AI for Engineering Knowledge Management
Discover how Leo AI and OpenBOM are redefining engineering workflows with Connected Product Memory.
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4 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
The future of engineering won't be about a single tool "doing it all." It will be defined by ecosystems where AI, CAD, BOM, and ERP systems connect through open APIs and shared data models.
OpenBOM emphasizes open data models, system integrations, and collaborative workflows.
Leo AI brings AI-driven reasoning and free-text Q&A into those workflows.
Together, they show how connected intelligence can empower engineers to work faster, reduce mistakes, and make smarter choices.
The numbers quantify the impact. Engineers spend 35% of their time designing parts that already exist. When they find the right part instead—whether internal or from 120M+ vendor options—BOM costs drop by 15% and time-to-market accelerates. Organizations running Leo report finding existing parts in their own vaults that would have cost thousands to custom-engineer.
The future of engineering won't be about a single tool "doing it all." It will be defined by ecosystems where AI, CAD, BOM, and ERP systems connect through open APIs and shared data models.
OpenBOM emphasizes open data models, system integrations, and collaborative workflows.
Leo AI brings AI-driven reasoning and free-text Q&A into those workflows.
Together, they show how connected intelligence can empower engineers to work faster, reduce mistakes, and make smarter choices.
The numbers quantify the impact. Engineers spend 35% of their time designing parts that already exist. When they find the right part instead—whether internal or from 120M+ vendor options—BOM costs drop by 15% and time-to-market accelerates. Organizations running Leo report finding existing parts in their own vaults that would have cost thousands to custom-engineer.
What Exists Today: AI + Cloud-Native BOM
Leo AI is built as an AI for engineers. It provides plain-language answers to technical questions, performs calculations and conversions, surfaces specs or past decisions, and even supports ideation. Instead of sifting through folders, engineers can simply ask and get precise, contextual results.
OpenBOM provides the data backbone. As a cloud-native product data, CAD, and BOM management system, it enables collaboration, version control, and structured integration of engineering and manufacturing information across distributed teams. Unlike traditional PDM and PLM software, it’s not about locking files—it makes data flow easily between teams, companies, and systems while keeping full control of the data lifecycle.
Together, these two platforms demonstrate how AI and cloud-native data platforms can already complement each other in engineering workflows.
IN PRACTICE
Challenges Along the Way
"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
The Vision: Connected Product Memory
The bigger leap forward is the concept of Connected Product Memory.
This idea goes beyond data storage. It means capturing not just what was decided, but why:
Recording the rationale behind every design choice;
Preserving knowledge across staff turnover and project handovers;
Using Leo’s Large Mechanical Model (LMM) to reason about complete assemblies, not just individual parts.
Connected Product Memory ensures that knowledge doesn’t die in archived emails or retired laptops. It becomes a living resource that informs every future decision.
Example in Action: AI + BOM Automation
Imagine an engineering team choosing a battery cell for a new device:
Leo AI interprets the technical requirements: high cycle life, thermal safety, compliance with transport regulations.
OpenBOM adds organizational context: Which cells passed qualification in previous projects? Which suppliers delivered reliably? What’s currently in stock, and what are lead times?
The recommendation isn’t just technically valid—it’s operationally correct for that company.
(Adapted from: OpenBOM Blog – Engineering Copilot & Product Memory)
Benefits for Engineering Teams
When AI intelligence is connected to BOM context, engineering teams gain:
Faster decision-making – engineers no longer wait days for answers buried in legacy systems.
Knowledge retention – expertise doesn’t leave when senior staff retire.
Error reduction – inconsistencies are flagged early in the design cycle.
Reuse of proven solutions – teams build on what worked instead of reinventing the wheel.
Smarter material/component selection – decisions factor in not just technical specs, but also cost, inventory, and supplier lead times.
FAQ
Stop Wasting Hours on Manual CAD Search
Leo AI turns your existing vault into a searchable knowledge base.
Leo AI connects to your PDM and makes every part findable by description in under 10 seconds. <a href="/onboarding">Try Leo Today</a>
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams
Stop Wasting Hours on Manual CAD Search
Leo AI turns your existing vault into a searchable knowledge base.
Leo AI connects to your PDM and makes every part findable by description in under 10 seconds. <a href="/onboarding">Try Leo Today</a>
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams