NobleJackal GEO Framework

A Proposed Standard for Representation, Evidence and Sustainable Value

# Author’s Statement

This book does not claim to have invented GEO. The field known as Generative Engine Optimisation appeared in academic and professional literature before this work.[1] The undertaking here is different: to propose a coherent standard for deciding what evidence can support a claim about the representation of a person, business, product or institution in generative AI systems; when that representation may be changed, by whom and within what limits; and to what extent the resulting effect may count as commercial value.

This is therefore not a guide to “how to do GEO”. It offers no formula for being mentioned more often, cited more frequently or recommended under every set of conditions. Before teaching a technique, it defines the object of that technique, its legitimate purpose and the point at which it must stop. It separates what can be measured from what matters; what has been observed from what has been proved; and what can be influenced from what can be controlled.

The intellectual core of the Framework can be stated in three lines:

> **Object: Representation.**
> **Purpose: Sustainable commercial value.**
> **Limit: Ethics.**

This is not a marketing promise. It is a hierarchy for making decisions. Visibility loses its meaning when the representation is false. A claim of success is empty when the commercial value is not real. If the method is unethical, even an apparently favourable outcome loses its legitimacy.

The book rejects the assumption that a system with access to a source will necessarily produce a correct answer. Generative search and answer systems may cite a source while leaving a claim only partly supported, drawing the wrong connection between source and answer, or presenting certainty where none exists.[2] The same prompt may also produce a different answer at another time, in another session or interface, under a different account state, language or context. That makes the surrounding conditions part of the method, not background detail.[3] These facts are not grounds for defeat. They are grounds for drawing the boundary of a claim in the right place.

The Framework does not promise absolute control. It proposes mechanisms for observation, record-keeping, comparison, human judgment, correction and withdrawal, while leaving responsibility with people. When the desired narrative conflicts with demonstrable reality, evidence takes precedence. When commercial interest conflicts with harm to the user, the user takes precedence. When the standing of the standard conflicts with a new finding, the new finding takes precedence.

The limit of this first edition is explicit. Its conceptual consistency and operational usefulness can be defended. Its empirical effectiveness can be tested only through real cases, independent practitioners, preregistered protocols and repeated measurement. Synthetic cases explain a principle; they do not prove its effect in the world. NobleJackal’s authorship does not make its judgments impartial. Independent review is essential wherever a conflict of interest exists.

This standard should derive its strength not from appearing immutable, but from remaining open to objection. A method that cannot be falsified is not a standard; it is a demand for loyalty. The final chapter therefore sets out routes for objection, revision, suspension and withdrawal alongside its provisions. The Framework cannot demand an accountability from others that it will not apply to itself.

— **Julian Gauss** *(Kaan Muraz)*

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# How to Read This Book

This book is written for executives making decisions about organisational representation in generative systems; GEO and search specialists; content and brand teams; quality and risk leads; auditors; researchers; and practitioners who design methods. Readers do not need to be model developers or statisticians. Those looking for a quick recipe for visibility, however, will not find the book’s real promise there. The promise is not greater visibility. It is the ability to distinguish which claims about representation and outcomes are legitimate, and under what conditions.

An executive may begin with *The Framework on One Page* to see the decision path. A practitioner can use the relevant chapter together with the corresponding record family in Appendix C. An auditor may begin with the Constitution, NJ-100 and the chapter on Objection. For researchers, the most important boundary is the separation between synthetic examples and empirical effectiveness. No reading path permits the reasoning of a chapter to be reduced to a form or a single threshold.

The book has three parts. Each establishes the legitimate ground for the next.

**Part One — Seeing Representation** defines the object of the work. *Centre* shows the distance between a real entity and its representation in systems. *Evidence* draws the boundary around what may responsibly be said about that representation. *Measurement* turns scattered outputs into comparable observations.

**Part Two — Changing Representation** addresses the cost and authority of action. *Intervention* approaches a verified distortion through the minimum sufficient change. *Governance* separates the right to observe, the right to act and the right to stop. *Time* explains how a sound decision ages and when it ceases to be valid.

**Part Three — Reaching Judgment** connects technical output with commercial and public responsibility. *The Final Test* examines the relationship between representation, customer fit and sustainable commercial value. *Audit* turns a claim into a recorded decision. *Objection* places the Framework before its own provisions. *Judgment* brings together the invariant core, the hierarchy of precedence and the absolute prohibitions.

The main text carries the reasoning behind each principle. Detailed record fields, decision schemes and audit instruments are gathered in the appendices. This keeps the book from becoming either a pile of forms or an abstraction that cannot be used. Readers applying a decision should use the main text together with the relevant appendix. The appendices are not checklists detached from their reasons.

Every synthetic case begins with the same notice: **Teaching simulation — synthetic data.** The figures in these cases exist only to make the reasoning visible. They must not be reported as actual performance, a market average or a probability of success.

Source markers lead to *Notes and References* at the end of the book. A citation does not mean that an external work validates the NobleJackal GEO Framework. Sources establish neighbouring fields of knowledge, historical context or the basis of a method. The author remains responsible for the provisions of this book.

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# The Framework on One Page

The Framework’s decision path can be read through ten questions:

1. **Centre:** Which representation of which real entity is being examined?
2. **Evidence:** What records support the proposed claim, and what records weaken it?
3. **Measurement:** Have the unit of observation, denominator, context and conditions of comparison been fixed?
4. **Intervention:** Is there a material distortion, and what is the minimum sufficient change?
5. **Governance:** Who holds the rights to observe, act, approve, publish and stop?
6. **Time:** For which version and period is the decision valid, and what will trigger review?
7. **Final Test:** Is there a reliable connection between the right customer, profitable delivery and sustainable contribution?
8. **Audit:** Can the evidence, method, conflicts of interest and opinion be traced in one audit file?
9. **Objection:** How will a record, coding choice, inference, grant of authority or decision of principle be reconsidered?
10. **Judgment:** When duties or rights conflict, which takes precedence, and when must the Framework withdraw itself?

The order is not arbitrary. Evidence cannot be sought until the representation has been defined. Measurement has no meaning until the limit of the evidence is understood. Intervention without measurement has no diagnosis. Intervention without authority has no accountable owner. A decision without a time limit hardens into a permanent entitlement. Work that is not connected to commercial value cannot be declared a business success. A judgment without audit, an audit without objection and a standard that cannot be withdrawn do not create trust.

The short form of the Framework is therefore unchanged:

> **Define the representation. Prove the claim. Fix the measurement. Choose the minimum sufficient intervention. Separate authority. Record time. Test value and audit it. Keep objection open.**

---

# Terminology Note

In this book, **GEO** is the abbreviation for *Generative Engine Optimisation*. The general category is described as a **generative artificial intelligence system** or, where the context calls for it, a **generative answer system**. An official product name, research title or technical quotation may retain its original wording.

An **entity** is the person, business, product or institution about which a representation is formed. A **representation** is the observable combination, within a particular answer, of the identity, attributes, context and judgments attached to that entity. **Evidence about the real world** is the record of reality against which the accuracy of the representation is checked. **Evidence of representation** records what the system said. **Evidence of outcomes** records the trace that representation left at the level of a user or a business.

The word **Framework** is retained as part of the proper name and the conceptual whole. A generic framework is written in lower case. **Proposed standard** refers to the normative provisions in this text. It does not mean independent acceptance, accreditation or international recognition.


Centre

Chapter I — Centre

Centre

## The Problem of Representation

When a system says your name, it has not found you. It has produced a representation of you. The appearance of the name is visibility. Attaching the right attributes to you is representation. Bringing the right customer towards you is a commercial outcome. For that outcome to be profitable, honest and repeatable is sustainable value. GEO’s first error is to erase the distance between these stages.

Choose the wrong object and the whole undertaking is distorted. Visibility may rise while representation deteriorates. Traffic may grow while customer quality falls. Sales may increase while the business loses money. Visibility is therefore not at the centre of GEO; representation is. Representation is not the final purpose. It is the object being worked on. From the business’s point of view, the purpose is sustainable commercial value created with the right customer. Ethics marks the boundary that cannot be crossed in pursuit of it.

This is the book’s first judgment.

## 1. The Distance Between Entity and Representation

A business is a whole in the real world. Its people, products, capacity, history, customers, limits and obligations are all parts of the same entity. What appears in a generative system is not that whole. It is a representation assembled, reduced and reconnected from the signals the system can reach. Retrieval-augmented generation can combine external information with model output, but finding a relevant fragment does not guarantee that the whole built from it will be true.[4]

The official website is only one of those signals. Independent publications, old pages, databases, user reviews, accounts in other languages and classifications made by third parties also enter the representational pool. These fragments may contradict one another. A company may call itself a “strategic technology partner” on its homepage while its service pages describe nothing beyond device repair. A hotel may appear adults-only in Turkish and family-oriented in another language. An expired certificate may be taken for a current capability; one international client may be treated as evidence of global operating capacity.

The system is not obliged to resolve these contradictions for us. It may choose one version, merge several, omit a material limit, or create a connection between fragments that has never been established in the world. The business’s task is therefore not to force its preferred story upon the system. It is to reduce the distance between demonstrable reality and public representation.

Once that boundary disappears, GEO ceases to be a discipline of representation and approaches propaganda. Propaganda enlarges strengths. A discipline of representation makes strengths and limits visible together. An entity that has not been identified cannot be optimised. It can only be amplified.

The commercial chain from representation to outcome is:

> **Representation → customer fit → commercial outcome → sustainable value**

The first link does not prove the last. A system may identify you correctly and bring no customers. It may bring the wrong customer, mediate a loss-making sale, or even create profit while making the operation unsustainable. The question for a technical report is therefore not, “How often did we appear?” It is, “Did the representation influenced through generative systems make an honest and sustainable contribution to a value relationship with customers we can genuinely serve?”

## 2. The Three Layers of Representation

Representation is not a single surface. It must be examined through three connected layers that cannot substitute for one another: **entity**, **attribute** and **judgment**.

### 2.1. Entity: whom does the system recognise?

A name is not an identity. Companies may share a name. Brand and product, founder and organisation, publishing name and legal identity may be confused. A former company name may displace the present identity. If the field of activity or geography is attached incorrectly, even a correctly named entity is placed in the wrong world.

An error at the entity layer is an error in the foundation. If the wrong person is recognised, accurate attributes are attached to the wrong identity. If the wrong category is chosen, the business is compared with irrelevant competitors. If the wrong geography is used, the recommendation becomes unusable. This layer tests the name, official domain, products, leaders, relationships between former and current names, similar names and language editions together. No claim of visibility can be established before identity is verified.

### 2.2. Attribute: how does the system describe the entity?

Correct recognition is not the same as correct description. The attribute layer examines the accuracy, scope and currency of the characteristics attached to an entity. A B2B company may be described as a consumer brand. A regional business may be presented as if it served the world. A service once offered may remain framed as the company’s principal activity. A real characteristic may be removed from its context and turned into an unlimited claim of superiority.

A favourable falsehood is still false. A negative error prompts objection; an error that appears to favour the business is often accepted in silence. When it reaches the customer, however, it becomes a delivery debt. Six questions are therefore required for every attribute: Is it true? Can it be evidenced? Is it current? Is its scope stated? Are its exceptions visible? Can the business meet the expectation it creates? A representation cannot carry more than the business is able to deliver.

### 2.3. Judgment: whom does the system recommend, to whom, and why?

At the judgment layer, a system does more than describe. It selects, compares, excludes or recommends. This is the layer closest to a customer’s decision and therefore the easiest to exaggerate. Being recommended in one answer does not mean being generally recommendable. Prominence in one language, for a particular budget and a particular intent does not carry into every other condition.

The reason for a recommendation matters more than its mere presence. What user need is the system trying to resolve? Among which alternatives is it choosing? Which characteristic makes the entity appear suitable? Can that reason be evidenced? Does it fit the business’s price, capacity, geography and ethical limits? A recommendation is a doorway to an outcome, not the outcome itself.

Transitions between layers create their own fields of error. The right name may be attached to the wrong category; the right category to an incomplete attribute; the right attribute to the wrong judgment. A system may sometimes recommend the right business to the right user for the wrong reason. The customer then arrives at the right place with the wrong expectation. A correct outcome does not validate faulty reasoning. A chain of representation is only as reliable as its weakest transition.

## 3. Representational Integrity and Forms of Distortion

An accurate representation is not one that pleases the business or repeats its preferred account word for word. It is one that satisfies five conditions of integrity together.

**Identity integrity** requires the name, brand, person, product and institution to converge on the correct entity. **Attribute integrity** requires an attributed characteristic not merely to exist, but to remain within the scope carried by its evidence. “Has worked with international clients” does not, by itself, establish “runs global operations”. **Context integrity** requires accurate information to be used for the relevant user intent; truth without context does not produce sound judgment. **Temporal integrity** makes visible the point at which information that was once true became outdated. **Delivery integrity** asks whether the business can actually meet the expectation created by the system. A representation that cannot be delivered is not accurate. It is merely persuasive.

These conditions fail in five principal ways:

- **Collision:** Two separate entities are treated as one identity. Accurate information is attached to the wrong entity.
- **Distortion:** A real attribute is removed from its proper scope; a limited capability becomes a principal activity, or a temporary success becomes permanent superiority.
- **Omission:** A material limit—price, capacity, an unsuitable user group or a mandatory condition—remains invisible.
- **Overflow:** The claim exceeds the field supported by its evidence. “Succeeded in this project” becomes “leads this field”.
- **Fossilisation:** Information that was once correct remains in the representation after it has ceased to be current.

Several distortions may occur in the same case. The type of error determines the intervention later required: collision calls for identity repair; omission for clearer limits; fossilisation for an update; overflow, often, for a smaller claim. Some problems need less assertion, not more content. Optimisation performed before the distortion is defined is treatment without diagnosis.

## 4. Representation Debt

A false representation is more than a missed opportunity. It is an active liability not yet visible on the balance sheet. The liability is collected when the user meets the business.

Identity erosion pushes the business into the wrong category and among the wrong competitors. Expectation debt allows a system to attribute a power or service the business does not possess; the user arrives on the strength of that promise, and the business pays for words it never said. Operational friction consumes the time of sales and support teams with requests for services that do not exist or exceed capacity. Commercial mismatch means that more demand produces fewer profitable customers. Strategic illusion leads management to mistake greater visibility for market validation and to base staffing or investment decisions on damaged data.

The debt is collected at first contact, in time spent correcting the representation; during quotation, through conflict over price and scope; in delivery, when the customer measures performance against a promise held in the mind rather than the contract; after the sale, through complaints and refunds; and in strategy, through resources moved into the wrong field. Visibility rises today. The cost appears later. The report of success has often already been delivered.

False visibility can therefore cost more than invisibility.

## 5. Teaching Case: Asteron Systems

**Teaching simulation — synthetic data.** This case does not represent a real business, system or GEO engagement.

Asteron Systems is a fictional provider of bespoke cyber-security audits for large organisations. It is mentioned frequently in the generative answers examined, yet is usually described as a consumer brand selling antivirus software to individuals.

*Table 1.1 — Synthetic representation profile for Asteron Systems*

| Observation | Synthetic result |
|---|---:|
| Total mentions | 42 |
| Correct organisational-audit context | 8 |
| Incorrect consumer-software context | 29 |
| Ambiguous context | 5 |
| Qualified organisational enquiries | 1 |
| Individual product enquiries | 17 |
| Enquiries converted to sales | 0 |
| Sales time spent on unsuitable enquiries | 26 hours |

A superficial report might say that Asteron has “strong AI visibility”. The data supports a different judgment: Asteron is not invisible; it is visible incorrectly. An intervention that increases the number of mentions would enlarge the wrong category rather than strengthen the right entity. The work must first establish that the entity being represented is Asteron, and then that the attributes attached to Asteron are accurate.

The case exposes a broader error as well. Demand can rise while customer fit falls. Fewer mentions can produce greater value. For a specialist industrial manufacturer, a small number of appropriate organisational buyers may be worth more than thousands of general visitors. Successful GEO does not always increase visibility. Sometimes it removes noise.

## 6. The Diagnosis at the Centre

A GEO engagement should begin not with a technical operation, but with five diagnostic questions:

1. **Which entity is being represented?** Do the name, category, geography, domain and language editions converge on the same real entity?
2. **Which attributes are being attached?** Are they accurate, current, demonstrable and deliverable? Are their limits visible?
3. **Which judgment is being made?** Whom does the system recommend or exclude, for which need and on what grounds?
4. **Which customer approaches?** Does that customer fit the business in need, budget, scope, timing and decision authority?
5. **Which sustainable outcome follows?** Does contact become a sale, the sale profitable delivery, and delivery satisfaction and repeat business?

These questions do not produce a single score. They establish grounds for judgment. A score gives one answer. Diagnosis preserves the tension among the questions that matter.

The ethical threshold is inside those grounds, not outside them. Not every business deserves to be strengthened; not every method is legitimate to apply; not every positive number qualifies as success. The same method may increase demand for two businesses. One states its price and capacity, refuses work it cannot perform and keeps its limits visible. The other attracts customers with a low initial price, adds charges later and advertises capabilities it does not possess. The technical outcome may look similar, but the second application scales harm rather than discovery. The fact that a method works does not make its use legitimate.

