{"id":20550,"date":"2025-06-25T23:03:56","date_gmt":"2025-06-25T23:03:56","guid":{"rendered":"https:\/\/durolabs.co\/?p=20550"},"modified":"2025-08-22T11:32:20","modified_gmt":"2025-08-22T11:32:20","slug":"natural-language-search","status":"publish","type":"post","link":"https:\/\/durolabs.co\/blog\/natural-language-search\/","title":{"rendered":"Why You Need Natural Language Search in Your Workflows"},"content":{"rendered":"\t\t
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Natural language search gives engineers instant access to accurate product data. <\/span>Every decision, from design iterations to supplier selection, relies on knowing precisely what\u2019s in the system. <\/span><\/p>\n

Which parts are approved, which assemblies are under revision, what lead times and costs look like, and whether the data is current. <\/span>When that information is hard to find, progress stalls, errors creep in, and teams start working from assumptions instead of facts.<\/span><\/p>\n

In most PLM (Product Lifecycle Management<\/a>) systems, the search function is constrained by rigid filters, static views, and field-specific queries. Users need to memorize field names or learn SQL (<\/span>Structured Query Language<\/span>) just to perform routine lookups. If you don\u2019t know how the data is structured or which metadata field to search, you\u2019re out of luck.<\/span><\/p>\n

This extra work adds up, creating unnecessary friction in daily workflows like reviewing parts, checking revision status, and validating supplier information. Time spent navigating product hierarchies, filtering <\/span>bill of materials<\/a>, or requesting reports from system admins can easily consume hours each week.<\/span><\/p>\n

Natural language search offers a different model. Users can describe what they\u2019re looking for in plain language, and the system handles the translation. That means no special syntax, no workaround reports, and no digging through admin settings.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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What Is Natural Language Search?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Natural language search replaces rigid query inputs with a flexible, intent-driven interface. Instead of requiring users to know specific field names or database structures, it allows them to describe what they need in plain language and get back precise, relevant results.<\/span><\/p>

This is more than a UI update; it fundamentally changes how users interact with PLM data. A sourcing manager might ask, \u201cShow me all components added in the last 30 days over $500,\u201d<\/b><\/em> while an engineer looks for \u201cassemblies waiting for release approval<\/em>.\u201d<\/b> The system parses the request, maps it to the appropriate fields and logic, and returns structured data, so no manual filtering or post-processing is required.<\/p>

By translating everyday language into structured queries, natural language search removes one of the most significant barriers to data access. It allows engineers, program managers, and supply chain teams to get the information they need, when they need it, without interrupting workflows or escalating to PLM admins.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Natural language search in PLM<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Why Most PLM Search Slows Teams Down<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Accessing product data should be fast and intuitive, but traditional PLM systems bury this data behind rigid filters, inconsistent field naming, and limited query capabilities. Without knowing exactly how the data is structured, engineers are left guessing.<\/span><\/p>

As a result, tracing how a subassembly rolls into a top-level product requires exporting data and reconstructing the hierarchy offline. Searching for all assemblies that include a specific component involves a series of manual clicks, cross-referencing part numbers<\/a> across views, or submitting a request for a custom report from a system admin.<\/span><\/p>

These slowdowns compound quickly. They hinder\u00a0<\/span>BOM management<\/a>, interrupt workflows, obscure critical information, and create unnecessary friction between teams. When engineering, sourcing, and operations can\u2019t access the same product structure in real time, decisions get delayed, and mistakes get made.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Natural Language Search Examples<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The contrast between traditional search methods and natural language search is most obvious in practice. Here\u2019s how common tasks change when intent drives the query:<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Use Case<\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Traditional PLM Search<\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Natural Language Search<\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Find recent high-cost parts<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Set multiple filters across cost and date fields, sort manually<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201cShow parts over $1000 added this quarter\u201d<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Track release status of assemblies<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Manually check approval fields across records<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201cList assemblies still waiting for release\u201d<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Identify where a component is used<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Run multi-level BOM reports, cross-reference part numbers<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201cWhere is part 100-00004 used?\u201d<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Flag sourcing risk by supplier type<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Export BOM, merge with supplier data, filter manually<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201cList components with overseas suppliers and long lead times\u201d<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Powered by LLMs, Tuned for Engineering Context<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Natural language search in an AI-native PLM like\u00a0<\/span>Duro Design<\/span><\/a>\u00a0is built on <\/span>large language models<\/span><\/a> (LLMs) trained to interpret plain language in the context of structured engineering data.<\/span><\/p>

These models recognize intent even when phrasing is imprecise, and they understand common synonyms across roles \u2013 <\/span>\u201crelease\u201d vs. \u201capprove,<\/em>\u201d \u201ccomponent\u201d vs. \u201cpart<\/em>,\u201d \u201csupplier<\/em>\u201d vs. \u201cvendor.<\/em>\u201d<\/b> They also infer relationships between parts, assemblies, revisions, sourcing metadata, and change orders<\/a>, allowing users to ask complex, multi-dimensional questions without breaking them into filterable fields.<\/span><\/p>

Unlike bolt-on AI tools that operate outside the PLM, Duro Design\u2019s natural language engine is built into the system itself. Queries run directly against your live product data, so the results reflect real-time status, not an approximate snapshot.<\/span><\/p>

Duro Design also shows its interpreted query logic alongside the results to prevent miscommunication between what a user asks and what the system returns. For example, if a user types <\/span>\u201c<\/b>Show me assemblies pending release<\/b>,<\/em>\u201d they will see Duro Design\u2019s interpretation below the search as Status = ‘Pending<\/strong>‘ AND Type = ‘Assembly<\/strong>\u2019<\/span>.<\/span><\/p>

This feedback loop improves clarity, builds trust, and makes it easier to refine queries on the fly, without needing to escalate to a PLM admin or second-guess the data. <\/span>As usage grows, the system learns. It adapts to the terminology, phrasing, and patterns unique to your team, continuously improving precision across roles and workflows.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Natural language search in PLM<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Business Impact: Real-World Use Cases<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Natural language search reduces friction across the product lifecycle, and that friction matters. When engineers can\u2019t access the data they need, decisions stall.\u00a0<\/span><\/p>

When sourcing managers have to chase down part details or lead time information, supplier negotiations lose momentum. When teams operate on outdated or inconsistent information, production slows, and errors multiply.<\/span><\/p>

Natural language search powered by AI avoids bottlenecks at the source by\u00a0<\/span>making product data accessible\u00a0<\/a>through plain language. The result is faster decisions, better alignment across functions, and fewer preventable delays.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The business outcomes are tangible:<\/span><\/p>