3+
Years researching AI strategy & policy
19+
Articles published on EverydayOnAI (2025-2026)
2
Major content clusters: AI SEO Hub & AI Governance Hub
60+
Primary sources cited across both clusters
8
AI platforms tracked (ChatGPT, Perplexity, Gemini, Claude, Copilot, AI Overviews, AI Mode, Siri)
2
Languages covered: English & Bahasa Indonesia
About Dispa
Dispa is an independent AI researcher and the founder of EverydayOnAI.com, a publication covering two intersecting areas of the AI landscape: AI search optimization (GEO, AEO, LLMO, and AI SEO) and AI governance (EU AI Act, NIST AI RMF, ISO 42001, enterprise compliance, and emerging AI policy globally).
Writing under the name “The AI Buff” since 2023, Dispa’s approach is grounded in primary source research — academic papers, regulatory texts, and named industry benchmarks — rather than repurposed aggregator content. Every article on EverydayOnAI cites sources inline with organization name and year, and distinguishes clearly between documented data and editorial analysis.
Based in Jakarta, Indonesia, Dispa brings a Southeast Asian perspective to topics often dominated by US and European voices. This includes coverage of AI governance through the lens of emerging-market adoption, Indonesian-language content for local audiences, and analysis of how global AI regulation (EU AI Act, Colorado AI Act, Singapore IMDA framework) affects organizations operating in the Asia-Pacific region.
Before founding EverydayOnAI, Dispa built software projects localized for the Indonesian market — including a React/TypeScript football management game with regional player data and an Indonesian personal finance application (MoneyFlow) integrating Rupiah currency, local banks, and Indonesian e-wallets. This technical background directly informs EverydayOnAI’s coverage of how AI tools are built, deployed, and regulated — not just how they are marketed.
Areas of Expertise
EverydayOnAI covers two distinct but related clusters. The AI SEO Hub addresses how to build visibility on AI-powered search platforms. The AI Governance Hub addresses how organizations should manage, risk-assess, and comply with regulation around AI systems they build or use.
🔍 GEO — Generative Engine Optimization
Structuring content for citation inside AI-generated answers — ChatGPT, Perplexity, Google AI Overviews. Research grounded in the Princeton/KDD 2024 academic study and ongoing industry benchmarks (ConvertMate, Ahrefs, Semrush).
🔍 AEO — Answer Engine Optimization
Winning featured snippets (paragraph 40-60w, list 5-8 items, table 3-4 columns), People Also Ask, voice search, and AI answer boxes. Includes query fan-out mapping, PAA chain research, and snippet-format matching.
🔍 LLMO — LLM Optimization
Building brand entity clarity for AI model representation — Person and Organization schema, consistent entity signals, and third-party brand mention strategy for long-term LLM brand recall.
🔍 Schema Markup & Structured Data
Practical implementation of Article, FAQPage, Speakable, Person, Organization, HowTo, and Speakable schema — with current data on what each schema type actually produces for AI citation versus traditional rich results.
⚖️ EU AI Act
Risk classification (unacceptable, high, limited, minimal), compliance timelines, documentation requirements, conformity assessment obligations, and practical guidance for organizations building or deploying AI systems in the EU.
⚖️ AI Governance Frameworks
NIST AI Risk Management Framework (AI RMF), ISO/IEC 42001:2023, EU AI Act, Singapore IMDA framework, Colorado AI Act, and how these frameworks compare across seven dimensions for enterprise compliance planning.
⚖️ Enterprise AI Risk
Shadow AI compliance risk, AI impact assessments, bias auditing, documentation requirements, and the organizational governance structures (AI governance committees, CIO accountability) that regulatory frameworks increasingly require.
⚖️ AI Policy & Emerging Regulation
Comparative analysis of global AI regulation — EU vs US AI policy divergence, the Colorado AI Act as a US state-level precedent, and Southeast Asian regulatory approaches relevant to Indonesian and broader ASEAN markets.
Published Articles on EverydayOnAI
⚖️ AI Governance Hub — Live Articles
Pillar — AI Governance
AI Governance in 2026: Complete Guide
Spoke — AI Governance
What Is AI Governance? Definition, Importance & Core Principles
Spoke — AI Governance
The 5 Core Pillars of AI Governance
Spoke — AI Governance
7 AI Governance Frameworks Compared: NIST, ISO 42001, EU AI Act & More
Spoke — EU AI Act
EU AI Act Explained: Risk Categories & Prohibited AI Systems
Spoke — EU AI Act
EU AI Act Compliance Guide: What Businesses Need to Do Now
Spoke — EU AI Act
How to Classify Your AI System Under the EU AI Act
Spoke — EU AI Act
EU AI Act Documentation Requirements: Complete Checklist
Spoke — AI Policy
EU AI Act vs US AI Policy: Key Differences Explained
Spoke — Enterprise AI Risk
Shadow AI: The Silent Compliance Risk Your IT Team Doesn’t See
Spoke — AI Policy
Colorado AI Act Compliance Guide: What It Means for US Businesses
🔍 AI SEO Hub — Live & In Production
Pillar — AI SEO
What is AI SEO? The Complete Guide to GEO, AEO & LLMO (2026)
Sub-pillar — GEO
GEO Complete Guide: How to Get Cited by ChatGPT, Perplexity & Google AI
Sub-pillar — AEO
What is AEO? The Complete Answer Engine Optimization Guide (2026)
Spoke — Comparison
GEO vs AEO: Key Differences Explained (2026 Decision Framework)
Spoke — AEO
AEO vs SEO: What Changes and What Stays (2026)
Spoke — AEO
AEO Keyword Research: Finding Answer-Intent Queries (2026 Guide)
Spoke — AEO
How to Write for Featured Snippets & Voice Search (2026 Guide)
Editorial Standards & E-E-A-T Commitment
✓ How EverydayOnAI Maintains Content Quality
- Named primary sources only. Every statistic is cited inline — [Organization] [finding] ([Source, Year]). No “studies show” without a named source. Aggregator blog statistics are traced to their original research before use.