## 7. The Boundary of the Centre

The Framework does not grant a business the authority to declare its own account true. Nor does it treat every unfavourable representation as an error. A critical representation may be accurate. A competitor may be better suited. Capacity may be insufficient. The system’s refusal to recommend the business may be the best outcome for the user. The most honest intervention is sometimes to enlarge nothing.

The Framework is not a promise to control every generative system. System behaviour can be observed, tested under stated conditions and influenced through some surfaces. It cannot be owned in its entirety. A practitioner who claims control over an outcome they do not control creates risk, not expertise.

You have not won merely because a system mentions you. You may not have won when it identifies you correctly, or even when it recommends you for a particular question. A real outcome begins when an accurate representation meets the right person, the promise is fulfilled, the customer receives value, the business earns a profit and the relationship can be repeated honestly.

The judgment of this chapter will not change:

> **The object on which GEO works is representation. Its purpose for the business is sustainable commercial value. The boundary it may not cross in pursuit of that purpose is ethics.**

Representation has now been defined. Every sentence said about it must next answer one question: **What do we have that allows us to say this?**


Evidence

Chapter II — Evidence

Evidence

## The Boundary of a Claim

Evidence does not make a claim larger; it sets its limits. Seeing one answer is not the same as knowing a system's behaviour. A screenshot records a moment, not a general disposition. A brand may be mentioned in an answer, a text may be cited, a business may be recommended, or a customer may say that AI influenced a decision. Each is an observation. None, by itself, warrants a general conclusion.

“We appeared”, “we were recommended”, “we outperformed our competitors” and “GEO increased our sales” are not statements of equal weight. Each adds a new consequence to the one before it, and each new consequence requires a broader, more qualified body of evidence. A single model response can establish only that the response occurred. A customer's account may establish that AI played a part in the decision, but not that the system alone produced the sale. A rise in traffic may establish that more visits occurred during a given period, but not that a particular intervention caused the increase.

A claim may travel only as far as its evidence can carry it.

## 1. Evidence Is Not Decoration; It Is a Load-Bearing Relationship

No record is inherently strong. Its strength depends on the sentence it is being asked to support. A single screenshot that preserves the date, prompt and complete response may support the claim: “In its response to this prompt on this date, this system used the brand's name.” The same record cannot support: “Generative AI systems strongly recommend our brand.” The observation has not changed; the claim has expanded.

Data need not be altered to turn a genuine observation into an invalid conclusion. Erasing the boundary is enough. Evidence discipline therefore judges the sentence before it judges the data. “Our name appeared” is an observation; “we were represented accurately” concerns representation; “we were recommended more often than the selected competitors” is a comparison; “our intervention caused the change” is a causal claim; and “this representation brought us profitable customers” is a claim of commercial impact. One record cannot carry all five.

If the evidence cannot bear the load, the sentence is reduced. Uncertainty is not concealed, and missing ground is not covered with the language of success. A claim that exceeds its evidence is not polished. It is rejected.

This is not anxious overcaution. Early research into generative search systems showed that the presence of sources in an answer does not guarantee that those sources support every claim the answer makes.[2] A leading provider's own documentation likewise acknowledges that generative answers may contain inaccurate information, fabricated citations or unwarranted certainty.[6] Findings about particular systems at a particular time cannot be generalised to every product in use today. The distinction they reveal, however, endures: **the presence of a citation and the adequacy of its support are not the same thing.**

## 2. Three Domains of Evidence

GEO makes statements about three distinct realities: the world, generative representation and commercial outcomes. These domains may be connected, but they are not interchangeable.

### 2.1. Evidence about the real world

Evidence about the real world carries the underlying truth about a person or organisation. Does the company genuinely hold the certification? Is the hotel in fact intended for adults? Does the product provide the stated feature? Can the business serve the territories it claims? The answer to such a question does not come from a generative system. A system may repeat a claim; repetition is not verification.

The appropriate evidence about the real world depends on the claim. Records from competent authorities, valid certificates, contracts, auditable performance data, official corporate records and reliable independent publications may all carry weight. A business's own page is also a record, but it does not turn the business's claim of superiority into an independent fact. The source must still be assessed for independence, currency, scope and direct relevance to the claim.

### 2.2. Evidence of representation

Evidence of representation shows what a system produced under specified conditions: which system and interface, which prompt, which date, which language, which session conditions, which complete response and which sources? It does not show what the world is like. It shows how the system reconstructed the world.

A company's possession of a certificate does not prove that the system knows about it accurately. The existence of an official service page does not show that the system associates the business with that service. Correct information may have been published while the representation remains wrong. Representational integrity cannot be judged until evidence about the real world is compared with evidence of representation.

### 2.3. Evidence of outcomes

Evidence of outcomes traces what representation did at the level of the user and the business. Did the user see the answer? Did it influence the decision? Did the user contact the business? Was the enquiry qualified? Did a sale take place? Was delivery profitable and successful? Did the relationship recur? Each step requires its own record.

An invoice proves that a transaction took place, not that AI created the sale. “I found you on ChatGPT” is a signal of influence; it does not eliminate every other point of contact. A referral record may show a movement of traffic, not the whole decision journey. Outcome evidence often establishes only a partial relationship between events. A partial relationship is not complete causation.

*Table 2.1 — The function and boundary of the three evidence domains*

| Evidence domain | The primary question it can support | What it cannot replace |
|---|---|---|
| Evidence about the real world | What is fundamentally true about the entity? | Whether the system knows that truth |
| Evidence of representation | What did the system say under specified conditions? | Whether what it said is true in the world |
| Evidence of outcomes | What happened at user or business level? | Proof that AI alone produced the outcome |

The right document, used in the wrong evidence domain, produces an invalid conclusion.

## 3. From Signal to Claim

“We have data” says nothing about the status of that data. The Framework distinguishes five stages: signal, observation, anecdote, evidence and claim.

A **signal** is a trace worth investigating: the brand appears in an answer, traffic rises, a customer mentions an AI system, or a competitor is absent from certain responses. A signal creates a question. It does not settle one.

An **observation** is a signal whose context has been recorded. The system, prompt, date, language, complete output and observer are known. It establishes what happened, not a general pattern of behaviour.

An **anecdote** turns the limits of a single event into a broader story. The event may be genuine while the generalisation remains unsupported. One customer's mention of AI does not establish that “AI brings us customers”; one recommendation in one response does not establish that “the system recommends us”. An anecdote need not be false. It is simply insufficient to serve as a rule of behaviour.

**Evidence** is a body of records arranged to support a defined claim. Its raw material is available, its context preserved, its comparisons made, its contrary results retained and its limits stated. Evidence does not remove uncertainty. It shows where uncertainty begins.

A **claim** is the precise, testable sentence the evidence is asked to carry. “GEO works” may be a slogan, but it is not a testable claim. “Within the defined set of thirty prompts, accurate attribute representation increased between these two measurement periods” reveals its universe, comparison and boundary.

## 4. Mention, Citation and Recommendation

These three observations are often presented as rungs on a single ladder of success. They are not the same event, and they do not carry the same evidential weight.

A **mention** occurs when an entity's name appears in an answer. The system may name the entity in an accurate or inaccurate context, confuse it with another entity, or use it as a negative example. A name's presence establishes linguistic presence, not understanding.

A **citation** occurs when the system associates a particular text with an answer. That association requires two separate tests. The citation test asks which claim the source has been attached to; the support test asks whether the source actually carries that claim. A source may be sound yet fail to support the sentence. It may concern another entity, be outdated or have too narrow a scope. To be cited is not necessarily to be supported by the citation.

A **recommendation** occurs when the system presents an entity as suitable for the user among a set of options. It is closer to a commercial outcome, but it is not one. It arose within a particular prompt, system, language, time and field of alternatives. A recommendation is evidence of decision influence, not evidence of purchase.

The boundary between the stages is clear: a mention does not by itself prove accurate representation; accurate representation does not prove recommendation; recommendation does not prove contact; contact does not prove a sale; a sale does not prove profitability; and profitability does not prove sustainability.

## 5. Five Conditions of Evidence

Five conditions must be considered together before an observation can support a public claim.

### 5.1. Context

Every observation records the system and mode of access, the date and time, the complete prompt, the language, the complete output, the sources shown and the observer. Where known, it also records conditions that may materially affect the result, such as account type, whether the conversation was new or continuing, and whether memory or search was active. If the system is unknown, the record cannot be compared; if the prompt is unknown, the observation cannot be retested; if the date is unknown, change cannot be followed. Output without context is not evidence. It is a memory.

### 5.2. Traceability

Evidence must be open to independent examination. The complete prompt and output are preserved; source links, access dates and, where necessary, archive copies are recorded; raw data is separated from human interpretation; and a synthetic example is never presented as a real observation. A cropped screenshot usually reveals only the area its narrator chose to show. A record that cannot be traced cannot be audited. A record that cannot be audited cannot support a consequential public claim.

