Detection AI
Judgement grounded in your actual registered marks
Text matching finds anything that mentions your brand. It cannot tell a fan account from a counterfeit storefront, or your authorised distributor from a clone. TracBrand puts a multimodal vision model between detection and your queue, and gives that model your real assets and your real filings to reason against.
Anatomy of a verdict
Every candidate produces the same structured output
Because the shape is fixed, the platform can act on it: route by class, prioritise by risk, threshold by confidence, and hand a reviewer a written rationale rather than a raw link.
- confidence_score
- 0–100. A discovery candidate must clear the configured threshold to become a case. Everything below is filed as reviewed-and-cleared and permanently remembered, so the same URL is never rated twice.
- infringement_type
- One of six classes — counterfeit, impersonation, logo misuse, piracy, gray market or domain squat — which determines the notice template and the abuse route.
- risk_level
- Critical, high, medium or low. Drives triage order and which findings escalate rather than queue.
- summary
- A written rationale in plain language. This is what a reviewer reads first and what accompanies the notice.
{ "logo_detected": true, "confidence_score": 91, "match_type": "registered word mark", "risk_level": "high", "infringement_type": "counterfeit", "recommended_action": "file platform notice", "summary": "Profile reuses the registered word mark and logo lockup to sell goods in class 25."}The model in production is a frontier multimodal model, currently Google Gemini 2.5 Flash. The output contract above is the platform's, not the vendor's, so the model behind it can be replaced without changing how cases work.
Inputs
Four things go into every comparison
Accuracy comes from context. A model shown only a suspect image guesses; a model shown your logo, your filings and the surrounding platform signals decides.
Your reference assets
The logo, trademark images and product shots you registered. Judgements are made against what your brand actually looks like, not against a text string.
The suspect material
The candidate's own imagery — a listing photo, a profile picture, an app icon, a video thumbnail, a page screenshot — fetched at detection time.
Your registered IP context
The word marks, legal owner and protected terms extracted from your trademark filings, injected into the prompt so the model knows what is actually protected and in which classes.
Platform context
Where available, the surrounding signals: captions, bio links, follower counts, video statistics, channel age, developer name and listing copy.
The IP document loop
Your paperwork is the part that compounds
This is the quietest capability in the platform and the one that matters most over time. Detection quality improves as your portfolio grows, without anyone maintaining a keyword list.
- 01
Upload
Trademark registrations, patents, copyright records and licence agreements go into the brand's document vault.
- 02
Extract
Each document is read for word marks, the legal owner, the Nice classification classes and the key terms worth protecting.
- 03
Verify ownership
A document naming an owner with no relationship to your brand is rejected rather than absorbed, keeping the vault trustworthy.
- 04
Feed back
Extracted terms widen future search queries and are injected into every subsequent AI judgement as registered IP context.
Why it matters
A notice that cites a specific registration number and class is acted on. A notice that says “this infringes our brand” is not. Because the citation comes from the vault, it is present on the first draft rather than added by hand later.
Noise control
Four filters before anything reaches a person
A brand protection queue is only useful if a human can finish it. These run in order, and each one is cheaper than the one after it.
- 1
Allowlist
Your own domains, properties and authorised sellers are removed first.
- 2
Exclusion list
Names you have explicitly marked as not yours — a similarly named unrelated business, a known partner — are dropped.
- 3
Permanent deduplication
Any URL already judged for this brand is skipped. Verdicts are remembered indefinitely, so repeat sweeps surface only genuinely new material.
- 4
Confidence threshold
Whatever survives is scored by the vision model, and only findings at or above the threshold are promoted to cases.
What it is not
Where we draw the line on AI claims
Brand protection is a legal process with an AI step in the middle, not an autonomous agent. Being precise about that is how the output stays defensible.
The model does not send anything
It scores and classifies. Drafting is templated, and sending requires human approval from a user with the right role.
Domain threats are not model-graded
Severity for certificate issuances, phishing hits, exposed services and leaks is decided by explicit rules, so it is reproducible and can be defended in a review. No model is involved in that pipeline.
Nothing is inferred about a real seller's intent
Verdicts describe what the material shows against your registered marks. Conclusions about the operator behind it are yours and your counsel's to draw.
Cleared is not the same as safe
A candidate below threshold is recorded as reviewed-and-cleared, and remains inspectable. It is filtered from your queue, not deleted from the record.
Run the AI against your own brand
The fastest way to judge detection quality is to point it at marks you already know are being abused. We will register them live and show you what comes back, including what it clears.