Most AI app builders on the market today are built around a single mental model: one person, one prompt box, one preview pane. That model works well for a solo builder moving fast, but it breaks down the moment a second person needs to be involved — a teammate reviewing progress, a designer checking a screen, a founder watching a build land in real time. This platform was built to close that gap: it takes the now-familiar "describe it, watch it get built" workflow and rebuilds it around a workspace instead of a chat, so a team — not just an individual — is the unit of work.
Collaboration Was an Afterthought, Not a Primitive
In the current generation of AI app builders, "sharing" a project usually means sending a link to a static export, or handing someone your login. There is no shared sense of this is what the AI is doing right now, no shared understanding of who is allowed to trigger a build, and no protection against two people accidentally kicking off conflicting AI runs on the same codebase at the same time.
We treated that as a data-modeling problem before it was a UI problem. If "team" and "build" aren't first-class objects in the schema, no amount of front-end polish will make collaboration feel real — it'll just be a chat window two people happen to be looking at. So the platform's schema starts from workspaces and roles, not from a single user's chat history, and every build is tracked as a run that belongs to a project inside that workspace, not to the person who typed the prompt.
Win #1 — Storage: Sandboxes That Don't Outlive Their Usefulness
Most AI builders spin up a persistent cloud container the moment you open a project, and that container tends to sit there burning disk and memory long after you've stopped actively iterating. Multiply that by every project a team has ever touched, and storage cost quietly becomes one of the largest line items in running the product.
The moment-to-moment loop — the agent editing files, the dev server hot-reloading, you clicking around a live preview — runs in a browser-hosted sandbox, not a cloud volume. Nothing is written to durable cloud storage just because you're iterating. A cloud sandbox only gets provisioned when a project genuinely needs durable, shareable execution, and that sandbox is leased, not owned outright: it carries its own expiry, so an idle project's cloud footprint gets reclaimed automatically instead of accumulating forever. A database-level constraint further guarantees there is exactly one active sandbox per project — never one per session, never one per teammate who happens to have the tab open.
The result: storage cost scales with active, in-flight work, not with the total number of projects a team has ever created.
Win #2 — Cost: One Agent Run at a Time, By Design
The single biggest hidden cost in an AI app builder is runaway concurrency — two people on the same team, or one person double-clicking, accidentally firing off two parallel AI agent sessions against the same project. Every one of those sessions burns LLM tokens and compute, and in the worst case they can even overwrite each other's work.
It is structurally impossible, not just discouraged, to have two concurrent agent runs on the same project. A unique constraint at the database layer enforces exactly one active run per project at any given moment — the second request simply can't start until the first finishes. On top of that, the live progress stream that powers the collaborative "watch the build happen" experience rides on the database's own realtime replication — there's no separate pub/sub service to stand up, monitor, and pay for on top of the database you already run.
The result: cost per active project is bounded by design, not by hoping nobody clicks twice.
Win #3 — Reliability: Team Edits That Can't Collide
| Guarantee | What It Prevents |
|---|---|
| Every build step (plan → patch → sandbox → commit → deploy) is claimed by exactly one worker, with a lease and a retry ceiling | A step running twice, or a stuck step hanging the project forever |
| Every progress event is append-only | History being edited or deleted out of the log after the fact |
| A single access-control function gates every table a teammate might read | Having a dozen different places to audit instead of one |
Inside the Builder
A team can watch — and, as the roadmap fills in, eventually act on — the same project without a build silently running twice, without one person's action clobbering another's, and without a client being able to quietly corrupt the shared event history.
The Collaboration Pitch, Stated Honestly
The category is full of solo-mode AI builders: one person, one prompt box, one preview. This platform's structural bet is that a workspace — not a single chat — is the right unit of work. Multiple teammates, each with a role, working inside one project, watching the same agent run its course live, with the database itself refusing to let two runs collide.
That is a real, defensible differentiator today.