### 5.3. Behavioural reproducibility

In generative systems, reproducibility does not mean seeing the same words every time. It means seeing the same representational behaviour recur under comparable conditions. The sentence may change while the wrong category persists; the wording may change while the same material limitation is omitted. A single record is an event. Predefined, controlled repetitions may establish a behavioural pattern whose scope is stated. What must recur is not the sentence, but the behaviour of representation.

### 5.4. Comparison and counter-evidence

A test that seeks only favourable results does not produce validation; it produces selection. Under the same conditions, the brand should be compared with relevant alternatives. The test set should also contain neutral prompts that do not name the brand, contrary prompts in which a competitor or another category may be more suitable, and exclusion prompts in which the brand should not appear. If contrary results narrow the claim, that is not a weakness in the method. It is evidence discipline doing its work.

### 5.5. Boundary

Every record must answer explicitly: “What does this observation not prove?” Behaviour in one system does not prove behaviour in others. A result in one language does not prove the same result in another. A week of observation does not prove permanence. A recommendation does not prove customer acquisition. A customer's account does not prove full causation. A sale does not prove sustainability. A record that cannot say what it does not prove does not yet understand what it does prove.

## 6. Claim Thresholds

The evidence threshold rises as a claim grows. The Framework uses five thresholds:

1. **Observation claim:** “This system used the brand's name in response to this prompt on this date.” The complete prompt, date and raw output may be sufficient.
2. **Behavioural-pattern claim:** “Across the defined prompt set, the brand was usually represented in the correct category.” This requires a controlled set, repetitions, a time range, coding criteria and records of deviation.
3. **Comparative claim:** “In the same test set, the brand was represented with accurate attributes more often than the specified alternatives.” Every party must pass through the same system, time, prompts and evaluation protocol.
4. **Intervention or causal claim:** “The intervention contributed to the change.” This requires before-and-after records, an intervention log, an adequate observation window and consideration of alternative explanations. To happen after something is not to happen because of it.
5. **Commercial-impact claim:** “During the defined period, AI-mediated discovery was associated with qualified enquiries and sales.” Customer accounts or referrals, CRM matching, enquiry quality, sales, profitability and other contact channels must be examined together.

These thresholds are not a scoring ladder. Reaching a higher threshold does not mean that better work was done; it means that a broader sentence is bearing a heavier load. If the evidence reaches only the first threshold, the correct conclusion is to stop there.

## 7. Four Fields of Counter-Evidence

A sound test universe contains four fields. The **supportive field** comprises prompts in which the entity should properly appear. The **neutral field** explores need and category without naming the brand. The **contrary field** covers conditions in which another option may be more suitable. The **exclusion field** covers contexts in which the entity should not appear at all.

The exclusion field is particularly important. Appearing everywhere is not success; appearing in irrelevant contexts is representational spillover. The Framework measures not only when an entity is selected, but also when it withdraws. Work that does not seek counter-evidence is not testing reality. It is confirming itself.

## 8. Teaching Case: Aurelis Packaging

**Teaching simulation — synthetic data.** This case does not represent a real business, source page or system response.

Aurelis Packaging is a fictional manufacturer of industrial packaging. A generative answer describes the company as “carbon-neutral, an industry leader and a user of 100 per cent recycled material”, citing the company's sustainability page as its source.

*Table 2.2 — Synthetic support test for Aurelis Packaging*

| Generated claim | Information present in the source | Support status |
|---|---|---|
| Recycled material is used in pilot production | 40 per cent in one specified product line | Limited support |
| Every product is made from 100 per cent recycled material | No such statement | Unsupported |
| The company is carbon-neutral | An emissions-reduction target is stated | Unsupported |
| The company is an industry leader | No comparative data | Unsupported |
| The pilot is running at two facilities | Explicitly stated in the source | Supported |

The citation is genuine; most of the conclusion is not carried by the source. The system's expansion is not the only risk. If the business repeats this favourable account in its own campaign, it will be treating generative output as evidence about the real world. The Framework does not approve the claim because a source exists. It returns every sentence to the limit the source can support.

The strongest publishable statement from this case is: “Aurelis Packaging reports that it is running a pilot at two facilities, using 40 per cent recycled material in one specified product line.” The claims of carbon neutrality and leadership are rejected because no independent evidence of suitable scope is available.

## 9. The Decision of Evidence

Every material claim is assigned, through the Evidence Record described in Appendix C, one of four decisions: **rejected**, **further observation required**, **permitted only as an internal hypothesis**, or **publishable in limited form**. The record is not completed in order to approve a claim. It is completed to decide whether the claim may be made.

Language is integral to that decision. Instead of “AI sees us as the leader”, write: “In three specified prompts, the system described the brand as a leader; no independent support was found.” Instead of “GEO increased sales”, write: “An increase in sales was observed after the intervention; causation was not established.” Limited language may look less spectacular. It carries more weight.

A record that fails the evidential threshold need not be discarded. A single output, a cropped screenshot, an unexplained score, a rise in traffic or a customer's account may be a signal that begins an investigation. What it must not be given is a burden it cannot carry.

The judgment of this chapter is:

> **Without evidence, there is no claim. With weak evidence, there is only a narrow claim. A strong claim can be made only on strong, traceable evidence whose limits have been stated.**

The Centre established what is being represented. Evidence has now set the limits of what may be said about that representation. The next task is to define the conditions, units and comparisons by which the observations carrying this load are to be recorded.


Measurement

Chapter III — Measurement

Measurement

## The Discipline of Observation

Measurement does not create reality; it defines the part of reality we can see. A number is not the same as knowledge of a condition. A graph may show change, not its cause. Mentions can be counted, citations tracked and recommendation rates calculated; traffic, enquiries and sales can be compared. All are measurable. None, by itself, is success.

A GEO report is often constructed backwards: first a number is produced, then meaning is assigned to it, the meaning is converted into success, and success is tied to a commercial outcome. Each transition adds a new claim while the measurement remains unchanged. Research into generative information retrieval has likewise questioned whether established, ranking-based evaluation is sufficient on its own for generated answers.[5] The Framework rejects this chain. Measurement is not a number used to decorate a claim. It is a protocol that makes observations comparable.

What you measure sets the ceiling on what you may claim.

## 1. The Object of Measurement

A measurement system must answer five questions. What are we measuring? What is our unit of measurement? Under what conditions are we measuring it? Against what are we comparing it? Over what period are we observing it? Without those answers, “Our brand visibility is 72 per cent” is meaningless. Seventy-two per cent within which systems, prompts, languages and denominator? Is it a measure of mentions, accurate attributes, source support or recommendations? How were missing records treated? An undefined number is not measurement. It is scenery.

GEO measures not one thing but four distinct domains.

### 1.1. Entity representation

Can the system distinguish the right person, business, product or institution? Do similar names collide? Are category, geography and former-to-current identity relationships correct? Does the same entity survive across languages? Records might be coded as **accurate**, **partial**, **conflated**, **wrong category**, **ambiguous** or **absent**. “The entity did not appear” and “the wrong entity was recognised” are not the same: the first is absence; the second is distortion.

### 1.2. Attribute representation

Every material attribute attached to the entity is examined for accuracy, support, scope, currency, visible limitations and deliverability. Codes might include **fully supported**, **partly supported**, **unsupported**, **distorted** or **missing**. The right word is not enough to establish accurate representation; scope must also be measured. Having worked with international customers does not imply unlimited global operating capacity.

### 1.3. Judgment behaviour

Does the system merely describe the entity, compare it, recommend it conditionally or unequivocally, or correctly leave it out? Codes might include **appropriate recommendation**, **conditional recommendation**, **inappropriate recommendation**, **unsupported rationale**, **correct exclusion**, **incorrect exclusion** and **no judgment**. The number of recommendations is not enough. The rationale, the user's intent and the business's limits must be assessed in the same record. A business recommended in every prompt may not have strong representation; it may be spilling beyond its proper representational field.

### 1.4. Commercial trace

A declared AI influence, an observable referral, contact, a qualified enquiry, a proposal, a sale, gross profit, cancellation or refund, satisfaction and repeat purchase are tracked separately. Traffic is not a sale; a sale is not profit; profit is not satisfaction; satisfaction is not continuity. Analytics tools can reveal parts of the chain, not the whole decision journey. A commercial trace may show an association between representation and outcome. By itself, it cannot establish causation.

These four domains may be linked, but they must not be dissolved into a single number.

## 2. Unit of Measurement and Denominator

The Framework's fundamental unit of measurement is not a score but an **observation record**. One record comprises:

> **One entity + one system and mode of access + one prompt + one language + one time + one session condition + one complete output**

If any one of these elements changes, a new record is created. Testing the same prompt in another system, language or at another date is not a continuation of the previous record but a separate observation. The result may be similar; the record is distinct.

