Content pruning and consolidation for clearer brand facts in AI search

Content pruning for AI search is the deliberate removal, redirection or archival of pages that create unsupported, duplicate or obsolete brand facts. It is not a traffic-cutting exercise or a promise of higher rankings. Inventory pages and claims, designate a canonical source for each important fact, preserve unique evidence and consolidate only when two pages serve the same audience and intent. Measure answer accuracy before and after the change, and keep a redirect and decision log so teams can understand what moved and why.

Find contradictions at the claim level

Crawl public pages, help centers, PDFs, campaign landing pages, newsroom posts and localized versions. Extract material claims about identity, products, integrations, pricing, availability, policies and performance. ModelSaid can expose prompt and topic gaps where assistants repeat an old statement or blend conflicting versions, which helps prioritize the inventory by user harm. Search for retired product names, expired year references and copied boilerplate, but have a human interpret context. Historical announcements and current documentation can legitimately differ when their dates and status are clear.

• Keep a page when it serves a distinct intent and its claims are supported and maintained.
• Update it when the purpose is still useful but individual facts or examples are stale.
• Merge it when another page answers the same need with stronger evidence and ownership.
• Redirect it when users have a clear equivalent destination and no historical context is needed.
• Archive it when the record matters but must be explicitly separated from current guidance.
• Remove it when it is harmful or unsupported and no honest replacement exists.

Create a canonical brand-fact registry

List each high-value claim, its approved wording, source URL, owner, effective date, allowed qualifications and review trigger. Include which surfaces repeat it and which team controls the upstream data. Product capabilities belong with product owners, legal terms with their approved policy source and organization identity with a durable company record. The registry need not be public, but the canonical claim must be. Use the schema validator to find markup that disagrees with visible facts, and the AI readiness checker to spot inaccessible or fragmented source pages before consolidation.

Consolidate without destroying useful context

Compare the pages section by section before merging. Transfer unique evidence, examples and inbound-link destinations that still help the shared intent. Write the surviving page answer-first, remove duplicated claims and add context for changed terminology. Redirect retired URLs directly to the closest equivalent; do not funnel every old page to the home page. Update internal links, sitemap entries, canonicals, structured data and downloadable assets in the same release. ModelSaid can draft brand-safe merged copy from the fact registry and explicit exclusions, but owners must verify that the draft did not erase a critical audience or condition.

1. Capture baseline branded and non-branded prompts, complete responses and citations.
2. Record which obsolete or contradictory facts appear and their likely public sources.
3. Approve a page-level keep, update, merge, archive or remove decision with an owner.
4. Implement content, redirects and internal-link changes; then test representative URLs.
5. Rerun the stable prompts and compare factual accuracy, ambiguity and cited sources.
6. Use ModelSaid to monitor movement and restore context if consolidation creates a new gap.

Judge the program by clarity, not page count

Track resolved contradictions, successful redirects, orphan reduction, current-source citations and answer accuracy. Preserve denominators and unchanged results. A better answer after pruning is not proof that deletion caused it, and a temporary traffic decline does not automatically mean the decision was wrong. Start with the AI visibility scan to see what assistants currently say, then monitor after releases and product changes. Review server logs and analytics for broken journeys while respecting privacy. The goal is a smaller number of accountable truth sources, not the smallest possible website: distinctive evidence, legitimate regional pages and useful historical records should remain when their scope is unmistakable. Keep the decision log available to content, support and product teams so removed wording does not reappear in a campaign or help article. Audit the registry after major launches, migrations, acquisitions and policy updates, when brand-fact drift is most likely.