings as a source of knowledge -- generating summaries, analytics, and searchable records. In Parallel treats meetings as a source of execution intelligence -- extracting decisions, ownership changes, and commitments, then keeping them as a structured record with a status and a source. One creates a knowledge layer; the other creates a coordination layer.
Can I use Read.ai and In Parallel together?
You could use Read.ai for meeting engagement analytics and In Parallel for execution coordination. However, since In Parallel also captures meeting content, many organizations find they do not need a separate meeting analytics tool. The key question is whether your bottleneck is understanding meetings or coordinating execution.
Price the knowledge, not the people.
41% of the work week goes to coordination. In Parallel gives it back.
Plans
Professional
€29
per user / month
120 Knowledge Units per user a day. For teams under 50 users: meeting notes, chat, pages and workspaces.
Business
€69
per user / month
250 Knowledge Units per user a day. Adds sharing: give people access to a workspace, with set access rights for what they can see. Up to 5 app integrations. Automations when released.
Enterprise
Custom
One annual price, invoiced in advance. From 50 users, everyone in the company on one contract: unlimited app integrations, SSO and a named contact.
Talk to sales →On request
Custom
Dedicated EU cloud, on-premises and professional services
Shared EU cloud is standard and included. Onboarding, trainings and the named contact are always included.
Talk to sales →- Billed monthly at list price, cancel any month — or prepay a year and save 20%.
- One plan for the whole company: moving up moves everyone. Every user who signs in has a seat, including clients added to a workspace; AI agents need none.
- Reads from your own AI tools over MCP are free within fair use. When the pool runs out, 1,000 KU top-ups for €250 are added automatically, up to a cap your admin sets.
Move from remembering to delivering.
See how In Parallel turns meeting decisions into execution. 30-minute demo.