Undercover.co.id provides modular and integrated services for organizations that need to improve how AI systems identify, explain, cite, compare, and recommend their business. The work begins with institutional understanding and buyer decisions, not with mass content production.
Core engagement routes
AI Visibility Audit
A structured baseline covering representative buyer queries, current answer patterns, source retrieval, entity accuracy, category fit, citation behavior, competitor visibility, failure states, and business risk. The audit produces a prioritized implementation roadmap rather than a screenshot collection.
Generative Engine Optimization
A cross-functional program that improves the probability that generative systems can retrieve and synthesize accurate information about the organization. It may include canonical pages, answer architecture, entity resolution, evidence paths, internal graph design, structured data, source alignment, and external authority routing.
Answer Engine Optimization
AEO improves the directness, completeness, and retrievability of answers to real buyer, customer, procurement, and stakeholder questions. It is especially useful when the existing website contains information but does not resolve the decision clearly.
AI Optimization
AI Optimization is the broader institutional discipline. It connects business identity, knowledge, evidence, relationships, governance, monitoring, and implementation across multiple AI surfaces rather than optimizing for one engine or one prompt.
Specialist implementation services
- Entity and knowledge architecture: organization, service, people, location, product, methodology, evidence, and relationship modeling.
- Schema optimization for AI: one coherent invisible JSON-LD graph aligned with visible content and real WordPress data.
- Knowledge graph optimization: contextual and reciprocal relationships between canonical pages, evidence, case studies, services, industries, and buyer questions.
- AI citation readiness: source clarity, claim boundaries, supporting records, and citation routes.
- AI visibility monitoring: repeatable observations with query, engine, date, mode, citation, source, confidence, and limitation metadata.
- AI reputation and misinformation response: source-of-truth repair, temporal clarification, identity continuity, and incident documentation.
How scope is determined
Scope depends on the organization’s decision risk, number of brands and entities, geographic coverage, language requirements, website condition, evidence maturity, regulated-industry obligations, and the number of buyer journeys that must be supported. A service list is therefore only the starting point.
Typical implementation sequence
- Business, institution, and buyer discovery
- Deep-intent query and decision-path mapping
- Knowledge and evidence inventory
- AI visibility baseline and gap analysis
- Canonical asset and relationship blueprint
- Implementation in controlled batches
- Rendered-source, schema, link, and evidence validation
- Observation, reporting, and governance