Design QA for screens that don't have a URL.

This is not a raw pixel diff. Two screenshots at different scale factors will never subtract to zero, and a tool that reports that noise is useless. Findings are triaged: a 1px icon shift is labelled a nit, a wrong brand colour is labelled a blocker.

How it works

1. 01

   Export the frame

   Any PNG from Figma. No plugin to install, no edit access to the file, no Figma account required.

2. 02

   Grab the screenshot

   ⌘S in the iOS Simulator, or the volume+power screenshot from a real device. Android Studio works the same way.

3. 03

   Get the ticket

   Scale is normalised automatically. You get a 0–100 match score, every mismatch listed with what the design shows versus what shipped, and a copy-ready ticket at the bottom.

What it checks

Ten weighted categories, 61 sub-checks, weights summing to 100. The weight is also the cap: no category can ever deduct more than its share, however many findings land in it.

• Layout & spacing

  18

  Gaps, padding, margins, and the vertical rhythm between elements. The single most common source of real design-vs-build drift, because spacing is the thing engineers most often inherit from a stack default rather than reading off the design.

• Missing or extra elements

  15

  Presence and absence. The highest-consequence category and the one generic image-diff tools handle worst, because a missing element registers to them as a spacing difference rather than an absence.

• Typography

  14

  Size, weight, family, line height, letter spacing, case, and text truncation. Second only to spacing in frequency, and the category where honest hedging matters most — a screenshot cannot tell you a font stack.

• Color & contrast

  12

  Fills, tints, text colors, borders, and the contrast relationships between them. Judge relationally — the two screenshots may differ in capture gamma, so absolute hue matching is not the test.

• Sizing & proportion

  11

  The dimensions of elements themselves, relative to the screen and to each other. Distinct from layout-spacing: this is the box, not the gap around it.

• Alignment

  10

  Whether elements line up with each other and with the grid, on both axes, including optical rather than merely geometric alignment.

• Shape, radius & elevation

  7

  Corner radius, border weight, shadows, blurs, and the depth relationships they create.

• Iconography

  6

  Icon choice, size, weight, and style consistency. On iOS this is largely an SF Symbols question.

• Content & copy

  4

  The actual words, where they differ structurally rather than as sample data.

• State appearance

  3

  Whether the state shown in the implementation is the state the design specifies — and whether states visible in a static capture render as designed.

Pay for what you check

For context on the price: the cheapest competing tool with AI analysis starts at $39/mo with no free tier, and its full visual-analysis tier is $249/mo. Those are good tools, built for web teams with a procurement process. This is built for the person who has one screen to check right now.

Questions

Can't I just paste both screenshots into ChatGPT?

You can — once. You'll get different findings every run, a vibes-based score (models rate themselves ~9/10 unprompted), and whatever format it feels like today. Here the model only reports findings; the score is computed server-side against a 61-check weighted rubric, so an 87 means the same thing on every screen, every run. That consistency is what lets a team use it as a handoff gate — and the output is already a severity-tagged ticket, not a chat reply you reformat by hand.

Does this work for web too?

Yes — feed it a browser screenshot and it works fine. Mobile is what it's tuned for and honestly where it's most useful, because the web already has half a dozen tools that plug into a live URL and read your CSS directly. Those are more precise than an image comparison when a URL exists. When one doesn't, they can't help you at all.

Do I need a Figma plugin?

No, deliberately. Most alternatives run inside Figma, which means you need edit access to the client's file — something contract devs frequently don't have. This takes an exported PNG, so anyone who can see the design can run the check.

How accurate is it?

Strongest on the things that change a screen's meaning: a missing or extra element, a wrong brand colour, a type weight that shifted, a hierarchy that inverted. Weakest on fine geometry across a full-length screen — a corner radius that dropped a few points, or spacing drift under about 5% of the screen's width, can slip past it, because at that scale the difference is smaller than what the vision model resolves in one pass. Crop to the region you care about and it sees far more. It also can't see what a still image doesn't contain: interaction states, scroll behaviour, dynamic type, anything below the fold. Treat it as a well-prepared reviewer with good instincts and imperfect eyes, not a compiler.

What if the screenshot is a different size than the frame?

That's the normal case, not an error case. Simulator captures come out at 2x or 3x; frames are usually 1x. The engine establishes a shared reference element and reasons in proportions, so a 3x iPhone capture against a 1x frame is fine.

Does it send my designs anywhere?

The two images go to Anthropic's Claude via OpenRouter to generate your report, and are not used to train anything. Nothing is written to a database — there isn't one. If you're under an NDA that forbids third-party processing of client assets, don't use this; use a local overlay tool instead. Straight answer, since you were going to ask.

Where does the score come from?

A published rubric: ten weighted categories summing to 100, and a fixed points-per-severity formula applied by the server. The model finds and classifies; it never picks the number. That's why the same pair of screenshots scores the same twice, and why the number survives an argument with an engineering manager.

Launched today. Here's the honest version.

There are no customer logos on this page because there are no customers yet — this shipped on 13 August 2026. Rather than borrow credibility, here's what's actually verifiable.

Found a bug or a bad call? I'm one person and I'll actually reply. jordan@jwcholding.com