AEO & GEO
Answer Engine Optimization Services
Get cited in ChatGPT, Perplexity and Google AI Overviews — not just ranked below them
AEO and GEO for B2B software and services companies selling into the US, UK, Canada, Australia, New Zealand, UAE and Singapore. Prompt-level audits, citation assets, and source-pool placement — measured against named competitors, reported monthly.
- Clients Worldwide
- 300+
- Projects Delivered
- 1,000+
- Rated on Clutch & GoodFirms
- 5/5
- Years Experience
- 13+
What is AEO?
Answer Engine Optimization (AEO) is the practice of getting a brand cited inside AI-generated answers — ChatGPT, Perplexity, Google AI Overviews, Claude and Copilot — rather than only ranking in the blue links below them. It works differently from SEO: AI answers are assembled from a small pool of retrieved sources, so the goal is entering that source pool. In practice that means earning placement in the ranked listicles and directories assistants retrieve, publishing extractable pages with named buyers and quotable figures, and structuring content so a model can lift a passage cleanly. Ortem Technologies delivers AEO for B2B software and services companies across the US, UK, Canada, Australia, New Zealand, UAE and Singapore.
A buyer used to type a keyword and pick from ten blue links. Increasingly they ask a question in full sentences — “who builds fleet management SaaS,” “best AEO agency for B2B SaaS” — and read a single generated answer that names three or four companies. If you are not one of the names, the ten blue links underneath rarely get the click.
AI answers are assembled, not written
An assistant answering a vendor question does not reason about your company from memory. It retrieves a small pool of pages — usually the ones already ranking — and assembles an answer from what they say. This has a hard consequence that most SEO work ignores: if you are not in the retrieved pool, no amount of on-page optimisation puts you in the answer. The first question in any AEO engagement is not “how do we rank” but “what is this category’s answer actually being built from.”
For vendor questions, that pool is mostly other people’s rankings
In our own September 2026 audit of B2B software buying prompts, roughly 85% of the top-ten results were not vendor service pages at all. They were “Top 10 X Companies in 2026” listicles, directories and comparison posts — frequently published by a competing agency that ranks itself first. That is the uncomfortable shape of the market: the page that answers a buyer’s question about your category is usually written by someone else, and being excellent at your own website does not get you into it.
So the work splits in two. On pages you own, the job is extractability — a named buyer, a quotable figure, a stated position, and clear scope boundaries, structured so a model can lift a clean attributable passage. On pages you do not own, the job is placement — getting into the rankings and directories that already occupy the answer, and earning the editorial citations that come from publishing numbers other people need.
What we will not tell you
We will not promise a citation in ChatGPT. AI answers are non-deterministic, they shift with model updates, and no agency controls retrieval or generation. We will not sell you a dashboard as a strategy — prompt-tracking tools are useful instrumentation and terrible deliverables. And we do not buy links; paid networks build a profile you pay someone to disavow later, and they do nothing for citation because assistants retrieve pages that rank on merit.
Want the baseline first? Request an AI visibility audit → Tell us your category and your target markets; we will run the prompt set and show you who is being named instead of you.
Capabilities
What AEO Actually Involves
Prompt-Level Visibility Audit
We map the buying questions your customers actually ask, run them live in every target market, and record whether you appear, who appears instead, and which pages the answer is built from.
Citation Asset Development
Cost breakdowns, benchmarks and head-to-head comparisons written to be quoted rather than to rank. A publisher needs a number they can lift with attribution — that is what earns the citation.
Source-Pool Placement
AI answers for vendor questions are assembled from third-party rankings and directories. We identify the ones that actually rank in your category and run the submission and outreach programme to get you inside them.
Extractability Rewrites
Passage-level restructuring of your service and product pages: a named buyer, a quotable figure, a stated position, and a clear scope boundary — the shape a model can lift cleanly.
Entity & Schema Layer
Organization, Service, FAQPage, Speakable and citation schema, plus consistent NAP and entity signals across the directories and knowledge sources models read. This is how a model knows what you are.
AI Crawler Access
robots.txt and llms.txt policy for GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot and Google-Extended, plus render checks that confirm your content exists in the HTML crawlers actually receive.
