Solution
Show Up in AI Answers
The on-site and off-site work that makes AI systems able to find, read, and correctly describe you.
The short answer
AI assistants increasingly shape a shortlist before a buyer clicks. No agency controls what those systems say, and Google's own guidance is that search fundamentals still apply to AI experiences. So we split the work into two halves we can actually influence. AEO is the on-site half: crawler access, indexability, clear factual answers, and evidence worth quoting. GEO is the off-site half: consistent public facts, corrected third-party claims, and earned authoritative mentions. Both are then observed against a fixed prompt set, with the raw outputs shown to you.
- AEO and GEO are our operating labels for on-site and off-site work, not official platform terminology
- The controllable levers are crawl access, indexability, factual consistency, source-worthiness, and authoritative mentions
- What we report is observed: the prompts we ran, the dates, and the outputs we saw
- Search fundamentals still do most of the work here, which is why we never sell AEO detached from SEO
Is this you?
Check the symptoms.
- You searched for your own category in ChatGPT and were not mentioned
- Your competitors show up in AI Overviews and you don't
- When models do describe your product or pricing, the details are wrong or years out of date
- Your pages only render after JavaScript runs, or AI crawlers are blocked in robots.txt without a deliberate decision
- Your pricing, product names, and positioning read differently on your site, your listings, and review profiles
- No one on your team can name three prompts your buyers ask AI
What we combine
Services in this solution.
Where this sits
How this program runs inside the system.
Step through the eight stages between market demand and qualified pipeline to see what each one contributes.
Stage 03
Search and AI discovery
SEO, AEO, and GEO run together so buyers and answer engines both find and describe you correctly.
Timeline
What the first six months look like.
01
Weeks 1 to 3
Baseline: crawler access and indexability, factual consistency across public sources, and verbatim capture of how assistants describe you against a prompt set built for your category.
02
Weeks 4 to 8
Fix access and rendering, restructure priority pages answer-first, publish structured data for machine clarity, and reconcile the facts across every place you are described. Optional experiments such as llms.txt are shipped and tracked as experiments.
03
Weeks 9 to 12
Off-site work: correcting outdated claims at the source, fixing listings and profiles, and earning disclosed coverage with original data. No undisclosed paid placements.
04
Month 4 onward
Monthly reporting of observed mentions and citations with raw outputs, including months where nothing moved. Prompt set expanded into adjacent buyer questions.
Recommended tier
Usually a Momentum engagement.
Momentum $15K / month, 6-month minimum
A meaningful AEO and GEO program lives in Momentum. Ignite gets the on-site foundation right but cannot fund the reporting cadence and off-site corpus work. Category Leadership expands both across product lines and geographies.
FAQ
Questions about this solution.
Is AEO real or hype?
The behaviour is real: buyers ask assistants for shortlists, and the sources those systems surface influence who gets considered. The hype is in the certainty some vendors sell. Google says its search fundamentals still apply and that no special AI schema is needed, so most of what works here is good, well-structured, well-sourced publishing plus crawler access. We treat AEO as our operating framework for doing that deliberately and measuring what we can observe.
What is the difference between AEO and GEO?
They are our internal split, not industry standards. AEO is the on-site half: access, indexability, answer-first structure, and evidence worth quoting. GEO is the off-site half: consistent public facts, corrected third-party claims, and earned mentions. We run them together because they share a prompt set and a content pipeline.
How do you measure this?
We run a fixed prompt set on a schedule and record what each assistant returned, with dates and raw outputs. That is observation, not a ranking system: outputs vary by session, personalisation, and model updates, so we report ranges and trends rather than precise scores. We also report the technical inputs we control, such as crawler access and indexability.
Does this work if we do not do SEO?
It works better with SEO, since LLMs pull from indexed content. We rarely do AEO or GEO alone; the disciplines share too much production.