From intake to graph: how we model brand DNA

Ingest without losing nuance

A brand is not a palette. It is not a font stack. It is not a tagline. It is a network of relationships: voice shifts by context, colors have roles, messages ladder from broad to specific, and rules constrain behavior differently across channels.

When you flatten that into a spreadsheet (hex codes here, font names there, tagline at the top), you lose the grammar. You keep the nouns but discard the verbs.

BrandMythos starts from a different premise: brand is a graph, not a document. Every source contributes a slice of how the brand shows up in the world, and the job is to model relationships, not just extract data points.

Five sources, five perspectives

URLs - the public face

Your website is the most visible expression of your brand. We crawl it and extract:

• Visual patterns: which colors appear where, which typography is used for what
• Voice patterns: how headlines differ from body copy, how CTAs are phrased
• Structural patterns: navigation hierarchy, page templates, component reuse

A homepage hero section tells us something different than a support FAQ page. Both are brand. Both matter.

PDFs - the canonical reference

Brand guidelines in PDF form contain the most explicit rules: "Use this color for CTAs." "Never use this word in marketing." "This is our messaging hierarchy."

We parse the document structure, extract explicit rules, and cross-reference them against what the website actually does. Discrepancies are flagged because often the guide says one thing and the site does another.

Figma - the design system

Figma libraries contain design tokens in their purest form: named colors, type styles, spacing scales, component variants. We import these directly and map them to CSS custom properties and Tailwind config values.

The advantage of Figma as a source is precision. A designer has already named things and organized them into categories. We preserve that structure rather than guessing from screenshots.

Drive folders - the institutional memory

Google Drive often contains brand assets that live nowhere else: logo variations, approved photography, presentation templates, internal style guides. We index these and extract metadata: file names, folder structure, sharing permissions (which tells us what is public vs. internal).

Social feeds - the living voice

Your social media presence is where brand voice is most dynamic. The way you respond to comments, the tone of your captions, the style of your stories. All of this is brand data that no PDF captures.

We analyze recent posts across platforms and extract voice patterns: formality level, emoji usage, sentence length, engagement patterns. This becomes part of the voice model.

Beyond hex codes: modeling relationships

Extraction is not a color picker exercise. We model when a color is used, how voice shifts by channel, and which messages belong in which context.

The knowledge graph

Below is a simplified JSON-LD shape showing how we model brand entities and their relationships:

{
  "@context": "https://schema.org",
  "@type": "Brand",
  "name": "Example Co",
  "slogan": "Clarity at scale",
  "knowsAbout": ["B2B SaaS", "design systems"],
  "hasOfferCatalog": {
    "@type": "OfferCatalog",
    "name": "Voice contexts",
    "itemListElement": [
      { "@type": "ListItem", "name": "support", "description": "Empathetic, concise" },
      { "@type": "ListItem", "name": "marketing", "description": "Bold, evidence-led" }
    ]
  },
  "hasBrandColor": [
    { "name": "Primary", "hex": "#9c4221", "usage": "CTAs, hero", "avoid": "body text" },
    { "name": "Accent", "hex": "#d97706", "usage": "secondary actions, highlights" }
  ]
}

The graph is not flat. Colors have usage rules. Voice contexts have tone attributes. Messages have audience targets. Every node connects to others through typed relationships.

For a deeper dive into building these graphs, see our guide to brand knowledge graphs for AI.

Ship and diff

Every export is versioned like code. When the brand evolves (new color role, voice refinement, updated messaging hierarchy), you see exactly what changed in a pull request.

## Voice - Marketing context
- Bold claims backed by customer stories.
+ Bold claims backed by quantitative data. Customer stories in support copy only.

This is how governance works at scale. Not by sending updated PDFs to a distribution list, but by reviewing brand changes the same way you review code changes: with diffs, approvals, and an audit trail.

The output formats

From a single intake, BrandMythos generates seven output formats:

1. CLAUDE.md - Universal agent instructions for Claude, ChatGPT, Copilot
2. .cursorrules - Editor-specific rules for Cursor code completion
3. AGENTS.md - Governance file defining what agents can and cannot do
4. Design tokens - CSS custom properties and Tailwind config
5. System prompts - For ChatGPT Custom Instructions and Gemini
6. JSON-LD knowledge graph - Machine-queryable brand entities and relationships
7. HTML brand guide - Shareable, hosted reference for humans

Each format serves a different tool, a different team, and a different purpose. Together they replace the PDF not with another document, but with loadable infrastructure.

Why this matters now

The research is clear: 88% of companies use AI daily, and 78% of employees bring their own AI tools. Every one of those interactions is an opportunity for brand drift or brand reinforcement.

The companies winning are not the ones with the best PDFs. They are the ones whose brand is embedded in the tools doing the work.

Try BrandMythos with your brand. Enter your URL and see your brand DNA extracted in minutes.