41,150,680 suspects. 2,029 real infringements.

The situation

Moérie sells haircare, and the brand has earned real scale doing it — more than 1,000,000 customers have bought from them. That scale is exactly what makes a brand worth counterfeiting.

Counterfeit listings surfaced across eBay, Alibaba, and Amazon. For the team, the damage was not only commercial. As co-founder Kipras Kazlauskas put it, the hardest part was knowing the brand's name was attached to products they had no control over.

Why this is harder than it sounds

The instinct is to think of counterfeit enforcement as a removal problem: find the fake listing, file the notice, move on.

The actual bottleneck sits earlier. Across three large marketplaces, the volume of things that might be a problem is enormous — near-matches on keywords, resellers who turn out to be authorized, generic products that share a product term, listings in languages nobody on the team reads. Most of it is noise. Some of it is not.

A team reviewing that by hand faces a choice with no good answer. Look at everything, and enforcement becomes a full-time job that still falls behind. Look at a sample, and real infringements go unnoticed while the dashboard stays comfortably quiet.

What the numbers show

The scale of the filtering problem is the story here.

41,150,680 suspects analyzed. Every listing, seller, and signal the system examined across the monitored channels.

42,390 cases created. The share of those signals that carried enough substance to become a structured, reviewable case rather than raw noise.

2,029 confirmed infringements. The cases that met the legal bar — trademark scope, product match, seller authorization — and moved into enforcement.

That final ratio is the part worth sitting with. Roughly one genuine, actionable infringement for every 20,000 signals. Handled manually, finding those 2,029 would mean a team wading through 41 million pieces of noise. That is not a resourcing problem a brand can hire its way out of.

What changed for the team

Two things, and neither is "we removed some listings."

Channels stopped being separate projects. eBay, Alibaba, and Amazon ran through the same pipeline instead of being monitored, filed, and chased one marketplace at a time.

The worrying stopped. This is the part Moérie's team names first — not the removals, but no longer having to constantly wonder what might be appearing next.

That shift, from reactive checking to continuous coverage, is the whole point of the architecture behind this filtering. It is also why Brand Registry alone stops being enough once a brand sells beyond one marketplace.

In their words

Hair plays a real part in how confident you feel, and more than 1,000,000 people have trusted Moérie with that. When counterfeits started showing up, the hardest part was knowing our name was attached to products we had no control over. EnforceShield found those listings across eBay, Alibaba, and Amazon and got them removed. More importantly, our team no longer has to constantly worry about what might be appearing next. At the end of the day, when someone buys Moérie, it should actually be Moérie.

— Kipras Kazlauskas, Co-founder, Moérie

What this proves

A brand at Moérie's scale does not have a takedown problem. It has a filtering problem that only looks like a takedown problem from the outside.

The measure of enforcement is not how many notices got filed. It is whether the real cases surface out of the noise fast enough to matter, and whether the team can stop thinking about it between incidents. For a fuller picture of what that noise costs when it goes unmanaged, see what counterfeits actually cost a brand.