Build the real thing, without waiting on a sprint

Prototypes that are made of the actual product

The questions that decide a roadmap need a working answer: does this hold up against our real data, at our real shape and volume, with the actual latency?

Describe what you want and the AI scaffolds a working application — React, Vue, or vanilla JavaScript — with the API functions and endpoints behind it. The app runs live in your browser with real builds against real infrastructure.

What you take into the review is the thing itself. If it survives the review, the next step is continuing it.

Real data, in plain English

Browse your DynamoDB tables and RDS databases and ask for what you want: “show accounts that churned last month.” The AI writes the real query, shows you the generated statement with an explanation and your data.  Edit, add, and delete data directly as you work.

Internal tools, without the internal ticket

The dashboard the support team keeps asking for. The form that writes to a real table. The Monday-morning report that someone currently assembles by hand. The tool that reconciles two systems everyone works around.

None of these are hard. They queue because they need engineering time that’s committed elsewhere. This is the queue you can now clear yourself — including scheduled work, through a visual schedule builder with time zones and fire times.

Point at the problem

Markup mode lets you draw arrows, boxes, and notes directly on the running app and send that composited screenshot to the AI. It is the fastest way to communicate a visual change — and it is the same instinct you already use in a design review.

On infrastructure your engineers already approve of

Everything runs on native AWS services in your own AWS account, connected to your own GitHub or Bitbucket repos — the same stack your engineering team already runs and already trusts.

Safety is built into the workflow: AI changes arrive as reviewable diffs, backend resources the AI wants to create are surfaced for approval first, data operations confirm before writing, and data-loss guards protect unsaved work.

Nothing gets thrown away at the handoff

When a prototype becomes a product, engineering inherits code in a repository on the infrastructure it already runs on — the same environment they’d have used themselves, at full depth.

That’s the whole argument for building this way. The work you do to answer a question becomes the first version of the answer.