Core Engine // 03
Search & Presence
SEO and AEO from the same pipeline. Optimized for Google and for the AI systems replacing it.
A dual-layer content engine that identifies search opportunities from 367K daily GSC rows and monitors AI citation presence.
Content engine playground
Content Engine Playground
How it works
Signal extraction
Real search console data — updated daily — is analyzed to find topics where the site has search visibility but is not converting it into traffic. Real demand, not estimated volume.
Competitive analysis
For each opportunity, the engine fetches the top-ranking pages, embeds their content into Qdrant, and identifies what structure and angle is winning — and what is missing.
Dual-LLM generation
A writer model (gemma3:12b) drafts the full article. An auditor model (llama3.2:3b) reviews it against quality standards. If it fails, the writer rewrites. Up to 3 cycles before the draft is accepted.
AI answer optimization
A separate agent structures content for AI answer engines — ChatGPT, Perplexity, Claude. The format is different from traditional SEO: direct answers, clear sourcing, machine-readable context files.
Presence monitoring
The engine actively queries AI systems with 10 strategic questions and checks whether the site appears in the response. AI citation is tracked as a metric, not left to chance.
Capabilities
Data-driven opportunities
Every content decision starts with real search data. Topics with existing visibility but low traffic are the highest-signal opportunities the engine pursues.
Competitive content analysis
Before writing anything, the engine reads and embeds what is already ranking. It knows the gap before it fills it.
Writer + auditor loop
Two models work in sequence: one writes, one audits. The auditor applies editorial standards and sends failing drafts back for revision. Quality is enforced, not assumed.
AI answer formatting
Content is structured to be cited by AI systems — not just indexed by search engines. Direct answers, clear structure, and machine-readable context via llms.txt.
Active presence monitoring
The engine checks AI-generated answers for site citations across 10 strategic queries. Tracked weekly — not rankings, actual appearances in AI responses.
Freshness tracking
Published content is monitored for traffic decay. The engine flags pages that need updating before they drop — targeted refresh, not full rewrites.
What it solves
Search is splitting in two. There is Google — and there are the AI systems that millions of people now ask instead of searching. Most content strategies are built for one of them. This engine covers both, from the same data pipeline, running every day. Any industry. Any language. Any content type.
Case Studies
ecommerce
Medición del Impacto de Contenido SEO en Revenue Attribution: De GSC a Closed-Won en Ecommerce
Este estudio investiga la correlación entre esfuerzos de contenido optimizado pa...
5% — Aumento de Revenue atribuido a Keywords Mejoradas
education
Optimizacion Semantica de Corpus para Citacion en Modelos de Lenguaje: Metodologia AEO para Educación
El presente estudio investiga la optimización de corpus de conocimiento para mej...
4.2/5 — Citation Snippet Quality Score
healthcare
Aceleración de Content Velocity en Healthcare: Generación Asistida con Calidad Técnica y Profundidad Editorial
El presente estudio analiza la problemática de la baja velocidad de creación de ...
3.5 artículos/semana — Content Velocity
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