GEO & AI Search Training for your in-house team

Build real, implementable AI visibility audit skills from technical foundations and brand clarity to evidence evaluation, AI answer testing, and competitive analysis. Learn how to connect findings across these areas and turn them into prioritized recommendations your team can act on.

Read the GEO framework
Training curriculum

What your team will work through

Discovery, retrieval & content selection

  1. Module 01: How generative search actually works

    Topics
    Traditional ranking versus generated answers; crawling, indexing, retrieval, reranking, synthesis, citations, query fan-out, freshness, personalization, and platform differences
    Practical exercise
    Take one AI-generated answer and map the likely journey from user prompt to retrieved sources and final response

  2. Module 02: GEO, SEO & AEO: what changes

    Topics
    What remains conventional SEO; what changes when the output is a synthesized answer; document-level visibility versus passage-level selection; why rankings, citations, mentions, and recommendations are different outcomes
    Practical exercise
    Classify 20 common “GEO tactics” as SEO fundamentals, GEO-specific work, platform controls, measurement, or unsupported claims

  3. Module 03: Technical eligibility and crawler controls

    Topics
    Crawlability, indexability, rendering, canonicals, redirects, noindex, sitemaps, IndexNow, JavaScript, server logs, and AI crawler policies; differences between search crawlers, training crawlers, and user agents
    Practical exercise
    Audit a website and produce an access matrix for Google, Bing, OpenAI, Perplexity, and other relevant systems

  4. Module 04: Prompt universe and demand modelling

    Topics
    Informational, commercial, comparison, recommendation, troubleshooting, local, branded, and unbranded prompts; prompt paraphrases; audience stages; query fan-out; topic decomposition
    Practical exercise
    Build a benchmark of 40–60 prompts grouped by business intent, audience, topic, and expected answer format

  5. Module 05: Retrieval and source-selection analysis

    Topics
    Why being indexed does not guarantee retrieval; relevance, topical coverage, source quality, corroboration, freshness, information gain, and suitability for a particular answer
    Practical exercise
    Compare cited and uncited pages for the same topic and document the likely source-selection gaps

  6. Module 06: Passage-level content design

    Topics
    Self-contained sections, explicit subject naming, direct answers, clear headings, definitions, comparisons, tables, evidence, examples, data provenance, and avoiding pronoun or context ambiguity
    Practical exercise
    Rewrite three sections from an existing page so each can be understood and quoted independently

Authority, measurement & experimentation

  1. Module 07: Entity clarity and factual consistency

    Topics
    How brands, products, people, services, locations, and relationships are described; inconsistent naming; ambiguous claims; ownership; author information; source provenance; structured data as reinforcement rather than proof
    Practical exercise
    Create an entity map and identify contradictions or missing relationships across the website and important external profiles

  2. Module 08: Off-site evidence and authority

    Topics
    Digital PR, independent coverage, expert contributions, reviews, industry references, original research, partnerships, communities, and authoritative third-party corroboration
    Practical exercise
    Build an evidence-gap map showing which important brand claims are supported only by the company itself

  3. Module 09: Content and source ecosystem strategy

    Topics
    Choosing appropriate page types; canonical source pages; product documentation; comparison pages; research assets; editorial content; help centres; feeds; databases; and maintaining freshness
    Practical exercise
    Design a topic-to-source architecture showing which page should become the primary source for each important claim or question

  4. Module 10: AI visibility measurement

    Topics
    Mention rate, citation rate, citation share, recommendation inclusion, answer accuracy, sentiment, source URL distribution, prompt coverage, referral traffic, assisted conversions, branded demand, and business impact
    Practical exercise
    Design a GEO scorecard that separates visibility metrics from commercial outcomes

  5. Module 11: Testing methodology

    Topics
    Baselines, repeated observations, prompt sampling, model and version changes, location, language, temporal volatility, control pages, treatment pages, holdout prompts, and avoiding conclusions from one screenshot
    Practical exercise
    Write an experiment plan for one page change, including hypothesis, treatment, controls, success criteria, and limitations

  6. Module 12: Operating model and governance

    Topics
    Responsibilities across SEO, content, PR, brand, analytics, product, engineering, legal, and customer experience; reporting cadence; claim verification; change logs; prioritisation
    Practical exercise
    Build a RACI and monthly GEO workflow
Training outcomes

What your team will be able to do

  • Explain how generative search systems discover, retrieve, select, synthesize, and cite information.
  • Separate established SEO fundamentals from genuinely new AI-search considerations.
  • Build a representative prompt and topic benchmark.
  • Diagnose technical, content, entity, authority, and source-selection problems.
  • Improve pages without turning them into robotic “AI-optimized” content.
  • Measure AI visibility beyond isolated screenshots and vanity citation counts.
Training formats

Match the training format to your team’s work

One workshop

Best for
One clear problem the team needs to solve together.
How it works
Work through live examples, agree on a shared method, and leave ready to use it again.

Two-day intensive

Best for
A team that needs time for shared foundations, hands-on application, and feedback.
How it works
Build a shared foundation, then review the team’s website, evidence, and prompts with direct feedback.

Short series

Best for
A team that needs time to apply the work between sessions.
How it works
Use the method in real work, then return to review decisions, results, and next steps.
Before we plan it

Questions teams ask before training

Who is this training for?

It is designed for in-house and corporate teams that need SEO, content, brand, PR, analytics, product, and related functions to use a shared AI Search method.

How is the training tailored to our team?

A short review before the session identifies where the team needs the most help. The agenda then gives more time to those areas instead of treating every topic equally.

What will we work on during the training?

Exercises use your website and current work. Depending on the priorities, your team may map an AI answer’s source journey, audit crawler access, build a prompt benchmark or GEO scorecard, rewrite page sections, or plan an experiment.

Which training format is right for our team?

Choose one workshop for one clear problem, the two-day intensive when the team needs shared foundations, hands-on application, and feedback, or a short series when the team needs time to apply the method between sessions.

What should we prepare before the session?

Bring representative pages, official brand facts, priority claims and supporting evidence, real search queries, relevant alternatives, and your current review workflow.

Can the training guarantee AI citations or recommendations?

No. Each platform decides which sources and brands appear. The training gives your team a repeatable way to improve what it can control and record what AI systems actually show.

Give your team a repeatable AI Search method