Your company’s context, available to every AI tool you use.
In Parallel builds and maintains a live model of your business — decisions, plans, goals, meeting signals. The MCP server makes all of it available to the AI tools your team already works in. Claude, Cursor, Copilot, whatever comes next. One connection. Everything in context.
Get startedThe world model your AI tools have been missing.
Most AI tools are smart but blind. They don’t know what your company decided last Monday, what’s drifting in Q3, or what the exec team agreed in Tuesday’s standup. The In Parallel MCP server changes that — exposing your live world model as context, so every AI tool you use starts from reality instead of a blank slate.
Your decisions, goals, and plans — queryable by any AI
Every meeting signal, plan update, and strategic decision captured by In Parallel is accessible via MCP. Ask your AI assistant what was decided last week. Get a draft that already knows the context. No copy-paste, no re-briefing.
Live context, not a static export
This isn’t a data dump. The world model updates continuously as meetings happen and plans change. When your AI tools pull from In Parallel, they get the current state — not last month’s.
One integration, every tool
MCP is the protocol the AI ecosystem is converging on. Connect once and your world model is available wherever MCP is supported — today and as your stack evolves.
Get startedNotes as the ultimate AI context layer
Early access
Your AI tools finally
know your business.
Via MCP (Model Context Protocol), every AI tool in your stack draws from your structured meeting context. One prompt writes a board pack. Another maps dependencies. Included in every workspace.
Claude
Cursor
ChatGPT
Windsurf
Perplexity
Board packs
“Write a board update for Q1.”
Claude draws from every leadership meeting, every status review, every risk flagged across the quarter.
Code decisions
“Why was this feature scoped this way?”
Copilot finds the product meeting where the decision was made and the trade-offs accepted.
Client prep
“Brief me on Acme before tomorrow’s call.”
Every mention of that client across all meetings — commitments, issues raised, timeline changes.
Onboarding
“Why do we do X this way?”
Your AI finds the meeting where the decision was made and the rationale — in seconds.
Optimised for AI agents
Context is structured so your AI gets exactly what it needs — no full transcripts, no noise.
Semantic segmentation
Retrieves the segment about pricing — not the hour-long meeting.
Speaker diarization & roles
Every utterance attributed to a speaker. Your AI knows the CFO raised the concern.
Pyramid-structured summaries
Headline decision first, rationale second, full transcript on demand.
Cross-meeting threading
Monday’s steering committee decision connects to Wednesday’s sprint review.
Temporal awareness
Distinguishes today’s decision from last quarter’s by freshness score.
9 typed signal extraction
Decisions, actions, risks, dependencies — each typed and linked to people.
Governed context boundaries
Workspace isolation — your AI in one workspace cannot see data from another unless explicitly linked.
Permission-scoped queries — AI only receives signals from workspaces the requesting user has access to.
No cross-tenant leakage — multi-tenant by architecture, not policy. Client A’s context never touches Client B’s.
Full audit trail — who asked, which AI tool, which workspace, which signals returned.