Geogen Shatters the AI 'Black Box' with Real-Time Extraction of Hidden Fan-Out Queries
Geogen introduces a transformative transparency layer that captures and analyzes the hidden background search strings generated by LLMs.
BRUSSELS, BELGIUM, January 13, 2026 /EINPresswire.com/ -- As the digital landscape completes its pivot from traditional search engines to Generative AI interfaces, a critical visibility gap has emerged between the user’s intent and the AI’s response. Today, Geogen, a global leader in Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) technology, announced the launch of a pioneering feature that exposes the "Fan-Out Queries" generated by Large Language Models (LLMs), data previously locked within the encrypted network calls of the world’s most advanced AI agents.
The Hidden Architecture of AI Retrieval
When a user interacts with a conversational AI, such as ChatGPT, Perplexity, or Google Gemini, the interaction appears as a seamless dialogue. However, behind this interface lies a complex "agentic" workflow. Modern LLMs do not simply "know" the answer to real-time queries; they act as primary processors that determine if external data is required to fulfill a request.
When a model determines a search is necessary, it triggers a Function Call. It autonomously synthesizes a series of highly optimized, technical search queries designed to query web indexes. The Query Fan-Out is the true bridge between a user’s natural language prompt and the authoritative sources the AI eventually cites. Until now, these calls remained invisible, nested within the background network layer of the LLM’s execution environment.
Bridging the Transparency Gap
While the SEO industry has long relied on tracking visible Search Engine Results Pages (SERPs), the transition to Generative Search has rendered traditional tracking insufficient. Most GEO tool providers lack the infrastructure to intercept the JSON payloads and Control Layer data where these background search strings reside.
Geogen’s new update establishes a new industry standard by extracting these hidden strings in real-time. By providing this data, Geogen enables brands to move beyond guesswork and understand the precise logic the AI uses to source its recommendations.
"We are moving beyond the era of ‘black box’ search," says Mauro Settimo Carini, CTO of Geogen. "With this release, we are finally providing marketers and SEO professionals with the critical insights needed to understand how AI finds and filters recommendations. By exposing the specific queries the models generate in the background, we allow professionals to see exactly how their brand is being sourced, and why."
Turning Data into Discovery
Geogen’s latest feature provides a strategic advantage for enterprises navigating the "Generative-First" era:
• Query Synthesis Analysis: See how a conversational prompt (e.g., "What is the best CRM for a mid-sized law firm?") is translated by the AI into technical search strings.
• Recommendation Pathing: Understand the specific information "buckets" an AI prioritizes before it delivers a final recommendation.
• Alignment & Optimization: Ensure that brand content is optimized not just for keywords, but for the sophisticated, multi-step search logic of agentic AI.
The Future of GEO
In a world where AI models synthesize information rather than just providing a list of links, visibility is the new currency. If a brand’s digital footprint does not align with the background queries generated by an LLM, it effectively ceases to exist in the generative output. Geogen’s mission is to provide the technical depth required to ensure brands remain visible in this new, automated ecosystem.
About Geogen
Geogen is a premier GEO (Generative Engine Optimization) platform providing advanced analytics for the AI-driven search era. By decoding the technical interactions between Large Language Models and the web, Geogen empowers SEO professionals and enterprises to secure their place in the future of digital discovery.
Alessandro Benigni
Lumen AI Lab BV
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