Building a Brand Wiki

Build an AI-Friendly Brand Wiki (Your Canonical Source for Models)

An AI Company Wiki is your brand’s neutral, fast, and citeable source of truth. It’s written for model consumption first and human skimming second: stable URLs, datum-dense pages, visible changelogs, and clear schema so ChatGPT, Copilot, Perplexity, and Gemini can quote you confidently.

Why a Brand Wiki Now

AI assistants are the new front desk. When buyers ask complex questions, models pull from sources that look like documentation, not marketing. A reference-grade wiki concentrates authority, reduces contradictions across your site, and increases the odds models cite you as the definitive answer.

What “Model-Ready” Means

• Neutral, fact-dense prose with definitions, tables, and examples

• Stable URL structure with shallow depth and canonical tags

• Clear update history (version badge + dated change notes)

• JSON-LD applied per page type (Article, FAQ, HowTo, CaseStudy)

• Fast HTML (minimal JS, no heavy client rendering) and tight headings

Information Architecture (IA)

• Top-level index: /wiki/ with an A–Z and category index

• Category hubs: /wiki/product/, /wiki/pricing/, /wiki/security/, /wiki/integrations/, /wiki/comparisons/, /wiki/faq/

• Page depth ≤ 2; slugs reflect specific intents, e.g., /wiki/comparisons/d-and-o-vs-e-and-o/

• Consistent page anatomy: Summary → Definitions → Details → References → Changelog

Core Page Archetypes

• Product Overview: problem, audience, capabilities, limitations, specs, references, changelog

• Pricing & Eligibility: plan matrix, inclusions/exclusions, examples, terms, last-updated

• Security & Compliance: controls, certifications, data flow diagrams, subprocessors, policy links

• Integrations: supported versions, endpoints, setup steps, known constraints, samples

• Comparisons: neutral criteria, side-by-side table, sources, decision guidance

• FAQs: one-question-per-URL for high-intent queries; short answers + references

• Case Studies: claim, context, method, results, limitations, source data links

Writing Guidelines Models Reward

• Lead with the definition and the answer; push narrative lower on the page

• Prefer concrete nouns, units, and examples over adjectives

• Cite sources (first- and third-party) inline where claims matter

• Use consistent term glossaries; define acronyms on first use

• Add “Known Limitations” to increase perceived honesty and trust

Technical Standards

• HTML-first rendering; page is fully readable with JS disabled

• Headings use a strict hierarchy; no decorative H tags

• Canonical tag to one URL per concept; avoid duplicate near-synonyms

• Changelog block with date, editor, and summary; expose lastmod in sitemap

• JSON-LD per archetype; include about, isBasedOn, dateModified, and citation where relevant

Governance and Workflow

• Assign page owners and SLAs (e.g., quarterly review for pricing, monthly for integrations)

• Require PR-style approvals from Legal/Sec/PM for sensitive pages

• Maintain a wiki backlog labeled by “evidence gap,” “consistency fix,” and “new prompt target”

• Log substantive edits in the page changelog and a public “What’s New” roll-up

14-Day Launch Plan

• Day 1–2: Inventory conflicting facts; pick ten high-intent questions to own

• Day 3–5: Stand up /wiki/ skeleton, IA, and components (summary, table, changelog, references)

• Day 6–9: Draft five archetype pages (Product, Pricing, Security, Integration, Comparison)

• Day 10–11: Apply schema, canonical, and internal links; publish sitemap and lastmod

• Day 12–13: Place two corroborating references on trusted third-party domains

• Day 14: Announce the wiki; monitor assistant citations and refine titles/definitions

Measurement and Diagnostics

• Citation Concentration: percentage of model citations landing on /wiki/ URLs

• Qualified Citation Velocity: new authoritative citations per quarter

• AI Referral Conversion: sessions arriving from assistant-cited pages → demo/meeting

• Evidence Coverage: completion of required fields per archetype across top intents

Common Pitfalls to Avoid

• Marketing tone over reference tone (models downrank vague claims)

• Deep or changing slugs that break previously cited URLs

• Overloaded “ultimate guides” with multiple intents on one URL

• PDFs without HTML twins (harder to parse, slower to refresh)

• Schema copy-paste without aligning to on-page facts

Production Checklist

• One intent per URL; explicit “This page answers…” summary

• Definitions and decision tables above the fold

• References section linking to primary data, standards, or docs

• Visible changelog and “last reviewed by” badge

• Internal links to sibling pages a buyer will “need next”

Bridge to Pipeline

• Add action modules to wiki pages: “Compare vs X,” “Security Review Pack,” “Pricing Request,” “Book a Consult”

• Use stable, crawlable CTAs with clear destinations and UTMs

• Mirror the same facts in sales enablement to keep human and model answers aligned

Getting Started

• Pick three intents you must own this quarter and convert their existing content to reference pages

• Publish the /wiki/ hub and ship weekly updates with visible version history

• Seed two external corroborating sources per critical page and cross-link back to canonical entries

CTA

See a sample AI Company Wiki, architecture kit, and page templates. Book a demo to have Unusual stand up your model-ready wiki and start earning citations fast.