AI SEO System: The Full Feature List
A goal-driven content system that researches keywords, builds SEO-optimized pages matched to search intent, ships each page with schema markup and sitemap updates, and monitors how pages perform in search and on-site. Every template change and every content-strategy update can be reviewed before it ships.
Below is a complete list of what the system does, including the universal features (image generation, learning, background automation) that work the same across other system types.
Pillar Pages
Deep long-form content on a core topic, designed to earn topical authority and anchor a cluster of related content. The coding agent builds each pillar with a consistent table of contents, section depth, and internal linking pattern.
Sub-Pages
Focused pages that support a pillar, each targeting a specific long-tail query. Sub-pages link back to their pillar, so traffic and ranking signals flow through the cluster instead of staying siloed on one URL.
Article Pages
How-to, list, comparison, and guide formats with structured content sections and consistent templating. Every article ships with schema, a table of contents, and on-page headers that map to the target keyword.
Product Pages
Commerce-oriented pages with Product schema, structured pricing, availability, and review markup. Feature blocks, FAQs, and trust signals follow a consistent pattern so every product page looks and ranks the same way.
Landing Pages
Conversion-focused pages with a clear CTA and lightweight body content. Built for paid traffic drops, email campaigns, or organic entry points where the visitor already knows what they came for.
A research agent runs a multi-step pipeline every 15 minutes to find keyword opportunities, classify search intent, and surface content gaps. Proposals reach the coding agent only after a skeptical step reviews them for realistic ranking potential and alignment with your target market.
Keyword Discovery
The research agent surfaces keyword opportunities matched to your target market, prioritizing realistic rankings over head-term traffic. Long-tail clusters with achievable competition are preferred over broad terms that will never rank.
Competitor Scan
Monitors competitor pages on the keywords you care about, identifies what ranks, and extracts patterns the system can use. Title formats, content depth, schema choices, and internal linking structures all feed back into your own templates.
Content Gap Identification
Compares your deployed pages against the topics your audience is searching and surfaces gaps where no page exists yet. Gaps become goal-ready proposals with a suggested page type, target keyword, and search intent classification.
Search Intent Classification
Each keyword is classified by intent (informational, commercial, navigational, transactional) so the coding agent picks the right page format. A commercial keyword gets a product or landing page, an informational one gets a guide or how-to.
The adaptive coding agent builds a page through a multi-step pipeline (plan, build, review, fix). Every page ships with schema markup, enters the sitemap, and is verifiable before it goes live.
Plan, Build, Review, Fix
Every page goes through a full multi-step build cycle. A reviewer agent checks the output for SEO quality, content depth, and template consistency before publishing. If the review fails, a fix pass addresses the issues; pages only deploy when the reviewer clears them.
Template-Driven Consistency
A single reference template defines the CSS, HTML structure, and pattern library every page uses. Changes to the template propagate to future pages, and hundreds of existing pages keep working because existing class names and styles are preserved.
Schema Markup On Every Page
Article, Product, FAQ, HowTo, Organization, and BreadcrumbList schema is applied based on the page type, giving search engines the structured data they need to surface rich results. The schema library evolves as the learning agent notices which patterns earn rich snippets.
Automatic Sitemap And Robots
Every new page enters the domain's XML sitemap, and the robots.txt is kept current, both redeployed along with each page. Search engines pick up new content on their next crawl without you needing to submit anything manually.
Pages deploy to your domain on the backend you choose. The system handles the deployment mechanics, you keep ownership of the content and the domain.
Your Domain, Your Infrastructure
Pages are built into your site, not rented from a third-party platform. You keep the content, the URLs, the organic traffic, and the brand equity every ranking page earns.
Flexible Deployment
Static hosting, managed hosting, or direct deployment to your own server. You pick the shape that fits your stack and the system handles the mechanics. Adding a new domain is a single command.
Clean URL Structure
Every page deploys with an SEO-friendly URL based on the page name, not a query string or opaque ID. Internal links use the same clean structure so the whole site reads naturally to search crawlers.
Generate On Demand
Ask the chat agent "generate an image of [description]" and the system creates a PNG using the Gemini CLI on your existing subscription. No separate API key, no extra billing. Images appear in a shared library at work/images.
Universal Across System Types
The image generator is a shared tool any background agent can use. Featured images, inline diagrams, and section art all draw from the same library. Browse images by date, prompt, and model.
Auto-Attach To Pages
The coding agent picks a relevant image from the library or generates a new one for each page it builds, optimizing for og:image social previews and featured-image slots. Alt text is generated along with the image and saved with the asset.
The system pulls live data from Google Analytics 4 and Google Search Console every hour. Every decision about which pages to improve, which keywords to target next, and which content approaches are working is grounded in actual numbers.
Hourly GA4 Refresh
Sessions, bounce rate, engagement rate, and conversions per page path. The coordinator and learning agents read this each cycle to decide which pages need attention and which templates are winning.
Hourly GSC Refresh
Search queries, clicks, impressions, CTR, and average position per keyword and page. This is the raw data behind "is this page actually ranking," pulled and cached every hour so answers are instant.
Per-Page Insights
Every page the system builds is tracked by URL. Ask "how is the page about [topic] doing" and the chat agent returns recent clicks, impressions, ranking position, and engagement for that specific page.
