AI powered brand detection

At Launchmetrics, data quality is at the core of our mission: helping fashion, lifestyle, and beauty brands accurately measure the impact of their communication strategies.
To continue improving reliability, we are introducing a new AI-driven approach to brand detection, designed to better handle complex and ambiguous brand contexts.

The challenge: complex brands, ambiguous contexts

Many brands operate across multiple languages and industries, or share their name with cultural references, places, or concepts. Historically, this has required:

• long and complex keyword lists
• detailed inclusion and exclusion rules
• frequent manual updates to maintain accuracy

This rule-based approach can introduce data noise and missed mentions.

A shift from rules to context

Our new approach marks a fundamental shift in how brand coverage is detected.
Rather than relying on complex query logic, it uses contextual understanding powered by AI.

This evolution is built on three key principles:

1. Simplified brand queries

Brand detection starts from a generic query, without multiplying keywords or creating rigid inclusion and exclusion rules. This significantly reduces complexity and ongoing maintenance.

2. Brand descriptions to provide context

Each brand is enriched with a clear, structured description that provides the AI model with essential context, including:

• what the brand represents
• its product categories and universe
• what should not be associated with the brand

This allows the model to correctly interpret mentions, even in ambiguous situations.

3. AI-driven relevance decisions

Using this contextual information, the AI model (LLM) determines whether a brand mention is genuinely relevant to the brand. Relevance is no longer decided solely by static rules, but by semantic understanding.

4. Manual quality checks

To maintain the highest level of quality, we also perform manual checks on top-performing and high-impact documents, adding an additional layer of validation where it matters most.

What this means for brands

This new AI-driven approach delivers tangible benefits:

• Fewer missed mentions, thanks to better contextual detection
• Less noise, with more accurate exclusion of irrelevant content
• Higher overall data quality, especially for brands with ambiguous names
• Simpler upkeep, with fewer manual updates

The result: more reliable insights and greater confidence in brand performance measurement.