GetXEO vs Profound
Getting cited is a chain. Find the questions a buyer actually asks, build a connected set of pages rather than isolated posts, write every block so a model can lift it, make sure engines can fetch and parse the result, and measure what happens. Profound is very good at the last link. GetXEO is built to run all five, which is the whole comparison.
We publish this page and sell GetXEO, one of the two products compared here. Every Profound figure was read on that vendor's own pages on 27 August 2026.
In short
The questions this page answers, and the short answers.
- What is the main difference between GetXEO and Profound?
- Profound measures answer engines and meters agents to act on the findings. GetXEO runs the full chain: question simulation, blog mesh design, extraction craft on every page, a technical audit, and measurement across AEO, GEO and SEO.
- Why does AEO need SEO foundations rather than a separate tool?
- Because the technical and structural work that made a page rankable is the same work that makes it retrievable. Splitting them into two products leaves the page fixed for one and broken for the other.
- How does GetXEO decide which questions to target?
- By simulating how a buyer behaves while researching, then deriving the questions from that. It is a different artifact from a keyword list with question marks added or a prompt list typed by a marketer.
- Why does a blog mesh earn more AI citations than isolated posts?
- An interconnected set where every post supports the others, so brand and topical authority compound. A single post has to win alone, which is why isolated publishing underperforms in AI answers.
- Why does a technical audit matter for AI citations?
- Because content an engine cannot fetch, parse or understand is wasted whatever its quality. Delivery sits upstream of everything, and a blocked crawler makes the best page on the internet invisible.
The chain that produces a citation
Five links, and each one can break alone.
| Link | What it decides | Profound | GetXEO |
|---|---|---|---|
| The questions | What you write about | Prompt sets | Simulated behavior |
| The structure | Whether authority builds | Not published | A blog mesh |
| The craft | Whether a passage is liftable | Agent output | Every block |
| The delivery | Whether engines can use it | Bot traffic | 3 pillar audit |
| The number | Whether any of it worked | Deep sampling | AEO, GEO, SEO |
Profound is strongest on the final row, which is measurement. The four links above it are where this comparison is actually decided, because each one can fail on its own and take the others down with it.
How Profound works
Measure the answer, then meter what acts on it.
A new account begins with a suggested prompt set for its industry, which you can edit, switch off or extend with your own. The judgment about what deserves tracking therefore sits with the buyer.
Acting on a finding runs through Agents, where each run spends credits and a more complicated job spends more of them. An estimate appears before the run and the actual cost afterward.
Agent Analytics sits alongside it and reports AI sourced traffic across your domains. The overall shape is measurement, then recommendation, then capacity for the team to act on what it finds.
How GetXEO works
Run every link in the chain, then measure it.
GetXEO starts where AI visibility starts, which is the question. It models how a buyer behaves while researching, then draws the questions out of that. The question set splits into People Also Ask, related searches and conversational prompts, and queries are then curated per pillar and mapped back to the question they came from.
The output is a mesh rather than a stack. Blogs are produced as one connected set instead of separate posts, the visualizer maps that mesh alongside keyword clusters and competitor networks, and the calendar sequences refreshes, technical fixes and new posts across twelve weeks.
Every page is written to be lifted, which means the answer comes first, each section stands on its own, and the structure follows the question rather than a template. Content rules hold the brand voice as plain instructions every writer reads.
Then delivery and the number. Three audits check whether engines can fetch, read and understand the pages, and one score reports AEO, GEO and SEO together with model citation rates.
Why SEO foundations decide AI visibility
One system across three layers, not three tools.
Most of this category treats AI visibility as a problem separate from search, and it is not separate at all. Clean structure, clear headings, entity markup and a page a parser can read are exactly what made a page rank, and they are also what allows a model to lift a passage out of it.
Profound is built around answer engines, and the classic Google ranking page is not among its published rows. A brand can hold its rankings while quietly losing ground inside AI answers, and only one of those two movements becomes visible.
A model weighs what it already holds about a brand against what it finds. Connected, well built evidence moves the first half. It is the same evidence a search engine rewards.
What Profound does well
Four real strengths, stated straight and without hedging.
- Depth of sampling. It runs about ninety checks per question each month on its main paid plan, and the same prompt can give different answers on different runs, so depth is what makes a number worth trusting
- Bot traffic. Agent Analytics covers unlimited domains on every plan, and plugs into Cloudflare, AWS, Akamai, Fastly, Google Analytics, Netlify, Vercel and WordPress
- Demand data. Prompt Volumes shows what people are actually asking answer engines, which is a useful input and one that few rivals publish at all
- Scale. Ninety six million dollars raised, with SOC 2 and single sign on for larger buyers. That matters to a procurement team
Anyone weighing the two products should take those four strengths seriously, because they are the reason Profound wins the evaluations it wins.
What GetXEO does well
Four strengths, and they are links in the chain.
