Case Study: Elbit Systems
Case Study: Elbit Systems
Case Study: Elbit Systems
Engineering at the Speed of Thought: How Elbit is Redefining Mechanism Design
"Leo gave me a variety of mechanisms that meet my requirements and ideas that were new to me, that I wouldn't think of otherwise. It feels like the world was opened and our minds were opened to different fields."
"Leo gave me a variety of mechanisms that meet my requirements and ideas that were new to me, that I wouldn't think of otherwise. It feels like the world was opened and our minds were opened to different fields."
Adi B.
Elbit Systems
About Elbit Systems
About Elbit Systems
Elbit Systems is Israel's largest defense technology company and a leading global provider of advanced solutions across aerospace, land, sea, and cyber domains. Publicly traded on both NASDAQ and the Tel Aviv Stock Exchange, Elbit develops, manufactures, and integrates unmanned aircraft systems, electro-optics hardware, electronic warfare systems, and night vision technologies.
The company employs approximately 20,000 people and operates in an environment where precision is not just a requirement, it is a matter of mission success.
Adi B. leads a high-level engineering team tasked with developing complex mechanical systems that must survive extreme conditions.
The Challenge: Fragmented data and Information Silos.
The Challenge: Fragmented data and Information Silos.
Before Integrating Leo, Adi’s team was fighting a battle against fragmented data.
The Search for Relevance: Standard search engines like Google Images flooded the team with "straightforward" or "irrelevant" pictures that ignored critical engineering requirements.
The Hallucination Hurdle: General AI tools often provided "too basic" information or, worse, "broken links" and references to sources that didn't exist when the team needed professional validation.
The Indexing Drain: Engineers were losing hours every month to the tedious task of manually indexing CAD parts, adding descriptions and tags just so the geometry could be found again later.
The Expert Bottleneck: If a mechanical engineer needed specific material data, they had to stop their workflow to contact a material expert in a different department, creating a cross-functional standstill.
Before Integrating Leo, Adi’s team was fighting a battle against fragmented data.
The Search for Relevance: Standard search engines like Google Images flooded the team with "straightforward" or "irrelevant" pictures that ignored critical engineering requirements.
The Hallucination Hurdle: General AI tools often provided "too basic" information or, worse, "broken links" and references to sources that didn't exist when the team needed professional validation.
The Indexing Drain: Engineers were losing hours every month to the tedious task of manually indexing CAD parts, adding descriptions and tags just so the geometry could be found again later.
The Expert Bottleneck: If a mechanical engineer needed specific material data, they had to stop their workflow to contact a material expert in a different department, creating a cross-functional standstill.
Before Integrating Leo, Adi’s team was fighting a battle against fragmented data.
The Search for Relevance: Standard search engines like Google Images flooded the team with "straightforward" or "irrelevant" pictures that ignored critical engineering requirements.
The Hallucination Hurdle: General AI tools often provided "too basic" information or, worse, "broken links" and references to sources that didn't exist when the team needed professional validation.
The Indexing Drain: Engineers were losing hours every month to the tedious task of manually indexing CAD parts, adding descriptions and tags just so the geometry could be found again later.
The Expert Bottleneck: If a mechanical engineer needed specific material data, they had to stop their workflow to contact a material expert in a different department, creating a cross-functional standstill.
Before Integrating Leo, Adi’s team was fighting a battle against fragmented data.
The Search for Relevance: Standard search engines like Google Images flooded the team with "straightforward" or "irrelevant" pictures that ignored critical engineering requirements.
The Hallucination Hurdle: General AI tools often provided "too basic" information or, worse, "broken links" and references to sources that didn't exist when the team needed professional validation.
The Indexing Drain: Engineers were losing hours every month to the tedious task of manually indexing CAD parts, adding descriptions and tags just so the geometry could be found again later.
The Expert Bottleneck: If a mechanical engineer needed specific material data, they had to stop their workflow to contact a material expert in a different department, creating a cross-functional standstill.
Before Integrating Leo, Adi’s team was fighting a battle against fragmented data.
The Search for Relevance: Standard search engines like Google Images flooded the team with "straightforward" or "irrelevant" pictures that ignored critical engineering requirements.
The Hallucination Hurdle: General AI tools often provided "too basic" information or, worse, "broken links" and references to sources that didn't exist when the team needed professional validation.
The Indexing Drain: Engineers were losing hours every month to the tedious task of manually indexing CAD parts, adding descriptions and tags just so the geometry could be found again later.
The Expert Bottleneck: If a mechanical engineer needed specific material data, they had to stop their workflow to contact a material expert in a different department, creating a cross-functional standstill.
