Semantic Gap: Can agents truly understand product value?

We often assume that because Large Language Models (LLMs) can "read," they understand. This is a dangerous fallacy.

An LLM processing your product page doesn't "know" what a "cozy, vibey atmosphere" is in the way a human does. It processes statistical correlations between tokens. This disconnect between human intent and machine representation is the Semantic Gap.

If you sell complex products—configurable software, luxury goods, industrial machinery—this gap is where sales are lost. If an agent cannot map your product's features to its principal's desires via mathematical proximity, you do not exist.

The Limitation of Keywords

Traditional search matches string "A" to string "B".

• User searches: "Red dress"
• Database matches: "Red dress"

Agentic search matches intent to properties.

• Agent searches: "Attire suitable for a sombre evening gala that isn't too flashy."
• Database must match: "Midnight blue velvet gown."

There is no keyword overlap there. The connection is purely semantic. A keyword engine fails here 100% of the time.

Bridging the Gap with Embeddings

To bridge the gap, we must translate our catalog into Vector Embeddings. We take the "human" description of a product, combine it with structured specs, reviews, and usage data, and pass it through an embedding model (like OpenAI's text-embedding-3). The result is a high-dimensional vector that places that product in a conceptual space.

Strategies

Conclusion: Next Steps

We are not just organizing data; we are building a framework that gets agentic AI closer to understanding value and utility.

By investing in semantic infrastructure, we ensure that the unique, intangible qualities of your brand—your "vibe," your quality, your ethos—are preserved when translated into the digital mind of an agent buyer.