Basic
$199/moBelow DA/DR targetNot a fitExcluded from recommendation
AI SEARCH & BRAND DISCOVERABILITY
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Your learning path: 12 lessons.
Module 01 / Lesson 04 · Read, apply, check
Audit relevant AI answers with a defined prompt set, evidence captures and an actionable issue register.
Define whose discovery experience you want to examine: a buyer in a particular market, an existing customer comparing options or a journalist checking expertise. Select the products and modes available to the audit and state what is outside scope. Include both branded questions and unbranded category questions, because they test different situations. A branded prompt already supplies the company name and cannot demonstrate that a buyer would discover it unaided. Write a small, purposeful prompt set before running the audit. This avoids collecting only the questions that produce flattering answers after the agency has seen the results.
Record the exact prompt, date, product, visible mode, language, market context and whether the conversation starts fresh. Note relevant personalization or account context where observable, without collecting unnecessary personal information. Save the complete answer and displayed source links through an appropriate capture method. A cropped screenshot may omit a qualification that changes the meaning. If the product declines to answer, returns an error or produces no AI feature, record that outcome separately. Missing observations should not silently become brand absences, and a failed request should not be retried until it produces the result the team wanted.
Review each answer for identity accuracy, current facts, relevant inclusion, comparison context and source support. Assign issue categories such as outdated capability, confused entity, unsupported assertion, missing context or no observed mention. Prioritize by potential consequence and the strength of the evidence. A wrong safety-related product statement can matter more than absence from a broad list. Distinguish errors in the answer from errors on a cited page and gaps in the client’s own information. The repair action depends on where the problem is visible, and some findings may remain observations without an obvious intervention.
For each priority issue, record the evidence, proposed action, owner and review date. Actions may include correcting an owned page, preparing a clearer comparison, requesting a factual source correction or obtaining better expert evidence. Avoid promising that a correction will propagate immediately into every answer. Keep a baseline capture so later reviews can compare like conditions. Present the audit as a sampled view of the chosen discovery situations, not a census of everything an AI system says about the brand. The client should leave with a manageable set of improvements and a clear understanding of the audit’s limits.
Define audience, products, modes and prompts.
Save complete answers and visible conditions.
Separate errors, omissions and missing observations.
Build an evidence-backed repair backlog.
Fieldmark’s agency selects twelve prompts across product identity, buyer comparison and implementation concerns, in one language and a defined market. It tests two specified AI-search experiences using fresh conversations and saves complete responses. One response repeats the old mapping description, two cite an outdated product page and several category questions do not mention Fieldmark. The agency gives the identity error priority because it misstates the offer, while recording non-mentions without calling them system failures. Its backlog asks the web team to update the old page and the product lead to supply a current implementation explanation. A separate capture fails because the service is unavailable; it is marked missing rather than counted as another absence. The client receives the prompt register, issue evidence and assigned actions, plus a clear statement that the sample does not represent every user, location or future answer.
A competing audit asks whether Fieldmark is the best choice, reruns the question until it gets a positive answer and saves only the favorable paragraph. It omits the supplied brand name, the unsuccessful runs and the source links. The resulting slide claims that AI recommends the client to all buyers. That conclusion is not supported by the selected evidence, and it gives the team no useful way to compare future results or prioritize a factual improvement.
An audit is valuable because it makes a defined set of observations inspectable, not because it can reveal every answer a product might generate. Small samples can guide practical work when their boundaries are explicit. They become misleading when presented as universal market share or as proof that the agency knows the system’s internal preference.
Use public or appropriately authorized information and preserve relevant context in captures. Do not include private customer data in test prompts. Platform behaviour changes, and a repeat may legitimately differ. The audit provides a baseline and action list, not a guarantee that a specific intervention will change a future answer.
Do this today · 30 minutes
Design Fieldmark’s twelve-prompt audit and a three-item issue register.
Use a document or spreadsheet and the fictional case in this lesson. Work with public or synthetic information; no purchase or confidential client records are required.
Before: Define audience, products, modes and prompts.
After: Build an evidence-backed repair backlog.
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Ask a colleague to challenge the interpretation. Record the evidence, uncertainty, next action, owner and review date before applying the plan to a client campaign.
Before you move on
An audit finds a wrong product description, several unbranded non-mentions and one failed request. A teammate wants to omit the failure and rerun only non-mentions until the brand appears. Which method preserves a useful baseline for the client’s next campaign review?
Next lesson →Bing: AI Performance reporting and its limits
AMEC: Integrated Evaluation Framework
Lesson edition 1.0 · References reviewed 16 October 2026 · Practical exercise reviewed 16 October 2026. Examples are illustrative teaching scenarios.
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