Brand Drift
Your brand will drift. Stylaform shows you when.
Drift is not a dramatic event. It is a hundred small, individually defensible decisions that add up to a product nobody designed.
Why every codebase drifts.
Speed is the point of AI-assisted development. Speed is also how an unapproved gray ends up in production on a Thursday afternoon.
A locked Stylaform gives you something drift can be measured against. Without a fixed reference, "off-brand" is an argument. With one, it is a comparison.
Enforcement
Your brand will drift. Stylaform shows you when.
Nobody decides to abandon the brand. It leaves one small compromise at a time, and then one day the product no longer looks like the thing you approved.
- A developer uses a slightly different gray.
- An AI agent invents another border radius.
- A new page introduces a different button.
- Someone adds a font because it was quicker.
- A generated component uses an inaccessible color pair.
- Unapproved fontInter
Approved: Source Serif 4, IBM Plex Sans. +9 pts if fixed
- Unknown color#7164FF
Not part of approved tokens. +6 pts if fixed
- Border radius mismatch16px
Expected: 6px. +4 pts if fixed
- Forbidden ruleGradient detected
Gradients are forbidden in this Stylaform. +3 pts if fixed
- Scan any live URL against a locked brand version, with crawl depth up to 6 pages.
- Scheduled re-checks, hourly, daily or weekly, plus a Run now action.
- A 0-100% brand score with plain-language bands: On brand, Partly on brand, Needs work.
- Findings sorted by score impact, each showing what a fix is worth in points.
- Click-to-highlight drilldown from an offending value to the exact source snippet and the brand rule that should replace it.
- Two repair paths: Fix the brand adopts the found value as an anchor and re-locks. Fix the page generates instructions and prompts for a developer or coding agent.
- A score trend graph over time, with per-target lines, date-based labels and before/after deltas.
- AI reviews alongside token checks in one history, filtered by target, run type and date range.
How a scan works
From a live URL to a ranked list of fixes.
- Point it at a URL
- Scan any live address against a locked brand version, crawling up to six pages of the target.
- Run now or on a schedule
- Hourly, daily or weekly re-checks, plus a Run now action whenever you have shipped something.
- Read a score, not jargon
- A 0-100% brand score with plain-language bands: On brand, Partly on brand, Needs work.
- Fix in order of impact
- Findings are sorted by score impact and each one states what fixing it is worth in points.
- Drill into the evidence
- Click a finding to highlight the offending value, the exact source snippet and the brand rule that should replace it.
- Choose which side is wrong
- Fix the brand adopts the found value as an anchor and re-locks. Fix the page generates instructions and prompts for a developer or coding agent.
- Watch the trend
- A score graph over time with per-target lines, date-based labels and before/after deltas for each run.
- One history for every check
- AI reviews sit alongside token checks in the same history, filtered by target, run type and date range.
Guides
Keeping one brand while an agent writes the code
Practical, tool-specific guides on brand consistency with AI coding agents, written for the tools you already have open.
How to build an app with AI without losing your brand
The five decisions to make before the first prompt, and how to hand them to Claude as tokens and a BRAND.md it can follow.
Read the guide →
Brand consistency when building with Copilot
Repository instructions, token files and review habits that stop tab-completion from quietly inventing its own design system.
Read the guide →
AI-powered design systems and how AI uses design tokens
What an agent-readable design system actually contains, why tokens change what a model writes, and who builds one.
Read the guide →
All three guides end in the same place: the tokens and rules Stylaform exports.
Stop letting the prompt choose your brand.
Decide what your product should look like once. Give every human and AI developer the same rules from then on.