Methodology
The AISO Methodology - how we make your brand the answer AI gives.
AISO (AI Search Optimization) is a four-phase methodology - Audit, Foundation, Optimize, Monitor - that turns your site into the canonical source AI assistants quote when people ask about your category.
✓ SEO‑safe ✓ Entity‑first ✓ Built for assistants
Why AISO needs a methodology, not tactics
AI assistants do not rank pages. They assemble answers. That means tactical SEO plays - a meta-tag here, a backlink there - cannot produce reliable AI visibility on their own.
AISO works end-to-end: we model your entities, govern your structured data, ship AI-liftable answer modules, and monitor how each engine cites you. Every phase produces an auditable artifact so the advantage compounds instead of decaying with the next model update.
What "SEO-safe" means in AISO
- No thin duplicate pages
AI-liftable modules live inside your existing key URLs. - No canonical drift
Every optimization preserves existing rankings and internal linking equity. - No schema sprawl
A single entity graph, governed centrally, feeds every page and every assistant.
The four phases
From baseline to compounding advantage
Phase 1
Audit
We baseline your visibility across six AI engines (ChatGPT, Perplexity, Gemini, Copilot, Grok, DeepSeek) with 377+ monitored prompts. You see where assistants already cite you, where they cite competitors, and where the gaps are.
Phase 2
Foundation
We stabilise the entity graph, JSON-LD schema, internal linking and page structure so assistants can safely lift accurate answers without breaking your SEO. This is the phase that prevents hallucinations about your brand.
Phase 3
Optimize
We design AI-liftable answer modules on your highest-value URLs, targeting the prompts that actually drive revenue. Each module is designed to be quoted verbatim by assistants and to convert the visitor afterwards.
Phase 4
Monitor
We track inclusions, coverage and answer quality monthly with our proprietary LLM Monitor. Every cycle feeds the next optimization, so your AISO advantage compounds rather than decaying.
Auditable artifacts
Every phase ships something you can check
AISO Baseline Report
From Phase 1. Citation rates across six engines, prompt-level inclusion data, and the top ten gaps ranked by revenue impact.
Entity Graph Specification
From Phase 2. The canonical JSON-LD graph powering every page, with disambiguation rules and ID conventions documented.
AI-Liftable Module Library
From Phase 3. Reusable answer blocks (FAQ, comparison, methodology, definition) designed to be quoted by assistants verbatim.
Monthly LLM Monitor Dashboard
From Phase 4. Trend data on citations, answer quality scores, and screenshots of AI answers over time across all six engines.
FAQ
Questions about the AISO methodology
What is the AISO methodology?
AISO (AI Search Optimization) is a four-phase methodology - Audit, Foundation, Optimize, Monitor - that prepares your site for AI assistants. Each phase produces an auditable artifact so the work compounds rather than decays.
How is AISO different from GEO or AEO?
AISO is the umbrella term we use for AI Search Optimization across every assistant and answer engine. GEO, AEO and AI SEO describe narrower slices. AISO covers entity modelling, structured data governance, AI-liftable content and live citation monitoring in one coherent system.
How long does the AISO methodology take to produce results?
Foundation work is typically visible in AI answers within 4 to 8 weeks, depending on crawl frequency. Citation trends stabilise after three monitoring cycles. Each cycle makes the next optimization cheaper because your entity graph gets stronger.
Does AISO replace SEO?
No. AISO is SEO-safe by design. Every optimization preserves and usually improves your existing search rankings. The goal is a single content asset that ranks in Google and gets cited across the six AI engines we monitor.
Ready to run the AISO methodology on your site?
Book a short call and we’ll show you where assistants already mention you, where they don’t - and what phase 1 would uncover first.