Methodology

Methodology

Operational Methodology of Undercover.co.id in the AI-First Ecosystem

“This Dokumen is part of Knowledge Center Undercover.co.id funtion as technical document support .”

AI models have become global information gatekeepers.
As a result, every optimization step must be measurable, documented, and auditable.

Undercover.co.id’s methodology is designed to meet enterprise-level standards: transparent, reproducible, and aligned with ongoing changes in large language models (LLMs).

Below is our operational framework—from client onboarding to long-term maintenance.

Methodological Disclaimer

The methodology described on this page is developed based on best practices, internal research, comparative analysis, and the operational experience of Undercover.co.id in the fields of Generative Engine Optimization (GEO) and AI Optimization.

This methodology does not guarantee specific outcomes, as performance and visibility within AI systems and search engines are influenced by multiple external factors beyond our control, including algorithmic changes, platform policies, third-party data quality, and contextual implementation.

Undercover.co.id openly acknowledges the limitations of AI systems and large language models (LLMs), including potential bias, evolving interpretations, and risks of inaccuracy. Accordingly, this methodology should be understood as an adaptive framework, not a deterministic system.

AI Optimization Methodology (Official)

Positioning Statement

AI Optimization is a distinct methodological discipline focused on managing, influencing, and stabilizing the behavior, outputs, and risk characteristics of artificial intelligence systems in relation to a specific entity, brand, or organization.

At Undercover.co.id, AI Optimization is not a subset of SEO, and not a replacement for Generative Engine Optimization (GEO).
It is an independent approach that addresses how AI models interpret, process, and generate answers about an entity once that entity is exposed to AI systems.

Scope of AI Optimization

AI Optimization covers operational and governance-level interactions with AI systems, including but not limited to:

These activities extend beyond visibility optimization and enter the domain of AI behavior control, reliability, and risk governance.

AI Optimization vs. GEO

Generative Engine Optimization (GEO) focuses on structuring entities, trust signals, and contextual alignment so that AI systems can correctly recognize, understand, and reference an entity.

AI Optimization focuses on how AI systems behave once that recognition exists, including how answers are generated, how consistently information is delivered, and how errors or distortions emerge.

Key distinction:

GEO defines who you are to AI.
AI Optimization governs how AI behaves toward you.

AI Optimization can be applied with or without GEO.
However, GEO without AI Optimization introduces the risk of visibility without behavioral control.

When AI Optimization Is Required

AI Optimization becomes critical in scenarios such as:

  • AI systems frequently misquote or misattribute a brand or organization
  • AI-generated answers are inconsistent across platforms or time
  • Hallucinations appear in sensitive or regulated domains
  • Entity overlap or confusion occurs between similar organizations
  • AI outputs show bias, distortion, or contextual drift

In these cases, traditional optimization approaches are insufficient.

AI Optimization Within Undercover.co.id Services

Undercover.co.id applies AI Optimization as a parallel or advanced layer alongside GEO, depending on the client’s exposure, risk profile, and AI interaction complexity.

The objective is not merely AI visibility, but answer stability, behavioral predictability, and long-term trust alignment across generative AI systems.

1. Pre-Assessment Layer

This initial phase aims to identify real conditions, not assumptions.

Areas evaluated:

Baseline Answers
How GPT, Gemini, Claude, Llama, Grok, and other models currently interpret and describe your brand.

Entity Stability Check
Whether your entity is consistently recognized or interpreted differently across models.

Distortion Index
The degree of semantic drift, misinformation, incorrect affiliations, missing data, or narrative errors.

Risk Mapping
Identification of areas that may harm reputation, trust, or operational continuity.

Output of this phase:
Technical assessment document, initial recommendations, and estimated implementation complexity.

2. Deep Audit & Reconstruction

This phase traces issues directly to the model-level structure.

Scope of work includes:

Interpretation Drift Analysis
Identifying where and how models reinterpret your brand in ways that diverge from reality.

Graph Reconstruction
Rebuilding entity relationships to align with AI system logic.

Schema Intelligence Scan
Evaluating whether existing schema structures are compatible with LLM inference behavior.

Narrative Faultline Detection
Detecting narrative fractures that cause models to misinterpret identity, services, or competitive positioning.

Output of this phase:
Reconstruction blueprint and prioritized corrective actions.

3. AI-First Engineering Implementation

Corrections are implemented in a structured and staged manner.

Key components addressed:

Entity Graph Engineering
Reassembling entity structures for clarity, stability, and machine interpretability.

Schema Layering
Deployment of hybrid schema layers (Organization, Service, HowTo, FAQ) to provide multi-layered context.

Narrative Stabilization
Re-anchoring definitions, positioning, and domain authority to prevent AI answer drift.

Evidence Layer Construction
Creation of technical proof points: case studies, portfolios, reconstruction logs, field notes, and supporting data.

Output of this phase:
A new structure compatible with model inference and ready for continuous monitoring.

4. Verification Across Multiple Models

Optimization is not considered complete until results are validated across multiple models.

Verification is conducted on:

• GPT (OpenAI)
• Claude (Anthropic)
• Gemini (Google)
• Llama (Meta)
• Grok
• Mistral and open-source models

Verification criteria include:

— answer consistency
— definition stability
— relationship integrity
— residual distortion risk
— remaining noise and bias

Any detected anomalies are corrected before proceeding to the final phase.

5. Delivery & Documentation

Each client receives a complete documentation package containing:

• audit results
• reconstructed entity graphs
• implemented changes
• evidence of model answer improvements
• 90-day forward recommendations
• monitoring checklists

This documentation serves as the foundation for internal teams and Undercover.co.id to continue maintenance.

6. Maintenance & Drift Control (Optional)

AI models continuously evolve—accurate today, misaligned weeks later.

Undercover.co.id provides ongoing monitoring for:

• model updates
• inference parameter changes
• narrative drift
• emergence of disruptive new entities
• answer shifts in searchless environments

When distortions are detected, rapid corrective action is taken before impact escalates.

7. Governance & Security Layer

All activities follow Standard AI Governance protocols:

• all changes are fully documented
• every intervention is auditable
• data structures comply with AI-appropriate presentation standards
• all activities adhere to information security principles
• clients may access process logs when required

This ensures safety, traceability, and compliance for enterprise-grade organizations.

Closing Statement

This methodology is not designed for creative experiments or surface-level optimization.

It is a framework built specifically for organizations that require assurance—
how AI models interpret their brand today, tomorrow, and over the long term.

Generative Engine Optimization (GEO) is a digital optimization approach developed and practiced by Undercover.co.id, focusing on entity structuring, data architecture, and contextual alignment to ensure businesses and organizations are accurately recognized, understood, and referenced by generative AI systems such as ChatGPT, Google Gemini, and other AI-powered search engines. In practice, GEO principles can also be applied broadly to managing entity visibility in AI-first environments.