Method
How We Run an AEO Engagement
- 01
Prompt set definition
We build the question set with you — the real phrasing buyers use, not keyword strings — and agree the target markets and the competitors we will benchmark against.
- 02
Baseline measurement
Every prompt is run live in every market. We record your appearances, competitor appearances, AI Overview presence, and the exact pages each answer draws from.
- 03
Source-pool analysis
We separate the ranking set into the global layer that appears in every market and the local layer that changes per country, then rank each source by how reachable it actually is.
- 04
Fix and build
Schema and extractability work on pages you own, citation assets where the category rewards a quotable number, and a placement programme for the third-party sources worth entering.
- 05
Re-measure and report
The same prompt set, the same markets, on a fixed cadence — so movement is measured against a stable baseline rather than against a moving one.
From our own research
What a Prompt-Level Audit Surfaces
These figures come from Ortem’s own September 2026 audit of B2B software buying prompts across seven markets. They are market conditions, not client results — the point is what a prompt-level baseline makes visible before any work starts.
- Buyer prompts returning a Google AI Overview20/21
Ortem audit of 22 B2B software buying prompts, US market, September 2026
- Top-ten results that are third-party rankings, not vendor pages~85%
Same audit — the ranked set is dominated by listicles, often published by competing agencies
- Ranked positions reviewed across seven markets490
49 live result sets in US, UK, Canada, Australia, New Zealand, UAE and Singapore
- Markets where a local pack takes three of ten slots3 of 7
UAE, New Zealand and Singapore — those slots are won with a Business Profile, not content
Markets
Why Single-Market Audits Give the Wrong Answer
Run the same buying question in seven countries and each result set splits into two layers. A global layer of the same handful of listicles and directories appears almost everywhere. A local layer of country-domain incumbents — .co.uk, .com.au, .ae, .ca — changes completely at every border.
That distinction decides your strategy. Where a category has a thick local layer, you need a per-market plan and a credible local presence. Where it has none, one well-built page can compete in every market at once. We tell you which of those you are in before you spend anything, and in three of the seven markets we cover — UAE, New Zealand and Singapore — we also flag the local pack, because a verified Business Profile wins three of ten slots there and no amount of content will.
- Prompt set covering your real buying questions, per market
- Competitor citation map — who is named instead of you, and where
- Source-pool inventory: the exact pages your category is answered from
- Placement shortlist ranked by reachability, not by domain rating
- Schema and entity gap report with the JSON-LD to ship
- AI crawler access and render audit across GPTBot, ClaudeBot, PerplexityBot
- Extractability scoring on your top service and product pages
- Quarterly re-measurement against the same fixed baseline
Engagements
Ways to Work With Us
| Engagement | What you get | Timeline | From |
|---|---|---|---|
| AI Visibility Audit | Prompt-level audit across your buying questions and target markets, source-pool analysis, competitor citation map, and a prioritised fix list | 2–3 weeks | $1,500 |
| AEO Foundation | The audit plus implementation: entity and schema layer, extractable page rewrites, citation-asset build, and directory placement programme | 10–14 weeks | $9,000 |
| Ongoing AEO Retainer | Monthly prompt tracking, new citation assets, placement outreach, and quarterly source-pool reporting against named competitors | Monthly | $1,500/mo |
Every engagement starts with the audit, because without a baseline there is nothing to measure movement against. If the audit shows your category is not yet being answered by AI in any meaningful volume, we will tell you that and recommend you spend the budget elsewhere.
Coverage
Surfaces and Standards We Work Across
AI & ML
- Google AI Overviews
- ChatGPT Search
- Perplexity
- Microsoft Copilot
- Claude
- Google Gemini
Data
- JSON-LD / Schema.org
- llms.txt
- Speakable & FAQPage
- Knowledge Graph entities
Other
- Search Console
- GA4
FAQ
Frequently Asked Questions
Find Out Who Is Being Named Instead of You
Give us your category and your target markets. We will run your buying questions live, show you the competitors AI names in your place, and hand you the source pool your category is actually answered from.
Also see: Digital Marketing & SEO · LLM Integration · AI Agent Development
