AI for Engineering Knowledge Management
Teamcenter search fails when engineers don't know the exact part number. Here is why it happens and how AI fixes part findability.
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8 min read
Dr. Maor Farid
Maor Farid is the Co-Founder and CEO of Leo AI, the first AI platform purpose-built for mechanical engineers. He holds a PhD in Mechanical Engineering and completed postdoctoral research at MIT as a Fulbright fellow. A Forbes 30 Under 30 honoree and former AI researcher, Maor leads Leo AI's mission to help engineering teams design better products faster.
BOTTOM LINE
Teamcenter search is reliable for retrieval and limited for discovery, because keyword and attribute queries depend on metadata that is often inconsistent and cannot see geometry at all. The cost shows up as wasted hours and duplicate parts that carry overhead for years. An AI intelligence layer that adds semantic and geometric search on top of your existing PLM closes that gap without a migration, turning your engineering history into something every engineer can actually find and reuse.
Ask any engineer who has lived inside a large PLM deployment and you will hear the same complaint: Teamcenter search works beautifully when you already know what you are looking for, and barely at all when you do not. If you have the part number, the item ID, or the exact file name, results appear in seconds. If you only know that a bracket like this one was designed two programs ago by someone who has since left, you are on your own. This article looks at why Teamcenter search struggles with discovery, what it costs engineering teams, and how an AI intelligence layer changes the equation without ripping out the PLM you already run.
Why Teamcenter search struggles with discovery
Teamcenter is a capable system of record. Its search is built on attribute queries and full text indexing (Active Workspace uses an Apache Solr index populated by the Teamcenter Full Text Search indexer). That architecture is excellent at retrieval, returning a known object from a precise query, and weak at discovery, surfacing the right object when the query is approximate.
The reasons are structural rather than a configuration mistake:
Search depends on metadata that humans entered. If a part was never classified, or was classified inconsistently, it will not appear under the category you expect.
Full text matching is lexical. It matches tokens and phrases, so a search for "clamp" will miss a part someone named "retaining fixture" even when they are functionally identical.
Geometry is invisible to text search. Two parts can be nearly identical in shape and completely different in their part numbers, descriptions, and file names.
Naming and classification drift across decades, sites, acquisitions, and migrations, so the same concept lives under many labels.
None of this is unique to one vendor. It is the nature of keyword and attribute search applied to engineering data, a problem we have written about in the context of why PDM search leaves engineers unable to find parts. The result is predictable: when finding a part takes longer than recreating it, the engineer recreates it.
IN PRACTICE
The search in Teamcenter has always been a weak point for us. If you don't know the exact part number or file name, you're basically not finding it. Leo changed that. I can describe a part geometrically or by function and it finds relevant parts from our own history, not just from an external catalog.
Verified User, Defense and Space Enterprise
The hidden cost of parts you cannot find
Failed search is not a minor annoyance. It is a steady tax on engineering throughput, and the numbers from independent surveys are sobering.
The McKinsey Global Institute reported that knowledge workers spend roughly 1.8 hours per day, about 9.3 hours per week, searching for and gathering information.
A 2022 survey by the parts management firm CADENAS, covering more than 100,000 engineers and designers, found that nearly half spent at least one hour every day searching for parts.
Industry analyses of part proliferation consistently estimate that a meaningful share of new part numbers created each year are functional duplicates of parts that already exist, each one carrying its own qualification, tooling, inventory, and maintenance overhead downstream.
The compounding effect matters most. A duplicate part is not a one time cost. It triggers a new qualification cycle, new supplier setup, additional inventory to carry, and another item to maintain for the life of the product. Multiply that across a year of programs and the avoidable spend is significant. We break the mechanics down further in our look at the real cost of bad PDM search. The root cause is almost always the same: the existing part was findable in theory and unfindable in practice.
What good part findability actually requires
If lexical and attribute search alone cannot solve discovery, what does a complete solution look like? Three capabilities have to work together.
Semantic search, so a query expressed in plain language matches parts by meaning and function rather than by exact wording. An engineer should be able to ask for "a stainless bracket that mounts a sensor to a 40 millimeter rail" and get relevant results.
Geometric search, so a part can be found by its shape. Drag in a model or sketch the form and the system returns visually similar parts regardless of how they were named or classified.
History awareness, so the system reasons over the company's own accumulated record (past CAD files, specifications, and the decisions behind them) rather than treating each query as if the organization had no memory.
These capabilities also depend on clean data exchange underneath. Standards such as ISO 10303, known as STEP, exist precisely so that product model data can move between CAD, PDM, and PLM systems without loss. STEP is a large family of standards (subdivided into roughly 700 parts) that makes geometry and product structure portable, which is what lets a modern search layer read across heterogeneous engineering data. For teams weighing where these capabilities belong, our guide to PLM versus PDM is a useful starting point.
How Leo adds an AI search layer on top of Teamcenter
Leo is an AI intelligence layer that sits on top of the PLM you already run. It does not replace Teamcenter, and it does not ask you to migrate your data. It connects to PLM, PDM, local and network directories, and ERP, then adds natural language and geometric search across your full engineering history. Integrations are available for Siemens Teamcenter, PTC Windchill, SolidWorks PDM, Autodesk Vault, Arena PLM, and other systems.
The value driver is part reuse. Before generating new geometry, Leo prioritizes parts you have already designed or purchased, drawing on your own history plus more than 120 million vendor options. Instead of relying on whether a part was classified correctly years ago, an engineer can describe a part by function or by shape and Leo surfaces relevant matches from the company's own record. That is the difference one engineering team described directly.
The platform was trained on more than one million pages of engineering standards, books, and articles, so its understanding of parts and terminology reflects engineering practice rather than generic web text. On the security side, Leo is SOC 2 certified and GDPR compliant, no AI is trained on your data, and your intellectual property is never shared. For a closer look at this approach in a Teamcenter context, see our overview of AI for Siemens Teamcenter PLM search.
Making the change without disrupting your PLM
The strongest argument for an AI layer is that it is additive. Your part numbers, revisions, workflows, and access controls stay in Teamcenter, where they belong. The search and reuse experience improves on top of them. That keeps the change low risk and avoids the disruption of a migration project.
A practical rollout looks like this:
Connect the layer to your existing PLM, PDM, network directories, and ERP so it can read across the full record.
Let engineers query in plain language and by geometry, and measure how often searches now end in a reused part rather than a new one.
Track the downstream effect on duplicate part creation, qualification effort, and BOM cost over a few release cycles.
Better findability also strengthens institutional memory, since the rationale behind past parts becomes searchable rather than trapped in the heads of people who may have moved on. That is the broader goal we describe in our work on engineering knowledge management. The point is not to fight Teamcenter. It is to give it the discovery layer it was never designed to provide.
FAQ
McKinsey Global Institute, The Social Economy (2012), supports the finding that knowledge workers spend about 1.8 hours per day searching for and gathering information.
CADENAS engineer and designer survey (2022), supports the figure that nearly half of more than 100,000 respondents spent at least an hour a day searching for parts.
NIST, Introduction to ISO 10303, the STEP Standard for Product Data Exchange, supports the description of STEP as a roughly 700 part standard for exchanging product model data across CAD, PDM, and PLM systems.
Siemens Teamcenter Full Text Search documentation, supports the description of Teamcenter search as attribute and full text indexing built on Apache Solr.
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