Drop

Design-to-code handoff breaks in four predictable places. In a regulated estate, cloud tooling is a dealbreaker.

Design-to-code tooling reaches a regulated buyer through vendor risk, and most of it does not get through. The architecture points outward by design, so design context leaves the perimeter before anybody has argued about the output.

Your vendor risk team has already killed one of these tools. It sent design context out to a third-party API.

Your reviewers ended the conversation there, on data residency and concentration in a single third party, regardless of what the demo looked like.

So the question underneath the purchase is where the work executes, and who decides whether the output is correct.

This is where Drop can help. Drop runs inside your perimeter.

Each stage runs on your hardware, from auditing your design tokens to picture comparison, and the audit log proves nothing leaves. Three modules run on top of it, and this page shows whether Drop is buyable.

Two questions, and most tools only answer one.

Regulated buyers ask two things that most vendors collapse into one. Where does the data go, and who decides whether the output is correct?

Your reviewers kill a cloud tool at the vendor risk questionnaire, which is visible and survivable. Most tools here fail the second without anyone noticing, and that one reaches production.

Nothing leaves

Your reviewers will want to know what lands on your estate.

Drop is a Figma plugin and a Docker container, and your engineers download the container once and run it on your own hardware. It holds the audit of your design tokens, the model that writes the code, and the check that decides pass or fail.

Nothing calls home, and no third-party API sits in the path unless you point the container at a cloud model, which an on-premises deployment does not do. Where you need it, the container ships air-gapped, with no network connection.

The model inside never sees your designs as training data and cannot, because it reaches nothing that could collect it.

Local also means your hardware, so somebody in your organisation owns keeping it running.

That is why quarterly updates arrive as an upgrade your team applies on your own schedule, at a time you pick. A named engineer also walks your InfoSec team through the deployment before anything runs in anger.

The closest comparable design-to-code tools are built the same way. Their architecture points outward by design, so retrofitting a perimeter means rebuilding the product.

Other vendors in adjacent categories do ship on-premises deployments. If you are comparing Drop against one of those, the first question is already settled on both sides, and the one that matters is the second.

Nothing marks its own homework.

Every tool in this category now runs a language model somewhere, so what matters is where it sits in the process.

When the model that generates a component also judges whether that component is correct, you have a machine marking its own homework. These models flatter work put in front of them, and output resembling their own hardest of all.

Swapping the judge for a second model doesn’t fix it. In code, the effect is worse again.

A model asked to complete code that already contains a bug will confidently finish the bug.

Your rules decide pass or fail, and Drop applies them to deterministic checks that do not vary between runs. It draws the component and compares the picture against the approved design. It counts how much of that design the code used, and checks that every value is bound to a token.

It also checks the code against your written rules. That is why it returns the same answer on Tuesday that it returned on Monday.

A model writes the component. The things that decide whether it survives aren’t models; a failed check sends the model back to try again against the same bar, and no sign-off appears until every one has passed.

I hold this position because I tested it. I built a classifier on Drop’s own run logs that predicted whether a component would pass, comparing a Naive Bayes model against a decision tree.

I shipped the tree even though the other model matched it exactly on recall, because only the tree produced a rule a board could act on.

I am quoting no figures from that study on this page.

The predictors I used were already inputs the check uses to decide pass or fail, so the model was partly predicting itself. I am rerunning it with predictors independent of the check before any number from it goes in front of a client.

Which module you need

One pilot, then three modules in the order most people buy them.

The pilot runs twenty days against one component family, and every route through Drop starts there. It measures how far your code has already moved, and that measurement is what scopes whichever module follows.

G1 is the way in, because the pilot is the smallest purchase that establishes whether you have a problem worth solving. G2 is the product it hands over to, and G3 extends the same enforcement across every brand and market you run.

Nothing merges because Drop decided it should. Your reviewers approve or reject every change across all three, and each page has its own method and limits.

This page carries no price. What it costs depends on how much of your estate is in scope, and how many brands and repositories sit inside it.

What your security team gets to keep

What happens if I stop

Every vendor risk function asks the same question about a single supplier, and it is a fair question, so here is the answer in writing, ready to forward.

I deposit the source code and the container images with a third-party escrow agent. The agent releases them to you on defined trigger events.

Those include my ceasing to trade, ceasing to support the product, or failing to remedy a material breach within the notice period. You don’t have to take my word for continuity, because someone other than me verifies the deposit.

Whatever happens to me, your artefacts were never mine to hold.

The design system, the token files, the generated components and the pull requests live in your repository from the first day. Your version control, your review process. You have nothing to hand back, and nothing I could withhold, and the container writes the audit log to your storage, where it stays yours.

The software also keeps running if I disappear tomorrow, because it calls nothing.

There is no licence server to phone, no API for anyone to retire and no outside service to switch off. So the failure mode of my going away is that you stop getting quarterly updates, while your build keeps working.

Agency of one, by design, and I will not pretend otherwise to an enterprise procurement function.

Escrow and local execution answer continuity, and they do not answer response time. If you need a support guarantee I cannot credibly give, say so early, and either I structure it through a partner, or you buy something else.

Where to start

If you do not yet know how far your code has moved from your system, then Design Drift Remediation is for you. It also settles whether the accessibility statement you signed still describes production. It is the smallest way to find out.

If you want that system shipped as a package and held there on every change, then Design System Unification is for you.

If your problem is one system across several brands and markets, then Multi-Brand Scale is for you.