At minimum, every record contains the protocol version, entity, system and interface, time, complete prompt, language, complete answer and sources, prompt family, coding result, evaluator, comparison status and evidential boundary. “Ten answers were examined” describes volume. “In seven of the ten answers, the entity was recognised in the correct category under the predefined criterion” may constitute a measurement.

Every rate has both a numerator and a denominator. “The brand was recommended seventeen times” is incomplete unless the total number of prompts, systems and repetitions are disclosed, along with whether the prompt named the brand, how failed outputs were handled and whether exclusion tests were included. “The brand was recommended in seventeen of 240 valid outputs generated from forty prompts, three repetitions and two systems” shows the reader the field within which the judgment applies.

A missing record is not zero. If the system produced no answer, the result is recorded not as “the brand did not appear” but as “no output obtained”. Every rate must disclose the total number of records, inclusion and exclusion criteria, repetition structure, missing data and coding method. When the denominator is hidden, the number grows and the information shrinks.

## 3. Why a Single GEO Score Is Prohibited

This Framework does not reduce its decisions to one composite GEO score, because representation is not one-dimensional. A brand may have a consistent identity, distorted attributes, frequent recommendations and commercially worthless outcomes. One number flattens those tensions. What has been flattened may be easy to compare, but it can no longer be understood.

Mentions, citations, recommendations, positive language, traffic and conversions can be weighted to produce a result such as 78. Such indices may serve as summary signals in internal operations. On their own, however, they cannot identify the distortion, establish which right is affected or determine whether a public claim is permissible. The Framework therefore uses a **measurement profile**:

*Table 3.1 — GEO measurement profile*

| Domain | Observed condition | Scope | Principal limitation |
|---|---|---|---|
| Entity representation | Identity mostly accurate | Two systems, two languages | Other systems not measured |
| Attribute representation | Partial and contradictory | Four core attributes | Price and capacity excluded |
| Judgment behaviour | Conditional recommendation | Neutral and comparative prompts | Long-term stability unknown |
| Commercial trace | Limited association | Twelve customer accounts | Full attribution not established |

Sub-measures may use rates or counts. What is prohibited is not the number itself, but the concealment of distinct realities inside one score endowed with decision-making authority.

## 4. Point, Set and Distribution

Measurement operates at three levels of scope. A **point observation** is one prompt, one system and one moment. It shows that an event occurred, not that a behavioural pattern exists. It is valid to say: “On 12 August 2026, System A recommended the brand as the second option in response to this prompt.” It is not valid to say: “The brand ranks second in System A.”

A **set observation** consists of controlled repetitions that test the same user intent through different formulations. It may support a statement such as: “Across a forty-prompt purchase-intent set, the brand was represented in the correct category in 62 per cent of valid records.” The result belongs only to that set. It cannot be carried into other languages, systems or intentions.

A **distribution observation** examines several systems, languages, prompt families and time windows under common coding rules. It broadens scope; it does not guarantee certainty. As distribution grows, it becomes more important not to hide differences between environments. A dispersed protocol can produce more noise with more data.

Time runs through all three levels. One measurement is a record of a moment. To speak of a “condition” requires behaviour repeated at predefined intervals. Behaviour may be reported as **isolated**, **recurring**, **stable**, **volatile**, **weakening**, **absent after prior appearance** or **not yet classifiable**.

## 5. Protocol Lock

The measurement protocol is written before the result is seen. The entity under examination, measurement domains, systems, languages, prompt families, number of repetitions, time window, comparison group, coding rules and treatment of missing data are locked in advance. A protocol may change. The change may not be hidden.

A new system, language, prompt set or evaluation criterion creates a new protocol version. If old and new results are to be compared directly, the shared and unchanged subset must be reported separately. “It was 42 per cent before and is now 68 per cent” has meaning only if both periods measured the same thing in the same way. When the protocol changes, the series breaks. A broken series cannot be joined together by a narrative of improvement.

A completely fixed test may fail to detect new problems over time; a test that changes constantly destroys comparability. The protocol therefore maintains two prompt sets. The **core set** remains fixed for comparison through time. The **exploratory set** may change to investigate new user intentions and types of failure. Results from the exploratory set are not mixed into the core series. They first produce hypotheses and may enter a later protocol version where sufficient grounds exist.

## 6. The Prompt Universe

A test composed solely of prompts that name the brand does not measure discovery. It measures directed recall. A balanced prompt universe contains five families:

- **Identity prompts:** test names, categories, executives, products and institutional relationships.
- **Neutral discovery prompts:** examine discovery through need, category, geography and conditions of use without naming the brand.
- **Comparative prompts:** compare options under the same conditions and record the rationale rather than merely the order.
- **Contrary prompts:** seek the point at which the claim breaks, in contexts where a competitor or another category may be more appropriate.
- **Exclusion prompts:** test for representational spillover through questions in which the brand should not appear.

The prompt universe should test the limits an entity can genuinely carry, not the result the evaluator wants. The degree to which the prompts represent real user intentions must be stated. A controlled prompt written by a researcher and a naturally occurring user expression must not be combined in one pool without distinction.

## 7. Human Judgment, Coding and Disagreement

Generative output cannot validate itself. Asking the system “Is this answer correct?” does not constitute independent review; it produces another model output. Measurement requires codes and decision examples to be defined before the results are known.

For consequential claims, at least two human evaluators code the same records independently. The raw agreement rate may be reported first, followed by an appropriate measure such as Cohen's kappa, which accounts for agreement that could arise by chance.[15] A high kappa does not prove that the codes are correct in the world; it shows only that the evaluators applied the definitions in similar ways. Low agreement is not a defect to conceal. It is evidence that the definition, training examples or evidential base is inadequate.

Model-based tools that measure textual or semantic similarity can provide useful signals across large record sets.[19] Similarity between two answers, however, does not mean that either is correct. Automated similarity measures may assist classification; the final judgment on real-world accuracy, suitability for the user and ethical consequence remains human.

## 8. Comparability, Uncertainty and Missing Data

Two numbers can be compared only if they measure the same thing. A change in system or model, interface, language, prompt set, number of repetitions, competitor set, coding rule, time window or treatment of missing data may not make the results invalid, but it may place them in different series. Comparison requires symmetry. Numbers do not become comparable merely by standing next to one another.

Measurement does not eliminate uncertainty; it makes uncertainty visible. Three states in particular must remain distinct:

- **Not observed:** the relevant test was not performed.
- **Not found:** the test was performed and the behaviour did not appear.
- **Not applicable:** the criterion did not apply to this record.

None of these states is automatically zero. If the system supplies no citations, report “source support could not be observed”, not “there was no source support”. If a customer does not mention AI influence, report “no influence was declared”, not “there was no influence”. If the brand does not appear in one prompt, report “the entity was not observed in this prompt”, not “the system does not know the brand”.

Every report keeps three boundaries visible: **scope**—what was measured; **missing field**—what was not measured; and **uncertainty**—what else might explain the result. Writing the unknown as zero does not remove uncertainty. It merely hides it.

## 9. Teaching Case: Sable & Pine

**Teaching simulation — synthetic data.** This case does not represent a real business or measurement period.

A two-period report is presented for Sable & Pine, a corporate interior-design firm: 42 per cent in the first measurement and 68 per cent in the second. At first glance, this suggests marked improvement. Once the protocols are opened, however, it becomes clear that the two periods measured different things.

*Table 3.2 — Synthetic protocol comparison for Sable & Pine*

| Element | First period | Second period |
|---|---|---|
| Number of systems | 1 | 3 |
| Language | English | English, German, Turkish |
| Number of prompts | 20 | 60 |
| Prompts containing the brand name | 10% | 55% |
| Competitor and exclusion prompts | Included | Omitted |
| Coding criterion | Accurate attribute | Mention |
| Number of repetitions | 3 | 1 |

A 42 per cent accurate-attribute rate and a 68 per cent mention rate are not the same variable. In addition, the brand was named more often in the second period, the contrary field was removed and the number of repetitions was reduced. When the twelve prompts that passed through a common protocol in both periods are examined separately, the change is from 42 to 44 per cent.

The Framework does not discard the second period's data. It retains the data as the baseline for another protocol. It does, however, reject the claim of a twenty-six-point “improvement”. A change in protocol is not a change in performance.

## 10. The Decision of Measurement

The Measurement Record, whose detailed fields are set out in Appendix C, shows what was observed. The Evidence Record decides which sentence that observation may support. Measurement produces the record; evidence permits the record to speak.

Within the Framework, none of the following counts as measurement: a single GEO score whose definition is undisclosed; a prompt set altered after the results are known; preservation of favourable outputs alone; brand prompts presented as neutral discovery; results from different systems and languages merged without distinction; protocol change narrated as performance improvement; missing data treated as zero; a percentage without its denominator; or traffic, sales and sustainable value used as though they were interchangeable.

