AI for Engineering Knowledge Management
Engineers waste hours searching PDM systems for parts they know exist. Learn why traditional PDM search fails and how AI-powered search is solving the problem.
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5 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 and Mechanical Engineer in an elite military intelligence, Maor leads Leo AI's mission to transform how engineering teams design better products faster.
BOTTOM LINE
PDM search has been broken for years, and most engineering teams have simply accepted the workarounds -- the hallway conversations, the personal spreadsheets, the parts designed from scratch because nobody could find the one that already existed. AI-powered search changes that equation entirely.
The technology to search engineering data by meaning, by geometry, and by plain language questions is here today. Organizations that adopt it are already seeing measurable improvements in part reuse, design speed, and knowledge retention. The rest are paying a hidden tax on every project -- one that grows with every file added to a vault nobody can search.
Every mechanical engineer knows the feeling. You need a bracket, a fastener, a housing -- something you are almost certain your team designed two years ago. You open your PDM system, type in a few keywords, and get either zero results or a thousand irrelevant ones. So you do what engineers have done for decades: you walk down the hall, send a Slack message, or just design the part from scratch.
This is not a niche complaint. It is one of the most common frustrations in mechanical engineering today, and it shows up constantly in online forums, team retrospectives, and vendor feedback sessions. The search capabilities built into most PDM and PLM systems were designed for a different era -- one where part numbers were sacred, folder structures were gospel, and the idea of searching by geometry or plain language was science fiction.
The result is a growing gap between the volume of engineering data organizations produce and their ability to actually find and reuse it. Parts get redesigned. Costs go up. Timelines slip. And the institutional knowledge that should be an organization's greatest asset becomes its most underutilized one.
The Real Cost of Bad PDM Search
Most engineering teams do not think of search as a cost center, but the numbers tell a different story. Studies consistently show that engineers spend between 20 and 30 percent of their working time searching for information -- drawings, specifications, past designs, supplier data, and material properties. When the search tool they rely on cannot surface what they need, that time gets spent on workarounds.
The most expensive workaround is part duplication. When an engineer cannot find an existing bracket that fits their envelope constraints, they design a new one. That new part needs its own drawing, its own BOM entry, its own tooling, and its own procurement cycle. Multiply that across dozens of projects and hundreds of engineers, and the downstream costs -- in manufacturing, inventory, and quality management -- become staggering.
There is also a less visible cost: the erosion of institutional knowledge. When experienced engineers retire or change roles, their mental index of where things are goes with them. The PDM system was supposed to be the organizational memory, but if nobody can find what is in it, that memory is effectively lost.
Beyond direct costs, poor search creates a culture of learned helplessness. Engineers stop trusting the system. They build personal folders, maintain private spreadsheets of part numbers, and rely on tribal knowledge networks that are fragile by nature. The PDM system becomes a repository rather than a resource -- a place where files go to be stored, not to be found.
IN PRACTICE
The connection to our PDM and using that as a data source is legit the best thing ever. I found three viable bracket options fitting my exact envelope constraints -- in minutes, not days.
Eytan S., R&D Engineer
Why Traditional PDM Search Falls Short
The core problem is architectural. Most PDM systems -- SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter -- were built as data management platforms, not knowledge retrieval systems. Their search engines are essentially database queries wrapped in a user interface.
This means search works well only when you know exactly what you are looking for. If you have the part number, the file name, or the exact metadata string, you will find it. But engineering work rarely starts with that level of specificity. More often, an engineer has a functional requirement or a geometric intuition about what they need.
Traditional PDM search cannot handle either of these. It does not understand geometry. It does not understand engineering context. It does not know that a mounting bracket and a support frame might serve the same function. And it certainly cannot look at a 3D model and find visually similar parts in the vault.
Metadata dependence compounds the problem. Search quality is only as good as the metadata engineers enter when they check files in. In practice, metadata entry is inconsistent at best and nonexistent at worst. Engineers are under time pressure, naming conventions drift between teams, and nobody wants to spend fifteen minutes tagging a file when there is a design review in an hour. The result is a vault full of CAD files with incomplete, inconsistent, or missing metadata -- and a search engine that cannot find what it cannot index.
