Brand Style Guide
Most brand guides live as a 60-page PDF and a Figma file two designers can find. This playbook turns those guidelines into structured rules the rest of your stack can actually read, then checks every asset against them before it ships. Tone, voice, colour tokens, type ramp, logo clear space, photography direction, the lot.
The problem
Brand drift is a governance problem, not a craft problem. The guide exists. The team has read it. But once five writers, three agencies, and an AI tool are producing copy and visuals in parallel, on-average-correct output starts shipping under the brand. By the time the next audit lands, the guide and the work have quietly diverged.
How DataChi runs it
DataCHI ingests your existing brand system from PDF, Notion, Figma, and your DAM, then encodes it as rules: voice patterns, banned phrases, approved colour tokens, type pairings, logo lockups, image treatments. Every AI-generated asset gets scored against the rules with a specific flag where it drifts. Reviewers see the diff against the guide, not a vague off-brand warning.
What's included
- Guideline ingestion from PDF, Notion, Figma, and DAM
- Voice and tone rules with banned-phrase and reading-level checks
- Visual rule set covering colour tokens, type ramp, logo lockups, and image treatments
- Per-asset drift report showing which rule was broken and where
- Channel, locale, and sub-brand overrides for legitimate variants
- Versioned guide that records every rule change with rationale
Who it's for
Brand directors, creative ops leads, and in-house studios trying to hold a consistent brand across multiple writers, agencies, and AI tools.
Outcomes
- Output that's on-brand by default, not by review
- Drift caught in days, not in the next quarterly audit
- Reviewer hours on craft rather than catching the same recurring break
- An auditable record of when the guide changed and why
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Ready to run Brand Style Guide with DataChi?
See the playbook in action with your data, your stack, and your team.
Book a 20-minute demo