Real work, spatially organised.

The pain

You're three weeks into a literature review. Forty-seven PDFs, a dozen open tabs, and a chat history you can't search. The model keeps confidently citing papers you never gave it.

On the canvas

Drop every paper, dataset, and competitor report into a knowledge node per theme. Spin up a chat node beside each one — methods, findings, limitations — that can only answer from its attached sources. Then connect them into a meta-synthesis chat that compares across themes and surfaces the contradictions you hadn't noticed yet.

The outcome

Every claim points back to the chunk it came from. You finish the lit review in days instead of weeks, with citations a reviewer can actually verify.

The pain

Your codebase has eight years of context. Every new feature touches three services, four teams, and a Slack channel that scrolled past last Tuesday. Pasting files into a chat box gets you 40% of the way and then collapses.

On the canvas

Connect a knowledge node to the repo. Add chat nodes for design, implementation plan, and PR review — each grounded in the same code but tasked differently. Wire in tool access so the implementation node can open issues in GitHub, run searches across Linear, and post a daily summary to Slack. A planning chat at the top of the canvas keeps it all aligned across sessions.

The outcome

The agent stops hallucinating function signatures. Reviewers see a PR description that actually traces back to the spec. Next sprint, the canvas is still there — picking up exactly where you left off.

The pain

You're scoping a new product line. There's competitive intel, customer interviews, internal roadmap docs, and a board deck due Friday. The thinking is happening across six tools and zero of them remember the others exist.

On the canvas

One canvas for the initiative. Knowledge nodes for interview transcripts, market research, and the current roadmap. Parallel chat nodes that argue different positions — a bull case, a bear case, a phased rollout. A decision chat draws from all three and surfaces the trade-offs in plain language.

The outcome

Walk into the meeting with a map, not a deck. The reasoning is legible, the assumptions are visible, and the next thirty decisions live on the same surface as the first one.

The pain

You're producing a deeply researched essay or a multi-part course. The outline lives in one tool, the research in another, the draft in a third — and the AI you keep asking for help has amnesia between every session.

On the canvas

Pin the research as knowledge nodes. Build a chat node per section that drafts from those sources in your voice. Connect them to a structural chat that watches for repetition, drift, and gaps. Iterate in place — the canvas is the manuscript and the workshop at once.

The outcome

Drafts that don't contradict themselves three chapters in. A working surface you can actually re-open in a month and still understand.

The pain

You run three engagements in parallel. Each one has its own data room, its own jargon, its own deliverables. Switching context costs you an hour a day, and the model has no idea which client it's talking about.

On the canvas

One canvas per client. Knowledge nodes for their documents and data; chat nodes for analysis, deliverable drafts, and meeting prep. Tools wired in for the systems that engagement actually uses. Close the canvas at the end of the day — open it tomorrow and the entire context is right there, untangled from everything else.

The outcome

Less context-switching tax. Tighter deliverables. A defensible record of how each conclusion was reached, when the client inevitably asks.