How Unvired helped a leading equipment manufacturer transform catalog discovery using natural language search, semantic retrieval, and AI-powered search intelligence.

Over the years, organizations have invested millions in ERP systems, CRM platforms, product catalogs, document repositories, engineering databases, and knowledge management systems. Yet employees still struggle to answer seemingly simple questions:

• Which product configuration best meets a customer’s requirements?
• Has a similar design been created before?
• Which assets contain a specific component?
• What configurations meet a particular set of technical constraints?

The problem isn’t the data. The problem is search. And more specifically, the way traditional enterprise search has been designed.

The Real Cost of Filter-Only Search

Traditional enterprise search is built around structured filters.

• Dropdowns for categories.
• Checkboxes for attributes.
• Sliders for price ranges.
• Keyword searches against predefined fields.

This works when users already know exactly what they’re looking for.

It breaks down when they don’t. The deeper challenge is vocabulary mismatch.

Users think in terms of customer requirements, technical specifications, business outcomes, or product characteristics.

Search systems think in terms of database fields. These are not the same language.

Every incorrect filter selection narrows results unnecessarily. Every missed attribute hides potentially relevant information. Every failed search increases frustration.

The catalog may contain exactly the right answer, but the user never finds it.

What Changes When You Add an AI Search Layer?

The move from filter-based search to intent-based search isn’t simply a user experience improvement.

It’s an architectural shift.

Instead of translating requirements into filters, users describe what they need in plain language.

The AI interprets intent and automatically generates:

• Search filters
• Query expressions
• Sorting parameters
• Semantic retrieval instructions

The user doesn’t see any of this complexity.

They simply see relevant results.

The operational improvement is significant. What previously required 20–30 minutes of manual filtering can often be reduced to seconds.

But the bigger benefit is discovery. Users begin finding products, designs, and configurations they didn’t know existed because the search engine understands concepts, not just keywords.

A Real-World Example: How an AI Search Agent Transformed an Equipment Manufacturer’s Design Catalog from Filter-Driven to Intent-Driven

At Unvired, we recently helped a leading equipment manufacturer modernize its internal design search platform.

The organization maintained thousands of product designs and configurations used by sales teams to respond to customer requirements. Although the information existed, finding the right design often required extensive filtering, browsing, and manual comparison.

By implementing an AI-powered search architecture that combined Natural Language Search, Dynamic Catalog Grounding, Component-Level Intelligence, and Hybrid Retrieval, the organization transformed search from a filter-driven process into an intent-driven experience.

The result:

• Approximately 90% reduction in filter interactions
• Search results are delivered in seconds instead of minutes
• Significantly improved catalog utilization
• Better discovery of relevant product configurations

The Hidden Problem: Data That Can’t Be Searched

Natural language search alone doesn’t solve everything.

Most enterprise catalogs contain two types of information:

1. Label-Level Data: This is the information typically indexed and searchable:

• Product name
• Category
• Description
• Price
• Product family

2. Component-Level Data: This is often the information users care about most:

• Components
• Materials
• Specifications
• Quantities
• Technical attributes
• Bills of Materials

In many organizations, component-level information lives in separate databases and never becomes part of the search experience. As a result, users can search based on what a product is called, but not based on what it actually contains.

The solution is component-level index enrichment. By joining detailed component information with searchable catalog data, organizations dramatically expand the searchable surface area of their information.

Every search becomes richer and more precise.

The Third Problem Nobody Talks About: The AI Doesn’t Know Your Catalog

There’s another challenge that sits between natural language search and reliable results.

The AI doesn’t know your business. Large language models understand language.

They do not automatically understand:

• Product families
• Business terminology
• Internal aliases
• Category definitions
• Technical classifications
• Naming conventions

Without context, the model is forced to make assumptions. Sometimes those assumptions are correct. Sometimes they are not. The result is inconsistent search behavior and unreliable results.

Dynamic Catalog Grounding: The Missing Piece

One of the most important innovations in our implementation was Dynamic Catalog Grounding.

Instead of relying on static prompts, the AI dynamically ingests current catalog information directly from enterprise systems.

This includes:

• Product families
• Product aliases
• Categories
• Features
• Component families
• Technical classifications

Because the AI works from actual business data, it remains aligned with the organization’s evolving catalog. When new products, categories, or terminology are introduced, the AI automatically adapts.

No prompt redesign. No manual maintenance. No stale assumptions.

This is often the difference between an AI search solution that works in demonstrations and one that succeeds in production.

Why Hybrid Retrieval Matters?

Enterprise search cannot rely on a single retrieval method. Keyword search alone misses conceptual relationships. Semantic search alone may miss exact business requirements. Structured filtering alone cannot capture user intent.

The strongest implementations combine all three.

Hybrid Retrieval Includes:

• Structured metadata filtering
• Keyword search
• Semantic vector search
• Component-level search

This enables users to search using exact values, technical specifications, business terminology, or conceptual requirements—all within the same experience.

The result is better relevance, broader discovery, and more accurate search outcomes.

Why GPT-4 Improved Search Performance?

During implementation, multiple language models were evaluated for query translation and search generation. While smaller models performed adequately for straightforward requests, enterprise search often requires:

• Complex filter generation
• Large catalog context handling
• Numerical reasoning
• Unit conversion logic
• Multi-criteria query interpretation

Moving from GPT-4 Mini to GPT-4 improved search accuracy, query consistency, and overall search reliability, particularly for complex requests spanning multiple search dimensions.

AI Needs Guardrails

One reality of enterprise AI is that language models occasionally make mistakes.

In a search environment, those mistakes typically appear as:

• Invalid filter syntax
• Incorrect operators
• Ambiguous references
• Numerical interpretation issues
• Formatting errors

Without validation, these errors can lead to failed searches and poor user experiences.

A production-grade AI search platform includes a guardrail layer that validates, repairs, and optimizes AI-generated queries before execution. This layer is invisible to users but essential for reliability.

The difference between a proof-of-concept and an enterprise-ready AI search solution often comes down to the quality of these guardrails.

Key Takeaways:

This demonstrates a broader principle: the richest data in an enterprise system is often invisible because it’s stored in the wrong place to be searched. Joining component-level data into a unified search index — grounding the AI in live catalog vocabulary — and wrapping it all in a natural language interface doesn’t just improve search. It changes what’s possible in a sales conversation.

When a rep can find the right design in seconds instead of minutes, they spend more time qualifying, customizing, and closing — and less time navigating databases. That’s the real return on an AI search investment.

About Unvired

At Unvired, we help organizations build AI-powered Search and AI Agent solutions that connect users to the right information across product catalogs, engineering systems, enterprise applications, and knowledge repositories.

If your teams struggle to find information buried inside enterprise systems—or if your current search experience relies on endless filters and keyword searches—we would love to talk.

Contact Unvired to explore how AI-powered Search and AI Agents can help your organization improve productivity, accelerate decision-making, and unlock more value from the information you already have.