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 demo
Keyword
project management software for remote teams
GSC signals
Opportunity gap
Ranking on page 2 with high commercial intent. Top results lack pricing context and real implementation detail.
Top competitors
Engine action
Generate new article targeting this keyword with competitive differentiation
Competitive gaps identified
AEO optimization
Structured for AI answer engines: direct question in H1, specific ranges in first paragraph, clear section headers matching search intent.
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
education
Arquitectura de Contenido para Visibilidad en Motores de Respuesta IA: Caso Educación
El presente estudio analiza la evolución de la búsqueda en la industria educativ...
15% — AI Search Click-Through Rate
retail
Análisis de Brecha de Contenido Retail: Identificación de Vacíos de Alta Intención con Embeddings Semánticos
Este estudio analiza la brecha de contenido en el sector retail, enfocándose en ...
85% — Content Gap Identification Accuracy
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