On this page
Wire's content pipeline handles the full lifecycle of a markdown page. You create it from web research, update it when industry news arrives, expand it with targeted depth, and compare it against competitors. Every operation produces a complete markdown file with valid frontmatter.
Creating Pages
A new page starts with web research. Wire searches for current information on your topic, fetches relevant pages, and sends everything to Wire's AI with your site's editorial rules.
python -m wire.content create products/newcorp
This produces docs/products/newcorp/index.md with frontmatter (title, description, created date) and a body that follows your styleguide rules. The page is ready to build and deploy.
Wire automatically excludes your own domain from search results so the AI works with external sources only. Every page requires at least one external citation, enforced by the styleguide, checked by the audit system.
Updating with News
Content goes stale. Wire's news intelligence pipeline gathers industry developments and saves them as pending files alongside each page.
python -m wire.chief news products # Gather news for all product pages
python -m wire.chief refine products # Integrate pending news into pages
The refine step reads pending news files, feeds them to the AI alongside the current page and search data, then produces an updated version. The AI sees editorial rules that prevent it from removing existing content or breaking internal links.
After integration, news files move to an archive directory. Wire knows which news has been processed and never re-integrates archived files.
Expanding Coverage
Sometimes a page needs depth on a specific aspect rather than general updates.
python -m wire.content expand products/acme "API integration capabilities"
Wire runs targeted web research on the expansion topic, then feeds the results to the AI with instructions to add depth without duplicating what the page already covers. The expand command is useful when audit findings reveal a keyword opportunity that needs substantial new content.
Comparisons and Consolidation
Wire generates comparison pages between any two items in the same topic.
python -m wire.content compare products/acme products/betacorp
This creates a side-by-side analysis at docs/comparisons/acme-vs-betacorp/index.md. After building multiple comparisons, consolidate them into a hub page that links to individual comparisons and synthesizes common themes.
python -m wire.chief consolidate --topic products --min 3
Merge and Differentiate
When the deduplication system identifies keyword cannibalization, it resolves overlaps through two operations.
Merge absorbs a weak page into a strong one. The donor's unique content transfers to the keeper. A merge guard prevents the operation if the output would lose more than 20% of the keeper's body.
Differentiate rewrites both pages to reduce keyword overlap. Each page gets sharper focus on its unique angle, and both gain cross-links. Neither page is archived.
Both operations create redirects automatically. For the full picture on URL changes, redirect chains, and the right order when restructuring a site into topics, see URL Management.
The Enrich Command
Enrich replaces the old workflow of running separate expand, seo, crosslink, and refine commands. It combines all four into a single pass with one AI call per page.
python -m wire.chief enrich products
How Enrich Works (4 Phases)
Phase 1: Analyze (free). Wire builds an amendment brief for each page using only local data: search keywords, BM25 semantic scoring, keyword routing. No API calls. This phase identifies what each page is missing and what specific changes to make.
Phase 2: Filter and Report. Pages that are blocked (pending merge or differentiate), recently touched (within 28 days), lacking search data, or empty are skipped. Wire reports how many pages are actionable and suggests new pages to create for keywords that do not fit any existing page.
Phase 3: Research (web search). For pages that need content on new keywords, Wire runs targeted web searches, maximum 3 keywords per page to control cost. Results feed into the improvement prompt.
Phase 4: Improve (one AI call). Each actionable page gets a single AI call with a pre-built amendment brief. The brief tells the AI exactly what keywords to add, where to add internal links, which headings to restructure, and what news to integrate. The AI gets precise instructions, not open-ended requests.
Cost Comparison
| Method | Per page | 500 pages | What you get |
|---|---|---|---|
| SEO agency | $150-300 | $75K-150K | Manual audit + rewrite recommendations |
| Freelance writer | $50-100 | $25K-50K | Rewritten content, no data backing |
| Wire enrich | included in your AI subscription | a fraction of agency cost | Analysis + rewrite + internal links + keyword targeting |
The analysis phase is the expensive part of traditional SEO. It requires understanding the keyword landscape, identifying gaps, and deciding what to change. Wire does this with local computation at zero cost. Only the content generation step hits the API.
Keyword Routing
Not every keyword belongs on the page it currently ranks on. Wire uses three signals to route keywords correctly:
- Impression ratio. Keyword impressions divided by the page's top keyword impressions. Above 40% suggests the keyword deserves its own page, not a mention on this one.
- Page breadth. Pages with more than 2,500 words and 15+ keywords are broad. Broad pages should mention new keywords briefly, not expand on them.
- BM25 semantic fit. Does the keyword's vocabulary match the page's existing content? "Close" keywords get added as sections. "Distant" keywords get a mention or a create suggestion.
This routing prevents the common mistake of cramming every keyword into one page until it becomes unfocused and ranks for nothing.
Why This Architecture Works
Most AI content tools follow a single-prompt pattern: user provides a topic, the tool calls an LLM, output lands in a CMS. The results are predictable. Ahrefs found that 90.63% of all pages get zero organic traffic from Google. AI-generated content without search data backing performs even worse. It targets keywords the author imagines are important rather than keywords with actual demand.
Wire inverts this. The content pipeline never calls the AI without data. The create command includes web research. The refine command includes search terms and pending news. The enrich command includes a pre-built amendment brief with specific keyword targets, BM25 semantic scores, and keyword routing decisions. The AI receives precise instructions built from evidence, not open-ended requests.
The cost difference is not marginal. A manual SEO audit and content rewrite costs $150-300 per page from an agency. Wire's enrich command uses minimal token usage covered by your AI subscription. The analysis phase is free, only the AI call uses tokens. At 500 pages, that is a fraction of a single freelance article versus $75,000-$150,000. The gap exists because Wire automates the analysis that agencies do manually: reading search data, identifying keyword opportunities, building rewrite briefs, and verifying output quality.
HubSpot's content pruning study demonstrated the value of systematic content management. They pruned 3,000 posts (72% of audited content), saw a 106% increase in organic views, then continued pruning and reached a 458% increase. The lesson: most content hurts the content that should rank. Wire's pipeline automates this lifecycle: dead page detection, keyword cannibalization resolution, and targeted enrichment of pages with genuine search demand.
Discovery Steps
When refine, seo, merge, differentiate, or improve rewrites a page, Wire checks whether the H2 headings changed. If they did and a steps.md already exists, Wire regenerates the discovery layer automatically using Claude. No separate command needed. Pages without steps.md are not affected.
Pages that Wire has never touched still need a manual first pass:
python -m wire.content steps guides/getting-started
At build time, RULE-53 catches any step IDs that no longer match an H2 anchor in the article.
Dry Run and Resume
Every command supports --dry-run to preview changes without saving. Long batch operations support --resume to continue after interruption.
python -m wire.content create products/testcorp --dry-run
python -m wire.chief news products --resume