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.

Expert-led AI Workshop
Book a Discovery CallComplimentary 30-min call to assess fit
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.

Popular Reads

Thought Leadership

Context is Your New IP

Discover why enterprise AI agents fall short in real-world decisions, and how closing the context gap is the key to making them reliable in production.

Thought Leadership

Full AI Autonomy Is a Myth. Managed Autonomy Is a Moat.

Explore how companies unlock greater value from AI by combining agentic systems with human judgment instead of chasing full autonomy.

Thought Leadership

Building AI Applications Your Teams Will Actually Trust and Use

Explore what it really takes to move from AI prototypes to trusted, high-stakes tools, where designing for trust matters more than confidence scores.

View all resources

Got questions? We're here to answer them for you

Have more questions?
Contact our support team to get what you need.

The person who built it, from the same environment they built it in. There is no handover to a platform team, no ticket and no separate release process. What ends up running traces back to a commit anyone can look up, so the deployment and the code are never two different stories.
The pipeline runs to a schedule and every execution is recorded. You can follow a build while it happens and come back weeks later to the same record, including the runs that failed. Nobody has to reconstruct what the system did last Tuesday from memory.
No. Deployment, scheduling and the execution record are part of the environment rather than something a separate team operates on your behalf. That is the difference between a solution that ships and one that waits in a queue.
Because it does not start from scratch. Before anything is written, what your organisation already knows is read back in, along with the reusable skills that encode how this kind of problem gets solved here. What you learn on this project is waiting for the next one.
Yes. It is a development environment rather than a wizard, so the output is code your engineers can read, review and own. That is what makes it reviewable in the first place, and what stops the result being a black box nobody can change later.
It is on the record like any other run. Every execution is kept, including the ones that failed, so the question is answered by reading what happened rather than reconstructing it. You can follow a build while it is happening and come back to the same record weeks later.
Yes, because what they receive is code. This is a development environment rather than a wizard, so the output can be read, reviewed and owned by your own team. A solution nobody can change later is a solution you are renting, not one you have.
Because it traces back to a commit anyone can look up. The person who built the solution deploys it from the environment they built it in, with no handover, no ticket and no separate release process in between. The deployment and the code are never two different stories.
No. It runs in your own cloud account, a managed environment or SaaS, and inherits the network, identity and audit posture that account already holds. Scheduling and the execution record are part of the environment rather than a service a separate team runs for you.
A running service, the code behind it, and the record of what it has done. Not a prototype waiting for someone else to productionise it, which is the usual gap between a promising pilot and a system anyone depends on.
RapidCanvas
G2 Fall 2026 awards: Momentum Leader, High Performer, Best Support, and the Users Love Us milestone
G24.9 out of 5
Gartner Peer Insights5.0 out of 5
Certifications: AICPA SOC, ISO 27001, ISO 45001, GDPR, HIPAA

Subscribe to our newsletter

Real AI transformation,​accelerated.

PrivacyFulfillmentTerms

Your privacy choices

Choose which optional data uses to allow on this browser. Turning advertising off opts you out of the sale or sharing of your personal information.

Strictly necessary storage is always on. See our Cookie Policy and Privacy Policy.

© 2026 RapidCanvas