RapidCanvas

Build it once. It runs on its own.

The work happens in a real development environment, on the context and skills your team has already built up, and it ships from the same window it was written in. From then on the pipeline runs to a schedule, with a record of every execution. What you built on Monday is still running in March, and you can see that it is.

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Context and skills

The second solution is cheaper than the first.

Before it writes anything, the agent reads what your organisation already knows, then loads the reusable skills that encode how this kind of problem gets solved here. Nothing is rebuilt from scratch, and what you learn on this project is waiting for the next one.

Code › VS CodeRecording
Fourteen concept files read, then two skills loaded. The run says it plainly — “I have the customer context. Before drafting the architecture I'll load the two skills that gate any RC solution plan” — and names them: rc-platform-context and rc-invoice-reconciliation.
Build and deploy

From working code to a running service.

No handover to a platform team, no ticket, no separate release process. The person who built it ships it, from the same window they built it in, and what is running traces back to a commit anyone can look up.

FastAPI › Deployed services
The project's deployed FastAPI services in table view: one service, invoice-dashboard-api, showing the git path it was deployed from, the exact deployment timestamp, the branch, a Running status and its owner.
Deployed 2026-08-19 12:41:10, from main. invoice-dashboard-api runs from backend/invoice-dashboard-api on the main branch. The row carries the path, the branch and the exact deploy time, so what is live is never a question.
What ran, and when

Know what ran, when, and whether it worked.

Follow a build while it happens, then come back weeks later and the record is still there. Nobody has to reconstruct what the system did last Tuesday from memory, and a run that failed is still sitting in the list where anyone can find it.

Canvas › Meridian-Invoice-ReconciliationRecording
Eight recipes, twenty-seven nodes, every one of them inspectable. Selecting the flagged_invoices recipe reports SUCCESS, a last successful run of 4 mins 5 secs, two input datasets and three outputs.

On a schedule

It runs whether or not anyone remembers.

Say when the pipeline should run and it runs, on its own, with the same record of every execution you get from a manual build. The work stops depending on somebody being at their desk.

Scheduler › Create Scheduler
Setting up a schedule: a form naming the run, choosing between Daily, Weekly and Cron, picking Monday and a time of day, and reporting the next run date — above the full pipeline it will execute, drawn as its graph of recipes and datasets.
Daily, weekly, or a cron expression. A weekly run set for Mondays at 05:30 resolves to “Every Mon at 00:00” UTC and reports its next run before it is saved — over the graph of the pipeline it will execute.