Ranking Trends Over Time
GSC snapshots stack across report pulls, so the learning agent can see whether a page is climbing, flat, or slipping. Recommendations reference the trajectory, not just the current snapshot.
Every action the system takes (page built, template updated, schema added, research completed) is logged as a structured outcome. The learning agent compares outcomes against the GA4 and GSC data and generates improvement ideas grounded in what actually worked. A skeptical step reviews each idea against your safety rules before it becomes a suggestion for your review.
Outcomes Logging
Every meaningful action becomes a structured event. The learning agent uses these to find patterns: which page structures get traffic, which keyword strategies produce results, which content approaches engage visitors.
Grounded Suggestions
Learning ideas reference real numbers: "pages using the comparison-table template have 40 percent higher CTR in GSC over the last 14 days, roll this pattern into how-to templates." Not "consider comparison tables."
Suggestion Lifecycle
Approved suggestions persist until you mark them implemented. Pending suggestions older than 30 days expire automatically (with a nudge at 7 days so nothing piles up unreviewed). Rejected suggestions also expire at 30 days.
Pattern Memory
When the learning agent identifies a clear pattern from outcomes data, it creates a memory entry that all background agents read each cycle. Over time the system gets sharper about what works for your audience without you needing to tell it.
Hourly Analytics Pulls
GA4 and GSC reports are pulled every hour so the system always has fresh data. The chat agent answers "how are we doing" instantly from the latest snapshot without burning API quota on every check.
Daily Maintenance
Cleans up stale logs, prunes expired suggestions, writes a status report (suggestion counts, recent activity, confidence, discovery), and refreshes anything that needs daily attention. The status report is what the chat agent reads when you ask "what is happening."
Log Curation Every 5 Minutes
The coordinator agent reviews what every background agent wrote, keeps what matters, deletes noise, and surfaces any errors to the flags queue for your attention. Your log history stays readable instead of piling up with transient status pings.
Pause-Aware Throughout
One pause command stops every background agent and scheduled job at the next tick. Resume restarts everything. The chat agent keeps working when paused so you can review and adjust.
Minimal Onboarding
First setup asks for your domain, one target market description, and your brand voice. The system starts building in under 15 minutes. Additional details (schema preferences, content themes, CTA targets) can be added on demand.
Natural-Language Commands
"Build a pillar page about [topic]," "research keywords for [market]," "show me how the [page] is doing," "deploy the updated template," "pause the system" all work as plain requests to the chat agent. It runs the right tool and shows you the result.
Pending Review Commands
"Show pending suggestions" lists everything the learning agent queued for your review. "Approve [id]" or "reject [id]" handles each one. Each pending item shows the category, the proposed change, the evidence, and the file paths involved.
Page Inspection
"List pages on [domain]" and "show me the [page]" pull the live page content for review without leaving the chat. Useful for sanity-checking a deployment or auditing how a specific page looks right now.
Settings By Conversation
Change brand voice, target market, content themes, cleanup window, anything in your config by telling the chat agent. It edits the right file with the right format and confirms the change.
Pause And Resume
"Pause the system" stops every background agent and scheduled job until you resume. Useful for content freezes, brand-voice reviews, or just when you want hands-on control for a few hours.
Four rules the learning agent and coding agent cannot override. Every page build and every content suggestion is checked against them.
No Keyword Stuffing
The coding agent is scored on readability and natural phrasing, not keyword density. Pages that over-optimize for a target keyword are rejected in the review step and rewritten to read the way a real person would write.
No Thin Content
Every page is built with enough depth to actually answer the search intent. A 200-word page for a commercial-intent keyword is a failure, the reviewer agent catches and rejects those before they ship.
Schema Accuracy
Schema markup only claims what the page actually contains. No fake review counts, no invented product data, no mismatched FAQ questions. Google penalties from schema abuse are avoided by refusing to generate abusive schema in the first place.
Brand Voice Consistency
Every page respects the brand voice you configure. The reviewer agent rejects pages that drift into off-brand territory before they ship, so the site reads like one voice even across hundreds of pages.
What Is Actually Running
The system runs as a set of background agents on your server, each on its own timer. All of them respect the system-wide pause flag and skip cycles silently when paused.
Master agent handles the chat interface (5-second tick). This is the one you talk to.
Coding agent runs every 15 minutes through a multi-step pipeline: plan, build, review, fix. Builds article pages, product pages, landing pages, pillar pages, and sub-pages.
Research agent runs every 15 minutes through a multi-step pipeline: explore keywords, classify search intent, identify content gaps, synthesize page proposals for the coding agent.
Learning agent runs every 15 minutes through a three-step cycle: creative ideas grounded in analytics data, skeptical review against safety rules, execute (auto-implement what is safe, queue everything else as suggestions for your approval).
Coordinator ticks every 5 minutes: curates background agent logs, surfaces stale pending items, extracts patterns from outcomes for the learning agent.
Scheduler handles hourly GA4 and GSC refreshes, daily maintenance, and any custom daily cleanup scripts you add.
All data stays on your deployment. Your domain, your pages, your search data, your learning outcomes. Nothing is shared with any external service the system itself does not call (the Google APIs you set up, the Gemini CLI for image generation).