These practices may produce numbers. They do not produce measurement.

The judgment of this chapter is:

> **The purpose of measurement is not to show how good we are. It is to distinguish what we know, what we have merely observed and what we do not know.**

Measuring a distortion does not correct it. Seeing a change does not explain its cause. Making a problem visible does not mean that every problem requires intervention. Before moving from measurement to action, the next chapter therefore asks: **When should we touch a representation, and when should we only observe it?**


Intervention

Chapter IV — Intervention

Intervention

## The Cost of Touching Representation

An intervention is not a correction. It creates a new state of representation. Seeing a distortion does not confer the right to change it. Measurement may make the problem visible, and evidence may determine what can be said about it; neither makes intervention automatically necessary.

Some distortions are isolated or temporary. Some cause no material harm to the business. Some displease the business but are true. Some lose their effect if left alone; others grow because they are challenged. More content, pages, structured data, external sources, repetition or visibility is therefore not inherently an “improvement”. Each adds something new to the representational environment. It may reduce contradiction or create fresh contradictions; it may bring the right customer closer or scale the wrong customer.

Good intentions do not guarantee good outcomes. To touch representation is to replace the old condition with a new arrangement of risk.

## 1. The Status of an Intervention

Intervention is not the power to change a generative system's answer directly. It is **a limited, measurable and auditable change made within the environment in which an entity is represented, for the purpose of reducing a verified and material distortion**.

This definition preserves four distinctions.

**Distortion is not discomfort.** A business's failure to be recommended, a competitor's prominence, a system's critical language, or a difference between the company's own account and the system's account does not in itself constitute an error. Grounds for intervention arise only where the integrity of identity, attribute, context, time, judgment, delivery or commercial fit has been compromised.

**Change is not correction.** Updating a page, adding schema or increasing the number of external sources establishes only that an operation was performed. Improvement in representation may be claimed only after remeasurement under the same protocol and in an evidential sentence whose scope is limited.

**Effect is not success.** An intervention may increase visibility or traffic while also increasing misrepresentation, unsuitable demand or loss-making sales. Effect is observed; success requires a separate judgment.

**An observed output is not a controlled outcome.** A business can change its own website or ask an external source to correct a record, but it cannot edit a generative system's answer directly. It may influence the representational environment; it cannot claim ownership of the result. The permissible statement is not “We corrected the AI answer”, but: “The specified surfaces were changed; different representational behaviour was observed in the defined test set.”

## 2. The Intervention Gate

A standards-compliant intervention passes through five gates. Broad implementation does not begin until all five are open; a gate that remains closed narrows the scope or stops the intervention.

### 2.1. Verification

The distortion must be demonstrated through recorded measurement. A response from one system to one prompt on one date does not authorise a broad intervention. The record must identify the layer of representation in which the failure occurred, the prompts across which it recurred, whether it survived counter-testing, and the difference between evidence about the real world and the record of representation. The decision for an unverified distortion is: “observe; do not intervene”.

### 2.2. Materiality

Not every error requires action. Misidentification of a person or institution, the wrong category, incorrect expectations of price or scope, unsupported health, legal, financial or safety claims, misdirected customers, demand beyond capacity, cancellations, refunds, complaints, wasted operational time or reputational harm may all establish materiality. A preference for another tone or word, or the business's desire to appear stronger, does not. Where no material consequence exists, the condition is recorded and monitored.

### 2.3. An addressable surface

The problem must have a source that can be addressed: the business's website, a contradictory language edition, outdated content, identity architecture, structured information, an authoritative directory, a partner page, an obsolete external source or an access failure. If the problem appears only in system output and its originating surface is unknown, broad changes are not made. A hypothesis is formed, sources are compared and diagnosis is extended. Change without an addressable surface rests on hope, not diagnosis.

### 2.4. Measurable outcome

The baseline is frozen. Before the result is known, the protocol states the expected change, the fields that must not change, the prohibited side effect, the observation window, the stopping condition and the commercial indicators. “Looking better” cannot be measured. “Reducing representation in the wrong consumer category across the fixed core set” can. If the outcome was not defined in advance, success will be invented after the event.

### 2.5. Reversibility or elevated authority

An intervention should be reversible. As reversal becomes more difficult, the thresholds for evidence, approval and observation rise. The record must state how the change will be stopped, which result will trigger withdrawal, how information propagated to external sources will be corrected, who owns maintenance and when the intervention will be reviewed. An irreversible change demands stronger evidence and clearer accountability.

The gate's decision language is as follows. If the distortion is not verified, **observe**. If it exists but is not material, **record and monitor**. If it is material but its source is uncertain, **extend the diagnosis**. If the outcome cannot be measured, **do not intervene**. If all five gates are open, **make a limited intervention**. If reversal is difficult or the effect is broad, **escalate to governance**. If the method breaches an ethical boundary, **reject it**. A decision not to intervene is not indecision; it respects the limits of evidence and authority.

## 3. Risk and Control Surfaces

Health, legal, financial or safety claims; competitor comparisons; public claims of superiority; changes that will propagate across many external sources, languages or countries; changes of brand, URL or corporate identity; promises about price, capacity or results; and operations that could harm a third party if mishandled are all high-risk. High risk does not automatically prohibit intervention. It raises the burden of proof, approval and reversibility.

The field of intervention and the surface of control are classified separately. A **structural intervention** orders the relationships between name and brand, product and company, person and organisation, former and current identities, categories, geographies and language editions. An identity failure is not repaired with volume. A company using three different names does not acquire one coherent identity by publishing forty new articles.

A **content intervention** touches the primary definition, service scope, attribute claim, evidential link, exception, unsuitable customer group, price, capacity or currency of information. Its default verb is not “add”. Sometimes the right action is to remove, narrow, or reduce a claim of “leadership” to “experience under specified conditions”. The purpose is not to say more. It is to be misunderstood less.

A **signal intervention** improves the conditions under which verifiable information can be accessed and carried: technical readability, structured information, authoritative directories, independent verification, source currency and the consistency of external identity. A signal cannot replace truth. An unsupported claim does not become true when entered as structured data; repeating the same false sentence across twenty directories does not create independent consensus.

These fields operate across three control surfaces:

*Table 4.1 — Intervention risks and control surfaces*

| Surface | The business's actual authority | Strongest permissible statement |
|---|---|---|
| Owned | Directly change its website, documents, profiles and data | “This surface was changed.” |
| Influenceable | Request a correction or update from an external source | “A correction was requested/published.” |
| Observed | Record the response of a generative system | “Different behaviour was observed under these conditions.” |

A generative answer is not a surface of intervention; it is a surface of outcome. Where there is no control, there can be no claim of control.

## 4. Minimum Sufficient Intervention

The purpose of intervention is not to make the largest possible change, but the smallest change that is sufficient. The principle of **Minimum Sufficient Intervention** uses five levels:

*Table 4.2 — Levels of Minimum Sufficient Intervention*

| Level | Action | Appropriate condition |
|---|---|---|
| 0 — Observation | Make no change | Distortion unverified or immaterial |
| 1 — Clarity | Correct a definition, boundary, date or small item of content | Narrow and reversible problem |
| 2 — Alignment | Align pages, languages, identities and service accounts | Recurring internal inconsistency |
| 3 — External correction | Seek correction from a directory, partner or independent source | Distortion persists on external surfaces |
| 4 — Structural reconstruction | Change architecture, category, brand or identity relationships | Lower levels are insufficient and the harm is material |

Movement to a higher level must be justified. Except where urgent harm exists, no higher level is used before the lower level has been considered. The quality of an intervention is measured not only by the change it makes, but by the unnecessary change it avoids.

This principle requires the remedy to match the distortion. Identity collision calls for structural separation; attribute distortion for a narrower claim; material omission for a supported limitation made visible; fossilisation for obsolete information to be updated or removed; and commercial misfit for correction of the target customer and delivery boundary. A wrong diagnosis turns even a well-executed operation into the wrong intervention.

## 5. The Intervention Protocol

An auditable intervention proceeds through eight steps:

1. **Freeze the baseline.** Preserve the core measurement, prompts, complete outputs, coding and relevant commercial records.
2. **Classify the distortion.** Name it explicitly as collision, distortion, omission, spillover, fossilisation, faulty judgment, faulty exclusion or commercial misfit.
3. **State the intended and prohibited outcomes.** For example: corporate-identity accuracy should rise without increasing recommendation frequency among consumer enquiries.
4. **Choose the minimum sufficient surface.** A problem on one page does not justify rewriting the whole site; a problem in an external source is not answered by inflating the owned surface with text.
5. **Separate intervention classes.** Apply structural, content and signal changes in different stages where possible. Where separation is impossible, record every variable.
6. **Lock the observation window.** The first favourable answer is not success, and the first unfavourable answer is not failure. Complete the predefined period.
7. **Remeasure under the same protocol.** Preserve the core set and place new prompts in the exploratory set.
8. **Decide.** Continue, observe, narrow, expand under control, stop, withdraw or escalate to governance.

