Ecosystem

AIEO and GEO Solutions

Science. Business. Cognition.

The IB Research Lab, adhering to the Kūkan-Ha framework, has developed a set of three tools specifically designed for Generative Engine Optimization. Our objective is to eliminate synthetic noise and establish definitive semantic authority throughout the Large Language latent space.

Module 01 WordPress

GEO LLM Meta Plugin

Turn your WordPress into a high-priority source for any Large Language Model by fixing the crawl indexation to the right vector, declaring LLMs.txt, and optimizing metatags for a better token-efficient HTML output optimized for RAG injection.

Explore Engine ➜
Module 02 Chrome Ext

GEO LLM Meta Inspector

Analyze your competitor's Generative Engine Optimization and SEO Engine Optimization strategies to understand the semantic vector they are being indexed to and precisely how any website is prioritizing, chunking or clustering the llms.txt.

Explore Inspector ➜
Module 03 SaaS Platform

GEO LLM Meta Monitor

Consolidate on one dashboard how your Generative Engine Optimization deployment is indexing content with the appropriate semantic vectors, creating citations for your material, and showcasing your branding assets across any Large Language Model.

Explore SaaS ➜
Algorithmic Pathology vs. IBRL Vector

Solutions designed to optimize vectorial space

Patology: DOM intoxicated (Bloatware)

LLMs' lost in the middle

Recent research has decoded how language models ignore content placed in the middle of overloaded pages. The phenomenon known as "Lost in the Middle" becomes invisible due to excessive syntactic friction in the code.

IBRL Vectorial Solution

Vectorial Distance Optimization

Our plugin eliminates structural noise and generates a high-purity llms.txt file by intervening at the WordPress source engine. We reduce the energy cost of reading, ensuring that the model ingests the essence of your brand without latency.

Pathology: Cognitive blindness

Spamming Keyword saturation

The market is still using 2018-era SEO tactics. Brands are competing blindly without understanding how Artificial Intelligence is semantically grouping (Clustering) their rivals.

IBRL Module (Inspector)

Latent Reverse Engineering

Absolute visibility, achieved by using an extension, maps the competitor's latent space, revealing how they fragment their data for RAG. We uncovered the crack in their strategy to position their brand above by increasing vector density.

GEO Invisibility

Non Zero-Click Authority

Appearing on ChatGPT or Gemini today does not assure your presence tomorrow. The absence of telemetry results in companies losing their "Source of Truth" status with even the most minor modifications to the model's weights.

Vector IBRL: Module 03 (Monitor)

Long-Term AI Readiness

Long-term governance with an SaaS that simulates 24/7 ingestions by transformational agents. We validate whether your brand maintains its citation rank, consolidating algorithmic reputation against any mutations in the neural networks.

Protocolo de Laboratorio

Data Governance Ledger

Our ledger represents how the IB Research Lab's Trinity intercepts commercial oversaturation and distills it into mathematically pure knowledge. For us, any deployed code is not an ornament; it is our Kūkan-ha doctrine.

IBRL Terminal // Secure Access

>> Initiating IBRL Data Governance Protocol...

[INGEST] Market trend detected. Warning: Infobesity Level at 94%.

[PRUNE] Executing Kūkan-Ha algorithm. Stripping commercial noise & dark patterns.

... Extracting core semantic entities.

... Reducing DOM Entropy.

[VALIDATE] Cross-referencing extracted vectors with IBRL Usability Labs & Peer-reviewed Studies.

[STATUS] Syntethic noise rejected. Scientific signal successfully isolated.

"OUTPUT: Pure Semantic Vector ready for Zero-Latency RAG injection."

Infobesity antidote

The market reacts to trends.
We govern the data.

Our technological solutions are neither conceived by chance nor driven by commercial trends. We operate as a scientific research laboratory. Each module we develop is supported by a steadfast empirical methodology.

Empiric phase

Research and Study Cases

We observed the collapse and conducted a mathematical analysis of the shortcomings of corporations through documented case studies. We examined failed architectures to identify the causes of cognitive de-indexing.

Active Deployment

Interviews and Data Polling

We directly extract the truth through comprehensive surveys and individual interviews with technical leaders. We eliminate marketing distractions to accurately capture the genuine aspects of human behavior concerning AI interfaces.

Scenario Development

Usability and Cognitive Latency

Prior to deploying a GEO module, we conduct rigorous usability testing on the architecture by measuring cognitive latency and mental load. If either a human user or a model displays signs of fatigue during the evaluation, the solution is subsequently rejected.

"Research is our governance. Our Code is simply the vehicle through which we exercise our authority."