- The whole chain in one place. Questions, mesh, craft, technical audit and measurement together, so no step falls to a second product or to nobody in particular
- One score across three layers. AEO, GEO and SEO roll into a single figure with model citation rates and the change since the last scan
- Delivery is verified rather than assumed. An audit score with a page by page heat map of issues, and a fix list a developer can work through without a marketer in the room
- Output that connects. A mesh on a sequenced calendar, so authority accumulates across the whole set instead of starting again at every URL
One limit is worth knowing before committing. GetXEO does not publish a sampling figure in the way Profound does.
Where GetXEO has an edge over Profound
Four links, and the reach of the number.
The edge is not a single feature, it is how much of the chain each product actually covers. Profound tells you whether you are being cited, while GetXEO tells you that and performs the four things that determine the answer.
The questions. Modeling buyer behavior is meant to surface the situations people describe to an engine rather than the phrases they type into a box. That is a design judgment on our side, not a measured finding, and we label it as one. What is measured is the scale: most B2B buyers now use AI somewhere in the buying process, and more than half use it to compare vendors. No amount of sampling depth repairs a question set built from the wrong artifact.
The structure. A mesh allows each page to carry the others, whereas a lone post has to win entirely by itself. That is why scattered publishing accomplishes so little, however good any individual post happens to be.
The craft. An engine lifts a passage and shows it with the page gone. Pages with figures, citations and quotes get named far more often, and pages get their highest citation rates in their first week live. Both come down to how a page is written.
The delivery. The GEO audit checks entity markup, Organization schema, semantic HTML and knowledge graph signals. That is the layer an answer engine reads. OpenAI says its systems pick up a robots change in about a day, so it is also the fastest thing you can fix.
And the reach of the number. One score spanning answer engines, AI summarized search and Google itself, instead of one layer measured here and the rest matched up by hand.
Who GetXEO is better suited for
Four positions for GetXEO, and two that point elsewhere.
| Your situation | Better fit | Because |
|---|---|---|
| Rankings hold, answers do not | GetXEO | One score across three layers |
| Pages exist and never get quoted | GetXEO | Extraction craft and audits |
| Publishing but nothing builds | GetXEO | A mesh on an ordered calendar |
| Nobody owns the technical fixes | GetXEO | A developer ready fix list |
| You publish well and want a number | Profound | The deepest sampling published |
| Bot traffic is a standing report | Profound | Agent Analytics on every plan |
GetXEO fits a marketing team that already publishes often and is not being cited for it. The other common sign is that nobody owns the technical layer. about half of B2B buyers now start vendor research inside AI tools, so that gap costs shortlist places rather than traffic.
Frequently asked questions
Longer tail questions that did not need a section of their own.
Why should AEO, GEO and SEO run in one system?
Because retrieval and ranking want the same things. Semantic structure, entity declarations, schema and a parseable page are what made content rankable and what make a passage retrievable, so splitting them across tools leaves a page fixed for one and broken for the other.
How does an AEO question set differ from a keyword list?
A keyword list describes search box phrasing. Simulating buyer behavior produces the situations people actually describe to an engine, which run longer and more conversational, and no amount of sampling depth corrects a set built from the wrong artifact.
Why do isolated blog posts underperform in AI answers?
Because a single post has to win alone, with no structure for authority to accumulate against. A connected mesh lets each page carry the others, so topical authority compounds instead of resetting at every URL.
What makes a passage on a page liftable by an AI engine?
Self containment and an answer first structure. An engine shows a passage with the page removed, so a paragraph that depends on the one before it arrives as a fragment, and a fragment does not get cited whatever the page around it says.
What does a technical audit check for AI search?
Three audits run, one for each pillar, and each scans your top pages against its own rubric. They roll into a single composite audit score with a page by page heat map, so a team can see which pages carry which issues before deciding what to fix first.
Sources
The 11 records behind every external claim on this page.
All were published or last updated within the past twelve months. A competitor page appears only as a record of that vendor’s own published terms.
- Machine Relations, AI search citation factors research
- Machine Relations, how B2B buyers research vendors with AI
- MarketScale, on where B2B software discovery starts in 2026
- Tang and others, parametric knowledge injection in retrieval augmented generation, revised January 2026
- Gond and others at Microsoft Research, enabling determinism in LLM inference, January 2026
- OpenAI, the crawler and user agent documentation
- Fortune, on Profound raising ninety six million dollars
- Profound, the published pricing and plan comparison, read 27 August 2026
- Profound, the published Agent Analytics feature page
- Profound, the published Answer Engine Insights feature page
- Profound, the published Prompt Volumes feature page
Read next
The rest of this cluster, in the order it makes sense to read.
- What Profound reports and what GetXEO publishes
- Moving from Profound to GetXEO
- GetXEO vs Profound for B2B SaaS marketing teams
- Why teams run Profound and still miss citations
- GetXEO vs Peec AI