If intervention does not begin with measurement, its effect cannot be known. If it cannot end in withdrawal, it cannot be controlled.

Every intervention also creates **maintenance debt**. A new page must remain current, a new claim be reverified, language editions kept aligned, external profiles monitored and structured information kept consistent with visible content. When the pace of change exceeds maintenance capacity, representation becomes fragile. An intervention that cannot be maintained should not be made.

## 6. The Urgent-Harm Exception

Waiting may be more dangerous where the identity of a person or organisation is actively causing harm; serious misinformation is circulating in relation to health, law, finance or safety; an incorrect price or service promise is producing continuing user harm; or an overt collision such as identity theft is present.

The purpose of an urgent intervention is not optimisation but containment. Only the narrowest surface is touched; the highest-risk error is corrected or removed; no broad visibility work is undertaken; the provisional decision is recorded; and full measurement and governance review follow. Urgency does not remove the evidence threshold. It changes the first objective.

## 7. Teaching Case: Meridian Ledger

**Teaching simulation — synthetic data.** This case does not represent a real business, system or campaign.

Meridian Ledger is a fictional provider of corporate regulatory and financial-compliance software. It is being confused with an accountancy blog of the same name. The first proposal is to publish forty new articles about the brand. The Intervention Gate rejects the proposal: the problem is not a lack of content but an identity collision.

Implementation is limited to separating the identities of the company and the publication, clarifying the company-to-product relationship, explaining the former brand name, aligning the language editions and bringing official profile records into agreement.

*Table 4.3 — Synthetic outcome profile for Meridian Ledger*

| Indicator | Before | After |
|---|---:|---:|
| Total brand mentions | 42 | 36 |
| Correct corporate identity | 25/60 | 53/60 |
| Identity collision with the blog | 21/60 | 4/60 |
| Misplaced consumer enquiries | 14 | 3 |
| Qualified corporate enquiries | 3 | 8 |
| Sales time spent explaining identity | 19 hours | 4 hours |

Total mentions have fallen while identity accuracy and commercial fit have improved. The success of the structural intervention lies not in appearing more often, but in appearing less often as the wrong entity.

The case also shows how an intervention should be reported. It is not permissible to say: “We corrected the misinformation in AI.” The defensible statement is: “Owned identity surfaces and specified official profiles were aligned; identity collisions with the blog declined across the same core set. Full causation in generative-system behaviour was not established.”

## 8. The Decision of Intervention

Representational integrity, customer fit, business effect and ethical legitimacy are assessed together. If integrity improves while commercial outcomes remain unchanged, observation continues. If integrity improves but burden or harm rises, the intervention is narrowed or withdrawn. If visibility rises while integrity falls, the work is invalid. If visibility falls while unsuitable demand and operational burden also fall, the outcome may be successful. A commercial gain obtained by breaching an ethical boundary is invalid in its entirety.

The Intervention Record, detailed in Appendix C, keeps in one place the linked evidence, the result at each of the five gates, the risk class, intended and prohibited outcomes, changed surfaces, observation window, stopping and withdrawal conditions, maintenance owner and final decision. The record is not kept to justify the work already done, but to decide whether that work may continue.

The judgment of this chapter is:

> **Know before you touch. Once you know, choose. Once you choose, measure. Once you measure, withdraw if necessary.**

Intervention has established when and how representation may be touched. The next question is who has the right to make that decision, who approves the public result and who remains accountable when the intervention fails.


Governance

Chapter V — Governance

Governance

## The Limit of Power

Authority can be delegated; accountability cannot. The fact that a change can be made does not mean that permission has been given to make it. The fact that a system can be influenced does not make that influence legitimate. A business's wish for visibility does not make it fit to be amplified. Nor does a practitioner's technical knowledge qualify that practitioner to make every decision.

Evidence established what may be said, measurement what has been observed, and intervention when and how representation may be touched. Governance asks a harder question: who may decide? Who observes, proposes a change, implements it, approves a public claim, stops the work and withdraws an approval already granted? When something goes wrong, who remains accountable?

If those answers are not written down, the risk has no owner. Ownerless risk does not disappear; it remains invisible until it produces a consequence. NIST's AI Risk Management Framework and its Generative AI Profile offer adjacent approaches that likewise treat governance, measurement and risk response across the lifecycle.[7][8] Governance may slow a method down. That is not a defect but its function. Speed does not make a false claim true. Automation does not assume accountability. An agency does not replace the entity owner. Model output is not a certificate of approval.

Where there is power, there must be a boundary.

## 1. Five Decision Rights

Governance is not a list of operations. It is the order that determines what may be done, who may decide and where accountability remains. One undifferentiated word—“authority”—cannot establish that order. There are five separate decision rights.

The **right to observe** produces records; it does not make decisions. An analyst may code outputs, an automation may run repetitions, and a model may flag similarity or contradiction. None of these actions authorises intervention or publication.

The **right to propose** allows a practitioner to submit an intervention or publication proposal containing the supporting records, purpose, intended and prohibited outcomes, risk class and withdrawal conditions. A proposal is a request for authority, not authority itself.

The **right to implement** extends only to the scope approved in writing. Authority to correct content does not include authority to change brand positioning. Authority to align a language edition does not include authority to publish a new claim of superiority. Authority to request a correction from an external directory does not include authority to manufacture a false independent source. Implementation authority ends at the approved boundary.

The **right to approve** is not a formality; it is an assumption of responsibility. The approver records that they have seen the scope of the evidence, the unknowns, the risk, the permitted language and the stopping conditions. An unscoped “fine” in a message, or silence in a meeting, is not approval.

The **rights to publish and to stop** treat public disclosure and the cessation of risky publication as separate decisions. A claim's evidential support does not make it automatically fit for publication. Every public sentence must have an owner, and a named human must have the prior authority to stop an erroneous publication. A structure that can begin something but cannot stop it is not governing it.

One person may hold several roles in low-risk work. In a high-risk case, one person cannot hold them all. A structure that chooses its own method, measures its own outcome, approves its own success and publishes the same claim does not produce oversight. It produces self-validation.

## 2. Three Planes of Accountability

The **entity owner** is the person, brand or institution concerned. It may authorise an agency to implement, a consultant to select a method, an employee to publish content and software to collect records. It remains accountable for the truth of its claims, delivery capacity, customer effect, approved public language, authorities granted and correction when something goes wrong. “The agency did it” explains an external relationship; it does not remove internal accountability.

The **practitioner** may be an agency, consultant, team or individual. The practitioner's duty is not to produce the client's preferred outcome at any cost, but to judge whether that outcome is consistent with the evidence and the ethical boundary. A client request cannot legitimise presenting weak evidence as a strong claim, turning an isolated output into general success, using synthetic data as real performance, degrading a competitor's representation, manufacturing false consensus or sustaining an intervention that should be withdrawn. Loyalty to the client cannot outrank loyalty to the truth.

The **intermediary system** may be a model, search layer, retrieval infrastructure, automation or analytical tool. It can produce effects, perform operations and flag risks; it cannot accept institutional accountability. A model's statement that “this claim is reliable” is not independent approval but another model output. “The AI decided” is not an explanation. It is an evasion of responsibility.

Accountability does not remain at the level of principle. Every material decision names the proposer, implementer, approver, publisher, person empowered to stop the work and final accountable owner. Traceability and accountability are likewise treated as distinct responsibilities in the OECD AI Principles.[11] Accountability without a name has not been assumed.

## 3. Fitness Gates

### 3.1. Business fitness

Not every business is fit to be amplified through GEO. Six fields are examined before work is accepted.

1. **Claim fitness:** The central statements to be amplified must be capable of proof. Unsupported terms such as “best”, “leader” or “guaranteed” are narrowed. If the business refuses, the work is refused.
2. **Delivery fitness:** Capacity, geography, price, staffing, delivery time, support, cancellation and refund terms, and promised outcomes must be capable of meeting the expectation created. A claim that cannot be delivered cannot be amplified.
3. **Customer-harm fitness:** Work is refused where hidden fees, manufactured scarcity, misleading guarantees, false reviews, fabricated success rates, concealment of harm or deliberate failure to exclude unsuitable customers form part of the business model. Making a harmful model more visible scales the harm.
4. **Correction fitness:** The business must accept narrowing unsupported claims, removing obsolete information, aligning language editions and withdrawing an erroneous public statement. Preserving its own contradictions while expecting the system to represent it accurately is not fitness.
5. **Transparency fitness:** Real and synthetic data, measured and assumed outcomes, sponsorships and conflicts of interest must remain separate.
6. **Accountability fitness:** The business must accept that final accountability remains with it. Work does not begin on the condition that “the agency assumes all responsibility”.