What Engineers Actually Need From Search
The gap between what engineers need and what PDM search delivers is not subtle. Engineers are asking for capabilities that feel obvious in 2026 but remain absent from most engineering data management systems.
First, they need natural language search. An engineer should be able to type "aluminum housing with IP67 rating used in outdoor sensor assemblies" and get meaningful results. This is how every other search experience works -- from shopping to research to code repositories -- yet PDM systems still demand structured queries built on exact field matches.
Second, they need geometry-aware search. The ability to upload a 3D model or a sketch and find similar parts in the vault is not a luxury feature. It is fundamental to part reuse, and it is the kind of capability that saves real money at scale. Text-to-CAD search (describing a part and finding matching geometry) and CAD-to-CAD search (using a model to find similar models) should be standard, not experimental.
Third, they need contextual understanding. When an engineer asks about a bearing housing, the system should understand the engineering context well enough to surface related assemblies, associated drawings, relevant specifications, and prior design decisions -- not just files with the word bearing in the name.
Fourth, they need cross-system visibility. Engineering knowledge does not live in one place. It is spread across PDM vaults, PLM workflows, ERP records, shared drives, email threads, and the heads of senior engineers. A useful search tool needs to reach across all of these, not just the files managed by one system.
How AI Is Changing Engineering Search
Artificial intelligence -- specifically, large language models trained on engineering data -- is making it possible to close the gap between what engineers need and what their tools deliver. This is not a theoretical future. AI-powered engineering search tools exist today and are already being used by teams ranging from small design shops to large defense contractors.
The shift works on several levels. AI enables semantic search, meaning the system understands intent rather than just matching keywords. When an engineer types "corrosion-resistant fastener for marine applications," an AI-powered system can interpret the functional requirements and surface relevant parts even if the word "marine" never appears in the file metadata.
Geometry-aware AI goes further. By analyzing the 3D geometry of CAD files, these systems can find parts that are visually or functionally similar -- even across different CAD formats, naming conventions, and vault structures. An engineer can describe a part in plain language and get CAD results, or upload a model and find matches across the entire organization's design history.
Perhaps most importantly, AI can index and make searchable the unstructured knowledge that traditional systems ignore: design rationale captured in meeting notes, engineering change order justifications, email discussions about material trade-offs, and the accumulated reasoning behind thousands of past design decisions. This transforms a static file vault into a living, queryable knowledge base.
Leo AI, for example, connects directly to leading PDM and PLM platforms -- including SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, and Arena PLM -- and layers an AI-powered intelligence system on top. Engineers can ask questions in plain language and get answers drawn from their organization's full knowledge base, complete with source citations they can verify. The system does not replace the PDM; it makes the PDM actually useful for retrieval.
From Search Tool to Engineering Intelligence
The real opportunity is not just better search. It is a fundamental shift in how engineering organizations relate to their own accumulated knowledge.
When search works, part reuse increases. When part reuse increases, BOM costs decrease, procurement cycles shorten, and design quality improves because engineers are building on proven components rather than starting from blank canvases. The ripple effects touch every phase of the product development cycle.
When search works, senior engineers get their time back. Instead of being interrupted five times a day with questions about where the spec for something is, they can focus on the high-value engineering judgment calls that actually require their experience. The AI handles the retrieval, and humans handle the decisions.
When search works, onboarding accelerates. New engineers do not need to spend months building a mental map of where everything lives. They can ask the system and get answers immediately, with full traceability to the source documents. This is particularly critical in industries with high turnover or large contract workforces.
The organizations that figure this out first will have a measurable competitive advantage -- not because they have better engineers, but because their engineers can access and build on everything the organization has ever learned. That is the real promise of AI in engineering: not replacing human expertise, but making sure it is never lost, never siloed, and always findable.
FAQ
Find Any Part in Seconds
AI-powered search across your entire engineering knowledge base.
Leo AI connects to your PDM and PLM systems to make every part, drawing, and design decision instantly searchable. Stop losing time to broken search.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams
Find Any Part in Seconds
AI-powered search across your entire engineering knowledge base.
Leo AI connects to your PDM and PLM systems to make every part, drawing, and design decision instantly searchable. Stop losing time to broken search.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams