Computer Vision for Counterfeit Detection
Computer vision for counterfeit detection: field-capture apps that turn photos into structured evidence for brand protection. Backed by a shipped CV build.
Problem: Counterfeit evidence is collected inconsistently on the ground and never becomes usable data.
What we build: A guided capture app plus computer vision that turns field photos into structured, verifiable evidence.
Outcome: Consistent, analysable counterfeit evidence that supports enforcement instead of sitting in a folder.
Brand-protection teams live and die on evidence quality, but the reality on the ground is inconsistent photos, missing context and data that never makes it into a usable form. Computer vision plus a guided capture app fixes both ends, standardising how evidence is collected and turning images into structured, analysable data. Here’s what we built and why the capture experience matters as much as the model.
The problem
When field agents document counterfeit product, the failure is rarely the camera. It’s consistency. Photos are taken from different angles, without the context that makes them admissible or analysable, and then dumped into folders and email where no one can search, count or act on them. The result is a brand that’s spending real effort collecting evidence it can’t actually use: no reliable count of where counterfeits are appearing, no structured record to support enforcement, and no way to spot patterns across regions because the raw material is a pile of unstructured images.
What we build
We build a guided field-capture app that walks the agent through exactly what to photograph and captures the context automatically, paired with computer vision that turns those images into structured evidence, detecting and classifying what matters and attaching the metadata that makes it usable. That means standardised capture in the field, automatic structuring on the backend, and a searchable, analysable evidence base instead of a folder. As with all field-vision work, the capture UX is half the battle: a model is only as good as the images it’s fed, so we design the on-the-ground experience to produce clean inputs, not just process messy ones.
The outcome
The payoff is evidence that supports action: consistent, structured, countable data that shows where counterfeits are appearing and holds up when it’s time to enforce. We’ve shipped exactly this pattern: a counterfeit-detection capture app that turned inconsistent field photos into structured evidence, plus related field-capture tooling used across hundreds of sites. The same guided-capture-plus-vision approach generalises to any field operation where the quality of on-the-ground data decides the quality of the decision.
Proof: shipped, anonymized
This is one of many builds like it. See the full delivered-build ledger or scope your version.
Questions, answered.
How does computer vision help with counterfeit detection?
It turns field photos into structured, classified evidence (detecting what matters and attaching usable metadata) so brand-protection teams get searchable, countable data instead of an unstructured pile of images.
Why is the capture app as important as the AI model?
Because a vision model is only as good as the images it's fed. A guided capture app standardises how agents photograph evidence in the field, producing clean inputs, which matter as much as the model that processes them.
Can this scale across many field sites?
Yes. Finzarc has shipped guided field-capture and vision tooling used across hundreds of sites, standardising collection so the resulting evidence is consistent and analysable at scale.
30 minutes with the founding team. Bring the problem; leave with a scope, a timeline, and the number it should move.