The decision may be **accept**, **conditional acceptance**, **observe and verify first**, or **refuse**. Commercial opportunity does not create ethical fitness.

### 3.2. Practitioner fitness

Technical knowledge alone does not confer fitness to exercise representational power. The practitioner must be able to distinguish entity, attribute and judgment layers; mentions, attribution, recommendations and commercial outcomes; and evidence, measurement and intervention. The denominator must be disclosed, counter-evidence sought, uncertainty recorded, and the method must not be presented as its own proof of success.

If a client asks for favourable outputs alone to be included in a report, a competitor to be weakened or an unsupported superiority claim to be made, the practitioner has not merely a right but a duty to refuse. Conflicts such as services supplied to competitors, outcome-based fees, revenue from badge sales, or the combination of evaluator and sales roles must be disclosed. A conflict does not automatically invalidate every engagement; concealing it invalidates trust.

The practitioner must also demonstrate record and withdrawal discipline, observance of the authority granted, and competence in high-risk fields. The outcome may be **fit**, **fit with limited authority**, **fit with additional oversight**, **not fit for high-risk operations**, or **not fit**. Competence does not create authority; authority must be granted separately.

## 4. Risk Classes and Two-Key Control

Governance decisions are handled under four risk classes:

*Table 5.1 — Risk classes and minimum approval*

| Class | Typical example | Minimum approval |
|---|---|---|
| Low | Correction of spelling, date, clear identity or a minor boundary | One accountable human |
| Medium | Multilingual narrative, external-directory correction, change in service scope | Practitioner + entity owner |
| High | Health, law, finance, safety, competitor comparison, superiority claim, difficult reversal | Two separate humans |
| Prohibited | False consensus, competitor degradation, synthetic data presented as real, covert manipulation | Reject |

The risk class cannot be lowered after the result is seen; it is determined before the operation. A high-risk decision requires at least two distinct humans: the **proposer** and the **approver**. The proposer is accountable for the method and rationale; the approver for the evidence, risk, language and withdrawal conditions. Neither key may be held by an automation or model.

Two-Key Control applies to public claims of success, competitor comparisons, language such as “best” or “most trusted”, health, legal, financial or safety effects, publications that use model output as evidence, structural interventions that are difficult to reverse, broad external propagation and any future mark of conformity.

## 5. Claim Governance and Transparency

Not every accurate observation should be made public. A claim occupies one of four publication states:

- **Internal hypothesis:** may be used in research and intervention planning; may not be published as an outcome.
- **Limited observation:** may be shared with the system, date, prompt and scope stated; may not be generalised.
- **Limited public claim:** has an Evidence Record, human approval, permitted wording and an explicit boundary.
- **Withdrawn claim:** may no longer be used; its earlier record is retained and marked as withdrawn.

Every public claim has an owner, a linked Evidence Record, a measurement scope, the exact permitted sentence, prohibited extensions, risk class, approver, publication and review dates, and withdrawal conditions. A public claim is not a writing decision. It is a decision to assume responsibility.

Transparency does not mean publishing everything. It means not withholding material information that would change the judgment. The method, systems measured and not measured, prompt universe, time range, evidential boundary, missing data, contrary results, synthetic examples, conflicts of interest, protocol changes and side effects must be visible. Trade secrets, security information and personal data may be protected; where detail cannot be published, the effect of that limitation on the decision must be explained.

Incentive matters as much as method. An agency rewarded for increased visibility may come to treat more visibility as success; an organisation dependent on badge revenue may lower its approval threshold; an evaluator whose contract depends on renewal may soften an adverse result. This is not an accusation about individual character. It is a problem of system design. Open disclosure, separation of roles, second review, fixed criteria, remuneration independent of outcome, exclusion of sales teams from fitness decisions and an appeal route all reduce the risk.

## 6. A Mark of Conformity: Design Before Authority

A mark of conformity is not a claim of superiority, a recommendation or a guarantee of results. It indicates that a specified scope was assessed against written criteria under a stated date and Framework version. Publication of this book does not in itself establish an active certification programme. **No “NobleJackal GEO certificate” or equivalent mark may be issued until the infrastructure for independent decisions, conflict-of-interest management, surveillance, appeals, suspension and withdrawal is operational.**

This boundary is consistent with international practices that emphasise impartiality, separate decision-making, surveillance, suspension and withdrawal, and appeal mechanisms in conformity assessment. The book does not, however, claim ISO accreditation or ISEAL membership.[17][18]

If a pilot mark is introduced in future, it may mean no more than: “The specified representational field of this entity was found to conform, within the stated scope and on the stated date, under the indicated version of the NobleJackal GEO Framework.” It can never mean that AI systems recommend the business; that the business is superior to competitors; that all its claims are true; that it is consistent in every language; that it will remain unchanged; that commercial results are guaranteed; or that ethical failure is impossible.

The mark exists together with an identifier, the entity assessed, the Framework version, included and excluded scope, evidence date, review date, suspension and withdrawal conditions, a public verification record and rules of use. An assessment service may be purchased; its outcome may not. The mark may not be renamed “AI approved”, “AI recommended”, “best” or “guaranteed”.

The mark is suspended where evidence has aged, a serious new contradiction emerges, the mark is used outside its scope, synthetic data is presented as real, a public-claim rule is breached, a correction request goes unanswered, a signal of serious customer harm appears, the Framework version changes or review is not completed. Fabricated evidence, false consensus, deliberate degradation of competitor representation, repeated scope violations, an undisclosed serious conflict of interest, material user harm or misleading sales use are grounds for withdrawal. A mark that cannot be withdrawn is not a governance instrument. It is a marketing ornament.

## 7. Incident and Enforcement Management

Governance does not assume that error will never occur. It establishes the route to follow when it does.

1. **Freeze:** Temporarily stop the risky claim, intervention or use of a mark and prevent further propagation.
2. **Preserve the record:** Retain the raw output, prompt, time, content version, approval and change history. Do not erase the traces of failure.
3. **Contain the harm:** Correct owned surfaces, send corrections to external sources where necessary, and stop the erroneous customer promise.
4. **Notify:** Inform the parties whose decisions are affected. A material public error may require a public correction.
5. **Adjudicate:** Distinguish failures of data, measurement, approval, authority, ethics, intent and system control, and record the resulting decision.

Depending on the gravity of the breach, enforcement may take the form of a correction request, written warning, claim withdrawal, cessation of the intervention, additional audit, suspension or withdrawal of a mark, restriction of practitioner authority, termination of the engagement or a public status record. Enforcement is not retaliation. It is the standard remaining faithful to its own sentence. The size or commercial value of the client cannot alter the criterion.

## 8. Teaching Case: AegisCare Diagnostics

**Teaching simulation — synthetic data.** This case does not represent a real healthcare provider, clinical dataset or model test.

AegisCare Diagnostics is a fictional provider of corporate screening and laboratory services. It wants to publish the statement: “AI systems recommend AegisCare as one of the most trusted diagnostic centres.” It submits six favourable generative outputs and one pilot satisfaction report.

*Table 5.2 — Synthetic governance decision for AegisCare Diagnostics*

| Governance field | Synthetic finding |
|---|---|
| Evidence about the real world | Service licences are valid |
| Evidence of representation | Six favourable outputs for specified prompts |
| Comparative evidence of trustworthiness | None |
| “Most trusted” claim | Unsupported |
| Domain risk | Health — high |
| Two-Key decision | Second approval refused |
| Permitted wording | “AegisCare was recommended in the six specified test outputs.” |
| Prohibited wording | “Most trusted”, “AI approved”, “selected by AI” |

Part of the observation is real, yet the requested public language is refused. A licence does not prove comparative trustworthiness, and six recommendations do not prove the superiority claim “most trusted”. In a health context, false certainty can have a serious effect on user decisions. A sentence's partial truth does not make it fit for publication.

## 9. The Decision of Governance

The Governance Record, detailed in Appendix C, brings together the matter for decision, linked evidence, business and practitioner fitness, risk class, proposer and approver, conflicts of interest, permitted and prohibited acts and language, review and withdrawal conditions, and final accountable owner. Without a record there is no decision—only an event for which a story may later be invented.

The judgment of this chapter is:

> **Authority may be delegated. Accountability may not. Automation can perform operations; judgment and the duty to answer for them remain human.**

Governance refuses some work, narrows some claims, declines some clients and withdraws approval where necessary. This is not weakness. It is the moment at which authority does its work.

No decision, however, remains valid forever. Evidence ages, protocols become obsolete, interventions create maintenance debt, and what is true today may cease to be current tomorrow. Time is therefore the outer ring.