The Solution: A Centralized Directory of Organizational Knowledge
The Solution: A Centralized Directory of Organizational Knowledge
The team integrated Leo to serve as the intelligent layer connecting their engineering departments. Unlike generalist tools, Leo was built by and for mechanical engineers, allowing it to understand the actual physics and geometry of their work.
Geometry Intelligence: One of the most significant "wow" moments for the team was Leo’s ability to recognize geometry directly from a CAD model. Leo understands if a part is flanged or not without any manual tagging, a feature Adi describes as "amazing" for its financial and operational implications.
Bridging the Departmental Divide: The studio utilized Leo to create a centralized directory of organizational knowledge. Now, when an expert like "Shimon" (a material specialist) uploads his proprietary research and "bibles" the entire organization can access those insights. Leo provides the answer and cites Shimon as the source, turning him into an internal hero while ensuring everyone makes decisions based on the same verified data.
Creative Explosion: By filtering out the noise and focusing on requirements, Leo AI suggested "miniature solutions" and mechanisms the team had never considered. This allowed Adi to brainstorm and expand his team's thinking into entirely new directions.
The team integrated Leo to serve as the intelligent layer connecting their engineering departments. Unlike generalist tools, Leo was built by and for mechanical engineers, allowing it to understand the actual physics and geometry of their work.
Geometry Intelligence: One of the most significant "wow" moments for the team was Leo’s ability to recognize geometry directly from a CAD model. Leo understands if a part is flanged or not without any manual tagging, a feature Adi describes as "amazing" for its financial and operational implications.
Bridging the Departmental Divide: The studio utilized Leo to create a centralized directory of organizational knowledge. Now, when an expert like "Shimon" (a material specialist) uploads his proprietary research and "bibles" the entire organization can access those insights. Leo provides the answer and cites Shimon as the source, turning him into an internal hero while ensuring everyone makes decisions based on the same verified data.
Creative Explosion: By filtering out the noise and focusing on requirements, Leo AI suggested "miniature solutions" and mechanisms the team had never considered. This allowed Adi to brainstorm and expand his team's thinking into entirely new directions.
The team integrated Leo to serve as the intelligent layer connecting their engineering departments. Unlike generalist tools, Leo was built by and for mechanical engineers, allowing it to understand the actual physics and geometry of their work.
Geometry Intelligence: One of the most significant "wow" moments for the team was Leo’s ability to recognize geometry directly from a CAD model. Leo understands if a part is flanged or not without any manual tagging, a feature Adi describes as "amazing" for its financial and operational implications.
Bridging the Departmental Divide: The studio utilized Leo to create a centralized directory of organizational knowledge. Now, when an expert like "Shimon" (a material specialist) uploads his proprietary research and "bibles" the entire organization can access those insights. Leo provides the answer and cites Shimon as the source, turning him into an internal hero while ensuring everyone makes decisions based on the same verified data.
Creative Explosion: By filtering out the noise and focusing on requirements, Leo AI suggested "miniature solutions" and mechanisms the team had never considered. This allowed Adi to brainstorm and expand his team's thinking into entirely new directions.
The team integrated Leo to serve as the intelligent layer connecting their engineering departments. Unlike generalist tools, Leo was built by and for mechanical engineers, allowing it to understand the actual physics and geometry of their work.
Geometry Intelligence: One of the most significant "wow" moments for the team was Leo’s ability to recognize geometry directly from a CAD model. Leo understands if a part is flanged or not without any manual tagging, a feature Adi describes as "amazing" for its financial and operational implications.
Bridging the Departmental Divide: The studio utilized Leo to create a centralized directory of organizational knowledge. Now, when an expert like "Shimon" (a material specialist) uploads his proprietary research and "bibles" the entire organization can access those insights. Leo provides the answer and cites Shimon as the source, turning him into an internal hero while ensuring everyone makes decisions based on the same verified data.
Creative Explosion: By filtering out the noise and focusing on requirements, Leo AI suggested "miniature solutions" and mechanisms the team had never considered. This allowed Adi to brainstorm and expand his team's thinking into entirely new directions.
The team integrated Leo to serve as the intelligent layer connecting their engineering departments. Unlike generalist tools, Leo was built by and for mechanical engineers, allowing it to understand the actual physics and geometry of their work.
Geometry Intelligence: One of the most significant "wow" moments for the team was Leo’s ability to recognize geometry directly from a CAD model. Leo understands if a part is flanged or not without any manual tagging, a feature Adi describes as "amazing" for its financial and operational implications.
