Defensible classification. At scale.

No. Veros is deliberately designed as a controlled classification system, not a conversational assistant.

Each AI analysis is run independently using the product information and context provided for that classification. It does not rely on a long-running conversation, hidden memory or accumulated chat history to determine what it should do next.

That matters in classification. Important decisions should not gradually change because a conversation has drifted or because previous interactions have become implicit context.

When new information changes the case, it becomes part of the classification record and a new analysis can be run. The reasoning remains explicit, reviewable and tied to the decision.

No. The expert remains responsible for the classification decision.

Veros does the research-heavy work: building product understanding, applying the relevant rules, considering alternatives, surfacing assumptions and drafting a reasoned memo.

Your classifier reviews that analysis, resolves open questions, applies business or technical context and approves the final outcome.

The objective is not to remove expertise from the process. It is to let that expertise go further.

Customer data is not used to train or fine-tune AI models.

AI processing is performed in stateless or isolated inference environments, and customer data is protected by the same security controls applied across the Veros platform, including encryption in transit and at rest, controlled access and logging.

For customers with additional security or governance requirements, Veros also supports alternative deployment and AI configurations.

Learn more in the Veros Trust Center

Classification should work across the organization, not only for the person sitting at headquarters.

Veros produces jurisdictional classification output in the language appropriate to that jurisdiction, making the memo easier to share with local teams, brokers, customers and customs authorities.

Users can also translate the output into their preferred working language, so a central team can review and collaborate on the same reasoning regardless of where the classification originated.

No. Veros is built for the product information companies actually have.

You can start with descriptions, specifications, spreadsheets, PDFs, drawings, images and other technical documentation.

Veros uses those sources to build an understanding of the product. If information that could affect the classification is missing, it surfaces the gap as an assumption or a specific question rather than silently inventing an answer.

You do not need to make your entire product master perfect before you can start improving classification.

Yes.

AI-assisted classifications, manually completed classifications and existing classification records can all be managed within the same governed process and master data environment.

The goal is not to create a separate database of AI classifications. It is to create one reliable classification repository, regardless of how an individual decision was reached.

No.

Veros can start with the files, documents and data you already have. Product lists, spreadsheets and supporting documentation can be brought into a controlled classification process without waiting for a major integration project.

Integrations can be added later where they create real operational value.

Veros is designed for companies where classification is an ongoing business process, not an occasional lookup.

If you only classify a few hundred straightforward products each year and your main requirement is simply to obtain an occasional suggested code, Veros may be more capability than you need.

Veros becomes valuable when classification is an ongoing business process. That might mean recurring volume, complex products, multiple classifiers or jurisdictions, significant customs or export-control exposure, or a need to improve governance, consistency and auditability.

Volume is therefore only part of the equation. A smaller number of difficult or high-impact classifications can justify a much more rigorous process than thousands of simple ones.