Pixel Perfect by Baz - Make designs and implementations match. Every time.

The problem we solve

Designers and product managers are deeply involved in product delivery today. That is good. The hard part is making their designs actually behave and look the same once they become interactive code. Small gaps add up: spacing drifts, icons move, copy truncates, and interactive states do not match the intended design. These gaps erode trust between design and engineering, slow releases, and increase rework.

Design systems and component libraries like Storybook help. They create conformity, but they need constant maintenance and governance. They break down at scale because teams diverge, tokens drift, and edge cases pile up. Maintaining parity between a living design file and a living codebase is a continuing, expensive effort.

How Baz approaches the problem

We treat designs as first class, machine-readable specifications and keep the verification where code runs. The flow is simple and practical:

1. Codify the design
   Figma artboards and attached screenshots are converted into structured visual specifications. One specification covers a whole component as a designer would name it: structure, key copy, relative spacing, and styling intent. This makes design intent explicit and testable.
2. Connect spec to implementation
   Those visual specs are correlated to the actual implementation surface. We collect browser snapshots and accessibility-style page representations so we can compare what the UI looks like and how it is structured in the DOM with what the design specifies.
3. Automated visual verification
   Baz automatically compares the design specification to live UI evidence. The comparison is not a blind pixel diff. It understands component boundaries, layout and proportion, copy presence, styling weight and interactive states, and reports differences that matter to designers and PMs.
4. Architecture, simplified
   Under the hood we built an architecture that links three generalized pieces: the design source, the runtime UI evidence, and the code that produced that evidence. The system maps design elements to UI elements and to code locations, runs targeted visual and structural checks, and captures concise evidence for every mismatch. This architecture is purpose built for scale and for multi-team workflows without tying you to a single framework or implementation detail.

How it works

A great human design reviewer works by following a tight, repeatable logic. First they get context, read the intent, and inspect the design as a set of named components. For each component they write one concise acceptance spec that captures structure, key copy, relative spacing and styling intent, and the interactive states that matter. The goal is a single, testable sentence of design intent per component.

Next they exercise the implementation like a user. They navigate, interact, and gather two kinds of evidence: a structural snapshot that exposes the DOM and refs for interaction, and visual screenshots that show spacing, proportion and styling. They compare the evidence to the component spec, mark the exact visual failures, and capture concrete proof for each failure.

A top reviewer then traces each failure to cause. They map the observed UI element back to the place in the code that renders it, decide if the fix belongs in the component library, the page, or the design tokens, and describe the minimal remediation that preserves intent. Finally they package findings into a short report that includes the spec, the UI evidence, the code locus, and a recommended fix.

Baz reproduces that exact logical flow in automation. Specification extraction, focused per-component validation, evidence-first checks, explainable mapping from UI to code, and a short remediation recommendation. The result is the same crisp output a senior reviewer would produce, at scale and in parallel, ready for the next step of proposing or applying the fix.

The result

Baz code reviews stop at nothing short of traceable conclusions. When a visual requirement is not met, Baz points to the exact place in the implementation surface and the exact place in the code that caused the mismatch. Reports include the design, the UI evidence, and a clear explanation of what is off and why. That removes guesswork from design review and shortens the path to a fix.

Pixel Perfect turns the subjective visual review into objective with traceable evidence: it codifies component-level intent from Figma, compares it against live UI snapshots, and points to the exact code locus responsible for any visual mismatch. By removing ambiguity and supplying screenshots plus precise code references, it replaces manual back-and-forth with a single actionable report, speeding fixes, restoring trust between design and engineering, and letting teams ship polished interfaces at scale.

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What’s next

Pixel Perfect is the verification step. The next step is closing the loop. We are building a Baz agent that will not only find the mismatch but will propose and, when you want it to, complete the coding loop by producing the minimal, safe change to bring the implementation into alignment with the design.