Ad compliance checking

What ad compliance means.

Ad compliance is the work of making sure advertising follows the rules that apply to it. Those rules come from several places at once. Governments and regulators set some of them, industry bodies set others, and each ad platform has its own policies.

The rules depend on what you sell and where. In the United States, the FDA regulates prescription drug promotion and expects risk information to be presented in fair balance with benefit claims, while the FTC deals with deceptive advertising more broadly. In Canada, Health Canada oversees drug advertising rules, and the Pharmaceutical Advertising Advisory Board reviews many pharmaceutical materials. Financial promotion has its own regimes, such as FINRA's communications rules in the U.S.

A platform accepting an ad doesn't mean the ad is compliant. Platform review looks at the platform's policies, and it isn't legal or medical approval.

When it makes sense.

Automated checking is useful when you produce many versions of ads across sizes, languages or platforms, because the checking load grows faster than the number of people available. It helps when the same errors keep bouncing back from review, such as a missing disclosure or a claim phrased the wrong way.

It may be more than you need if you run only a few simple ads a year in a lightly regulated category. It also won't help much if nobody has written down your rules or approved your claims. Someone has to decide those first, and that's work for your qualified reviewers.

What automated checks can and can't do.

A compliance checker is good at comparing an asset with something written down. It can check whether a claim matches your approved wording, whether a required disclosure is present, whether a banned term has appeared, or whether the landing page says the same thing as the ad. An AI model can also read images and flag phrasing that's close to a problem without being an exact match.

The limits matter just as much. An AI compliance checker can miss a problem and can raise false alarms. It may not understand the overall impression an ad gives, which is often what regulators care about. It doesn't know about new guidance unless someone adds it, and it can't weigh risk the way an experienced reviewer can. A clean result only means the checks found nothing, so nobody should read it as approval.

That's why we treat automated checks as support for review. Legal, medical and regulatory decisions stay with qualified reviewers, and a responsible approver signs off before anything sensitive goes live.

Building your rule set.

The checks are only as good as the rules behind them. We work with your team to gather what's already approved, such as claims lists, required safety wording, brand standards, market rules and past reviewer comments. Past comments are often the most useful part, because they show how your reviewers actually apply the rules.

We keep the rules separate by product, market and language, since a claim allowed in one country may not be allowed in another. Each rule records who approved it and when.

How it fits with your review process.

Many pharma teams run a medical, legal and regulatory review, often called MLR, and we don't try to replace it. The checks run before materials reach those reviewers, so reviewers can spend their time on the questions that need their training.

We also check that what's live matches what was approved. Files get renamed, copy gets edited during trafficking and a destination URL can change after sign-off. Comparing the live ad with the approved version catches those gaps.

Sensitive data needs care too. Health details shouldn't end up in URLs, form fields or event parameters, so we check those as part of the work. You can read more on our pharma and healthcare page.

Our experience in regulated work.

With agency partner Klick Health, we worked on a year-long U.S. social program for Takeda, within FDA compliance requirements, serving both patients and healthcare professionals. We directed the cross-functional team delivering the program, which combined team leadership with content delivery.

We mention this as experience in a regulated environment. It isn't an example of this checking tool at work, and no clinical outcomes, prescription figures or unsupported engagement metrics are attributed to that program.

How we measure it.

We look at how many real problems the checks catch before review and how many reach reviewers anyway. We also track false alarms, because a tool that flags everything soon gets ignored.

We don't treat a low flag count as proof that the work is compliant. Your reviewers and regulators decide that.