Product
Use cases
Resources
AXP - Answer Experience Platform

AXP: make your content
fit for AI extraction.

AXP prepares content for a reader that is not a person but a retrieval system: self-contained passages, machine-legible structure, governed bot access. That is a technical precondition for being cited — not a promise of inclusion.

Start free View pricing
Context / Problem

The page is read in fragments

Many retrieval/RAG scenarios work on passages rather than whole pages — a poorly structured block can land in the context and still fail to shape the answer. This is not a universal law: Google states no requirement to chunk content for generative AI.

Content written only "for humans"

Text written purely for a human reader often extracts badly for a machine consumer — a RAG system or an agent.

Access and structure are disconnected

Bot policy and content structure are governed in different places. With no single layer between them, visibility leaks without anyone noticing.

What this looks like inside the page

AXP does not rewrite text for its own sake — it assembles the page as a source of an answer

A product page has to explain more than a feature. It has to show the working scenario: which signal was found, which block of the page must answer it, and where the evidence stops.

Short AI answer

AXP is the layer that prepares content for AI extraction: the page gains self-contained passages, explicit definitions, sources, an FAQ and governed agent access. That improves fitness to be cited; it does not guarantee inclusion in any particular model [17, 27, 65].

Audit scenario

When the page exists but the answer quotes someone else

In a GEO audit it looks like this: the brand is mentioned, yet the model explains the category through a third-party review, because the product page carries no short block with a definition, evidence and limits.

Evidence
Check the prompt matrix: mentions present, owned citation absent; citation context resolves to an external source.
Owner action
Add an answer block to the opening sections, tie it to the FAQ and sources, then re-run the prompts.
Limit of the conclusion
This is a structural precondition. Without live measurement, uplift stays N/A rather than a proven result.
Ownership

Content gets an owner instead of staying general advice

AXP pays off when every change has a named owner: the editor owns the claim and the evidence, SEO owns schema and canonical, engineering owns bot access and render stability.

Evidence
In the audit, the claim-to-evidence link is recorded next to the technical finding, not in a separate note.
Owner action
Turn the recommendation into a backlog item: page, intent, block, source, acceptance check.
Limit of the conclusion
With no owner assigned, the recommendation stays an open loop rather than being published as done.
GEO pattern

One block answers one user question

A self-contained passage works better when it does not mix definition, comparison, process and caveats. For AI extraction each part has to stand without its neighbours.

Evidence
The GEO contract asks for a direct answer, section chunking, an FAQ and visible evidence for key claims.
Owner action
Split long product descriptions into answer, proof, limitation and next step.
Limit of the conclusion
Google requires no artificial chunking; the structure is there for clarity, not to game a rule [65].
AXP · content preparation layersSchematic
1Input: your content and your bot access policy
2Processing: preparation / extraction / measurement
3Signal: citations, absorption, gaps
4Output: prioritized actions (with no guarantee of inclusion)
Illustrative process schematic, not real data.
How it works
01

Content as an interface for machines

Every block is designed to be legible to a retrieval system — an explicit claim, its context, and the limits of application [17].

02

The self-contained passage

Dense retrieval works on semantic proximity; a passage should carry one complete thought — an editorial heuristic, not a proven factor of inclusion [26, 27].

03

Where the claim sits in the context

The key statement goes at the start of the block, because the middle of a long context is used less well [35].

04

Quality of representation in the corpus

An AI answer is retrieval plus the model’s parametric memory — not "only from your sources" [49].

05

Governed agent access

Access policy and content structure are brought into one layer, aligned with the visibility goals [70, 75].

What the product includes.

📑

Passage Layer

Self-contained blocks that open with the claim.

Read more →

Machine Schema

Machine-legible structure and markup.

Read more →
🗄️

Corpus Fit

Quality of representation on the retrieval side.

Read more →
🛡️

Access Control

One coherent bot policy.