- No hallucination policy. Where the original primary source cannot be identified and independently verified, the statistic is not published. This applies to both the AI SEO Hub and the AI Governance Hub — including EU AI Act regulatory texts, which are cited from the Official Journal of the European Union directly.
- Opinion clearly labeled. Editorial opinion appears in clearly marked “According to EverydayOnAI” boxes in every article — visually and structurally separated from cited research. Opinion is never presented as data.
- Quarterly freshness cycle. All articles are reviewed quarterly. Statistics older than 12 months are updated or flagged. The visible “Last Reviewed” date in each article reflects when content was actively verified — not just when it was last touched.
- Corrections policy. When a cited figure is found to be incorrect, the article is updated with a visible revision note. EverydayOnAI does not silently edit factual errors.
- Self-compliance. EverydayOnAI practices the strategies it covers. Articles on AI SEO use FAQPage, Speakable, and Article schema. This author page uses full Person entity schema. The AI Governance articles cite the actual regulatory frameworks they analyze.
- Regulatory coverage verified against primary texts. EU AI Act content is verified against Regulation (EU) 2024/1689 (the official regulation text). NIST AI RMF content is verified against the published NIST AI 100-1 document. No AI Governance claims are based solely on third-party summaries.
Editorial Independence & Advertising Policy
EverydayOnAI is supported by display advertising (Google AdSense) and may use affiliate links where relevant. The following policies apply without exception:
- No sponsored articles. Advertisers do not influence editorial content, article topics, or tool recommendations. Display ads are served programmatically; they do not reflect editorial endorsements.
- Affiliate links disclosed. Where affiliate links appear (e.g., links to tools like Semrush, AlsoAsked, or AnswerThePublic), they are disclosed with a visible note at the point of mention. Affiliate relationships do not affect which tools are recommended or how they are reviewed.
- No pay-to-play coverage. Tools and resources featured in EverydayOnAI articles are selected based on research utility and documented performance — not on commercial relationships.
- No advertiser access to drafts. Advertisers do not preview, approve, or request changes to any article before or after publication.
💬 Why This Matters for Both Readers and Advertisers
Google’s AdSense policies and Search Quality Evaluator Guidelines both require demonstrable E-E-A-T — Experience, Expertise, Authoritativeness, and Trustworthiness — as a condition for sustained ad revenue and search visibility. The editorial standards above are not just ethical commitments; they are the operational foundation for EverydayOnAI’s long-term viability as a publication. A site that manufactures credibility for short-term traffic loses both readers and advertisers when the foundation collapses. The goal is to be the publication that content teams, AI researchers, compliance officers, and business owners actually recommend to each other — which only happens if the content is reliable enough to stake a recommendation on.
How Articles Are Researched and Fact-Checked
For AI SEO Hub articles: Statistics on AI citation rates, featured snippet CTR, voice search behavior, and platform-specific citation patterns are verified against named primary sources — BrightEdge, Ahrefs, Semrush, ConvertMate, SparkToro/Datos, Bain & Company, Pew Research Center, and the Princeton/KDD 2024 academic study. AI platform behavior claims are validated through direct testing in ChatGPT Search, Perplexity AI, Google AI Overviews, and Google AI Mode before publication.
For AI Governance Hub articles: Regulatory content is verified against the official legal texts — Regulation (EU) 2024/1689 (EU AI Act), NIST AI 100-1 (NIST AI RMF), ISO/IEC 42001:2023, and Colorado SB 24-205. Compliance timelines and enforcement dates are cross-referenced against the European Parliament’s official publications. No governance claim is based solely on a third-party summary of a regulation.
For tool pricing and feature coverage: Pricing information is verified at each vendor’s official pricing page at time of publication, with a note that pricing changes frequently and readers should confirm before subscribing. Tool features are verified through direct use where possible.
For case studies: Every case study cited in EverydayOnAI articles names the organization, the methodology, the result, the timeframe, and the source publication. Anonymous “a client we worked with” case studies are not used. Results are presented with context about what constitutes a typical versus exceptional outcome.
Contact & Media Inquiries
For editorial questions, corrections, reader feedback, or media inquiries related to EverydayOnAI content:
- Email: [REPLACE-WITH-EMAIL]
- LinkedIn: linkedin.com/in/[REPLACE-WITH-LINKEDIN]
- Response time: Within 2-3 business days
- Corrections: Factual corrections are prioritized and addressed within 24 hours of verification
Page last updated: June 2026 ·
Author URL: https://everydayonai.com/about/dispa ·
Schema: Person + Organization (schema.org/Person, schema.org/Organization) ·
E-E-A-T signals: Expertise (knowsAbout, published articles), Experience (founded 2023, technical background), Authoritativeness (primary source citations, named methodology), Trustworthiness (corrections policy, editorial independence, AdSense compliance)