Legacy Keyword Search Is Failing Modern Teams

For years, search and recommendation engines have relied on simple keyword matching — product titles, short descriptions, and tags.
But as catalogs grow and user expectations rise, this approach breaks down.

Customers don’t type the same words your catalog uses. They search by intent, need, or context.
If your data isn’t structured and enriched enough to understand those signals, even the best search algorithm can’t connect users to what they want.

That’s why the next generation of product discovery isn’t about better search boxes.
It’s about better data.

The Real Challenge: Moving Beyond the Product Title

Most teams underestimate the complexity of their own data.
Behind every “red cotton shirt” lies a mess of supplier descriptions, inconsistent formats, missing fields, and outdated attributes.

To deliver true discovery, companies must:

• Generate additional attributes (color, material, use case, fit, sustainability, etc.)

• Normalize and clean data from multiple sources

• Classify and cluster products in user-friendly ways

• Make data understandable to both humans and AI systems

These steps sound simple — but doing them at scale, across thousands of SKUs, is painful, manual, and error-prone.
This is where most search and merchandising initiatives stall.

Why Enriched Data Drives Conversions

Rich product data does more than make your catalog searchable.
It powers relevance. It drives recommendations. It informs analytics.

When your data is complete and normalized, every user action — search, filter, click, or purchase — becomes a stronger signal for personalization.
Teams that invest in enrichment see higher engagement, better conversion rates, and improved decision-making across merchandising and marketing.

Claro: From Messy Metadata to Meaningful Attributes

Claro is built for teams that want to keep their search stack in control — but evolve beyond keyword-based discovery.

With just a few clicks, Claro allows you to:

• Automatically generate additional attributes (materials, categories, usage contexts, etc.)

• Normalize messy data into a consistent structure

• Classify and enrich products using AI models optimized for your catalog

• Build feedback loops that continuously improve accuracy over time

Claro integrates seamlessly via API or spreadsheet-like UI, so both technical and non-technical teams can take full control of their data enrichment pipeline — no retraining models or rebuilding infrastructure.

Why This Matters Now

In an era where customers expect intuitive, context-aware search, staying keyword-based means falling behind.
The companies winning today aren’t just optimizing search — they’re optimizing understanding.

If your products aren’t enriched, normalized, and classified in user-friendly ways, your users won’t find them — and your AI won’t either.

Let’s Build the Future of Discovery

Claro helps organizations move from fragmented, manual enrichment to automated, intelligent data orchestration.
So you can focus on what matters most: delivering products and experiences users actually connect with.

👉 Learn more at getclaro.ai