Group exact brand labels while keeping identity resolution explicit

This technical walkthrough outlines a process for grouping company-brand labels in an AI-assisted system, without attempting to resolve brand identity. The system accepts input containing up to 60 unique signal rows, each with information such as signal ID, brand name, event category, severity, status, timestamp, and source URL.

For example, a sample input contains three rows: two for Nike (one with a high-severity active product launch and one with a medium-severity resolved price change) and one for Apple (with a critical active risk observation). The system creates deterministic summaries for the brand labels and preserves the source URLs, which are then deduplicated in the final report.

The system does not establish a corporate hierarchy, check trademark ownership, or prove that each source record refers to the same entity. It simply groups rows under the exact label, such as "Nike" or "Apple", and provides a count for each group. The reviewer can then reconcile this group count against the original rows to verify the summary.

The workflow is designed to be used in an authorized analyst export, with the input file, brand-label mapping, report digests, and upstream collection window saved for review. It does not attempt to estimate market size, compare historical snapshots, or infer commercial importance from observation counts. The workflow assumes that upstream processes handle source verification, and that aliases are resolved before grouping.

If consolidation of brand labels is needed, the decision to map certain labels to a single reporting key should be made upstream.