About TracBrand
Built for the part of brand protection that nobody automates
Detecting suspicious links is a solved problem. Proving a finding, drafting a notice the receiving platform will act on, sending it, and chasing it until something happens is where brand protection programmes actually get stuck. TracBrand exists to automate that part.
Why we built it
Most programmes fail on volume, not on tooling
Ask any team responsible for brand abuse how their week goes and the answer is the same. A monitoring tool produces a list. Somebody opens every link, works out whether it is really their brand, screenshots what matters, looks up the right registration number, writes a notice, sends it, and adds a row to a spreadsheet so it can be chased later.
That process works at ten findings a month and collapses at two hundred. The collapse is never announced. Detection keeps running, the queue keeps growing, follow-up quietly stops, and eighteen months later nobody can say how many notices were actually actioned.
We built TracBrand around the assumption that the constraint is human attention. Every design decision follows from it: judge candidates before a person sees them, capture evidence at detection rather than reconstructing it, generate notices in the format each platform expects, and chase silence on a schedule rather than on somebody's memory.
What is left for a human is the set of decisions that should involve one — approving a notice, dismissing a finding, escalating a network to counsel. Those are recorded, with a name and a timestamp, because that is what makes an enforcement programme defensible.
How we build
Five principles that decide what ships
These are the arguments we have internally, resolved in advance.
Automate the work, not the judgement
Searching, comparing, classifying, drafting and chasing are mechanical, and we automate all of them. Deciding to send a legal notice is not, so it stays with a named person who has the right role.
Evidence at detection, not afterwards
Infringing material disappears or mutates. Everything needed to act on a finding — the image comparison, the score, the class, the rationale, the cited mark — is captured the moment it is detected.
Say what the software does
We list the surfaces we cover and the feeds we read. Where a decision is made by a rule rather than a model, we say so. Where a model is involved, we show you the output it produced.
Your queue should be finishable
A tool that produces more findings than your team can review has moved the problem, not solved it. Filtering aggressively and remembering every cleared verdict permanently matters more than raw recall numbers.
The record matters as much as the removal
Brand protection eventually meets counsel, a platform escalation or a board. Tenancy, roles and a complete audit trail are foundations, not enterprise upsells.
Where we are
An honest note on stage
We would rather be straight about this than have you find out in procurement.
TracBrand is a young platform. The detection, verification, clustering, enforcement and monitoring loops described across this site are built and working — that is why every page lists specific surfaces and feeds rather than a coverage number. What we do not have yet is a decade of case studies or a logo wall, and we are not going to borrow either.
If you are evaluating us against an incumbent, the right way to do it is a live sweep against a brand you already know is being abused. You will see the true positives, the false negatives and what the AI clears. That comparison is more useful than anything we could write here.
Judge it against your own brand
Thirty minutes, your marks, a live sweep. If detection is weak on your portfolio you will know inside the session.