Before you commit budget, we assess whether ChatGPT Ads fits the business, whether the acquisition journey is ready and what a pilot would need to prove before further spend is justified. The recommendation can be to launch, fix dependencies first or not invest yet.
A useful audit should not end with a generic list of best practices. It should end with a decision, a priority order and a pilot design with explicit success and stop criteria. We separate OpenAI-documented requirements and platform capabilities from our own fit, economics and test-design methodology.
We analyse what you sell, who buys it, which problem they are trying to solve and how much context they need before deciding. The aim is to identify where a sponsored placement could add genuine value rather than simply create another source of impressions.
We also assess whether the opportunity is already captured effectively through Google, Meta or another paid channel, or whether ChatGPT Ads could add incremental coverage.
We review category, market, proposition and campaign materials against the current OpenAI advertising policies. The current version is v1.6, updated 10 September 2026, and covers advertiser eligibility, category and market restrictions and the contexts in which ads may or may not be placed.
Readiness does not end with policy. During Ads Manager onboarding, OpenAI reviews the advertiser account and considers whether the business offers products or services eligible under its ad policies. Verification, account information and billing must be complete before campaigns can deliver. The current account-setup documentation is also explicit that the advertiser should create its own account and invite the agency afterwards; we therefore treat account ownership and access as part of readiness rather than administration.
The audit flags risks before build: unsupported claims, sensitive categories, inconsistency between ad and destination, or account dependencies that could block review or delivery.
Primary sources: Ad policies · OpenAI · Ads Manager Beta Account Setup.
We check whether the destination is specific enough, whether the message continues the intent created by the ad and whether the business conversion can be measured reliably. We review events, UTMs, OpenAI Pixel, Conversions API, preservation of oppref through redirects and navigation, and downstream validation in analytics or CRM.
When Pixel and CAPI send the same conversion, OpenAI recommends using the same event ID for deduplication. Advanced matching and modelled measurement may also be available. Conversion data should only be shared after users have been given clear information about the data collected and all legally required consent has been obtained.
If measurement is weak, we would rather fix it before optimising a pilot against noisy or incomplete signals. A discrepancy between Ads Manager and GA4 or CRM is not automatically an error either: attribution windows, timestamps, reporting time zones, consent, storage or deduplication can all explain differences.
Primary source: Conversion Measurement · OpenAI Help Center.
We design an initial map of ad groups, situations, needs and messages. Context hints should be specific and natural, but they are not exact-match keywords, audience-targeting rules or delivery guarantees for a particular conversation.
We also assess whether there is enough creative coverage to learn without fragmenting the account until no group has useful signal. Different propositions, messages and destinations should exist because intent differs, not simply to increase asset count. That structural choice is our methodology; the limits of what a context hint controls come from the platform.
Primary source: Create Ad Groups for ChatGPT Ads · OpenAI Help Center.
We translate the commercial model into operating limits: the value of a purchase, lead or opportunity, the CPA or ROAS that would be defensible, the maximum budget worth putting at risk and the amount of signal required before interpreting the test.
The audit should leave three possible outcomes: launch, fix dependencies first, or do not invest yet. An audit that always recommends media spend is not really an audit.
You do not need live campaigns first. An audit can be useful before the first euro is spent, when a new channel enters the mix or when it is unclear which part of the system is weakening the test.
The value is in making a better decision before spend begins, not producing a document that forces you into management afterwards.
Yes. It can be particularly useful before the first euro is spent. We can review fit, eligibility, intent, landing experience, measurement and pilot economics without an active campaign. When Ads Manager is opened, we recommend that the advertiser creates the account directly and then invites CODE GPT Ads with the required permissions, in line with OpenAI’s current setup flow.
Yes. For an active account we review structure, ad groups, context hints, creative, destination, measurement and results so we can separate delivery problems from message, landing-page or conversion-quality problems.
Yes. We compare category, market, claims, ads and landing pages against the current OpenAI advertising policies. We also review onboarding and verification dependencies that could prevent the account or campaign from serving. The current policy version is v1.6, updated 10 September 2026.
Yes. We review message match, proposition, proof, CTA, forms, friction and measurement readiness. If the landing page makes the channel impossible to evaluate properly, we treat that as a pre-launch dependency.
The audit includes measurement diagnosis and design. We review events, Pixel, Conversions API, UTMs and, where relevant, CRM. Technical implementation can follow as a separate execution phase.
A prioritised diagnosis with an opportunity map, readiness matrix, initial pilot design and a clear recommendation: launch, fix dependencies or do not invest yet. We also define success and stop criteria so the test can be evaluated properly afterwards.
We document why. It may make more sense to fix a landing page, measurement, eligibility issue or proposition first, or to allocate the budget to another channel. A useful no-go can save more money than a weak pilot.
Yes, but it is not required. The audit can become a pilot brief for CODE GPT Ads, an in-house team or another agency. The account and data remain with the advertiser.
The audit can lead to campaign management or to one specific dependency: tracking, landing experience, feeds, B2B measurement or investment structure.
Tell us what you sell, which paid channels you use today and which conversion matters. We will return an actionable recommendation: launch a pilot, prepare dependencies or do not invest yet.
What you sell, which markets you work in and what you want to learn from the first pilot. If the scope is not clear yet, we will define it with you.