Read more →
Sources (E-E-A-T)
17 · Salemi et al., 2024
Search for machine consumers and RAG.
↗
26 · Karpukhin et al., 2020, EMNLP
Dense passage retrieval: semantic proximity.
↗
27 · Khattab & Zaharia, 2020, SIGIR
ColBERT: retrieval at passage level.
↗
35 · Liu et al., 2024, TACL
Lost in the middle: position within the context.
↗
49 · Borgeaud et al., 2022, ICML
RETRO: retrieval as external memory.
↗
70 · OpenAI
Crawlers and user agents.
↗
75 · Anthropic
ClaudeBot/Claude-User and how to block them.
↗
65 · Google Search Central
AI features: no chunking requirement.
↗
Enigma in practice

Stories from audits, not just theory

What we put into the product is not abstract SEO advice but the patterns that keep recurring in GEO audits: what the model saw, which source it cited, where the brand went missing, and which page has to be rewritten.

Cited definition

An Enigma field note is an anonymized audit story containing a checkable signal, the action the page owner took, and the limits of the data. The format helps people and AI understand not only what to do, but why the recommendation appeared at all.

B2B SaaS · brand visibility

The brand is mentioned, but the source belongs to someone else

In a typical B2B SaaS audit the brand shows up for a direct branded query, while comparison and buying-intent prompts lean on review sites or competitor pages. The problem is not the brand name — it is the absence of a cited methodology and a comparison of its own.

Evidence
The prompt matrix shows mentions without an owned citation; citation rows point to a third-party source as the basis of the answer.
Action
Publish a methodology page, comparison answer blocks, and an FAQ covering selection criteria.
Limitation
Client names and uplift figures are not shown without confirmed permission; impact stays N/A.
E-commerce · category intent

AI picks directories over your category pages

In e-commerce the model often cites marketplace directories, because the category page carries no short block on selection criteria, availability, returns and alternatives. The page exists — it just offers no answer-ready evidence.

Evidence
Citation context resolves to an external directory; the brand category is in the sitemap but does not cover source-authority intent.
Action
Add a buyer guide, a criteria table, a shipping and returns block, and an FAQ that matches the visible content.
Limitation
Demand estimates made without a live SEO export are labelled Estimated, never Measured.
Agency · multi-client governance

The content team and the technical team pull different levers

A recurring agency problem: an editor adds FAQs and comparisons while robots.txt, the CDN or a WAF quietly block some AI and user-requested agents. The content is citation-ready on paper and not always reachable in practice.

Evidence
The crawler checklist records the gap between the intended access policy and the rules bots actually meet.
Action
Split training crawler policy, retrieval/search access and user-triggered fetch agents into a separate decision table.
Limitation
The final policy follows the brand's legal position and should not be imposed by a template.
Research-led content · proof depth

The evidence is there, buried too deep

Research-led articles usually do have sources, but the claim, the date, the study limits and the action for the reader sit in different places. AI can lift a fragment out of context and lose the point of the recommendation.

Evidence
The content-gap audit flags long paragraphs with no opening claim and a weak link from claim to evidence.
Action
Rewrite sections as claim, argument, proof, limitation — and put a summary box ahead of the deep material.
Limitation
Where primary sources cannot be reached, the block is marked needs review rather than published as fact.
Ownership workflowHow a story becomes a backlog item
Input

URL, intent cluster, model, run date, and the visible fragment of the answer.

Evidence

Mention, citation, absorption, the cited source, a crawler finding or a content gap.

Decision

A specific page, answer block, schema, technical rule or source layer.

Guardrail

Unverified metrics stay N/A; client names are never published without permission.

Frequently asked questions

What is AXP?

The Answer Experience Platform: the layer that prepares content for machine extraction — self-contained passages, machine-legible structure and a coherent bot access policy. It is a technical precondition for citation, not a guarantee of inclusion [17, 27].

Does AXP guarantee a place in the AI answer?

No. AXP improves the odds and removes technical barriers, but inclusion depends on the retrieval system, the model and competition between sources [35, 49].

How is this different from a regular SEO audit?

An SEO audit optimizes a page for ranking in a list. AXP additionally prepares content for extraction by a RAG system — often a different unit of analysis with different criteria, and without any requirement to break the page apart [26, 65].

Why does AXP govern bot access?

Because different platforms use different user agents; an incoherent access policy quietly costs you visibility before the question of content structure is even reached [70, 75].

Prepare your content for AI extraction.

A technical precondition for citation — with no promises of inclusion.

Start free