Bridging the Departmental Divide: The studio utilized Leo to create a centralized directory of organizational knowledge. Now, when an expert like "Shimon" (a material specialist) uploads his proprietary research and "bibles" the entire organization can access those insights. Leo provides the answer and cites Shimon as the source, turning him into an internal hero while ensuring everyone makes decisions based on the same verified data.
Creative Explosion: By filtering out the noise and focusing on requirements, Leo AI suggested "miniature solutions" and mechanisms the team had never considered. This allowed Adi to brainstorm and expand his team's thinking into entirely new directions.
The Impact: From Days of Searching to Minutes of Designing.
The Impact: From Days of Searching to Minutes of Designing.
The impact of Leo at Elbit Systems was immediate and transformative:
Solutions in Minutes: What used to take days of conceptual "starting point" research is now done in minutes. Adi’s team can now build a massive array of potential solutions for a single problem almost instantly.
Reclaiming Engineering Capital: By automating the indexing of CAD parts, the system saves hours of manual labor every month for every single engineer on the team.
Total Technical Confidence: Because Leo provides direct links to relevant engineering books and images, the team feels confident in the solutions they choose, knowing they are grounded in verified engineering logic.
Organizational Connectivity: Leo has "connected the dots" across the company, allowing mechanical, environmental, and material engineers to share knowledge rather than just files.
The implementation of Leo AI has fundamentally changed how HP Indigo approaches the development of its next-generation platforms:
Accelerated Development Cycles: By reducing the "search time" for technical information, the team can focus more on active innovation and testing for the V12.
Enhanced Team Productivity: With dozens of engineers empowered by AI, the collective "IQ" of the organization is amplified. New team members can contribute faster, and senior engineers can focus on high-level strategy rather than answering repetitive technical questions.
Strategic Advantage: The ability to synthesize decades of HP Indigo’s proprietary research allows the team to maintain its competitive edge in the high-speed digital printing market.
The implementation of Leo AI has fundamentally changed how HP Indigo approaches the development of its next-generation platforms:
Accelerated Development Cycles: By reducing the "search time" for technical information, the team can focus more on active innovation and testing for the V12.
Enhanced Team Productivity: With dozens of engineers empowered by AI, the collective "IQ" of the organization is amplified. New team members can contribute faster, and senior engineers can focus on high-level strategy rather than answering repetitive technical questions.
Strategic Advantage: The ability to synthesize decades of HP Indigo’s proprietary research allows the team to maintain its competitive edge in the high-speed digital printing market.
The implementation of Leo AI has fundamentally changed how HP Indigo approaches the development of its next-generation platforms:
Accelerated Development Cycles: By reducing the "search time" for technical information, the team can focus more on active innovation and testing for the V12.
Enhanced Team Productivity: With dozens of engineers empowered by AI, the collective "IQ" of the organization is amplified. New team members can contribute faster, and senior engineers can focus on high-level strategy rather than answering repetitive technical questions.
Strategic Advantage: The ability to synthesize decades of HP Indigo’s proprietary research allows the team to maintain its competitive edge in the high-speed digital printing market.
The implementation of Leo AI has fundamentally changed how HP Indigo approaches the development of its next-generation platforms:
Accelerated Development Cycles: By reducing the "search time" for technical information, the team can focus more on active innovation and testing for the V12.
Enhanced Team Productivity: With dozens of engineers empowered by AI, the collective "IQ" of the organization is amplified. New team members can contribute faster, and senior engineers can focus on high-level strategy rather than answering repetitive technical questions.
Strategic Advantage: The ability to synthesize decades of HP Indigo’s proprietary research allows the team to maintain its competitive edge in the high-speed digital printing market.
Conclusion
For Adi and his team at Elbit Systems, Leo has moved the needle from "searching for information" to "innovating with information." By automating the tedious parts of the design process and centralizing years of organizational expertise, Leo has become a vital asset in the race to develop the next generation of defense technology.
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© 2026 Leo AI, Inc.
Subscribe to our engineering newsletter
Be the first to know about Leo's newest capabilities and get practical tips to boost your engineering.
Need help? Join the Leo AI Community
Connect with other engineers, get answers from our team, and request features.
© 2026 Leo AI, Inc.
Subscribe to our engineering newsletter
Be the first to know about Leo's newest capabilities and get practical tips to boost your engineering.
Need help? Join the Leo AI Community
Connect with other engineers, get answers from our team, and request features.
© 2026 Leo AI, Inc.
Subscribe to our engineering newsletter
Be the first to know about Leo's newest capabilities and get practical tips to boost your engineering.
Need help? Join the Leo AI Community
Connect with other engineers, get answers from our team, and request features.
© 2026 Leo AI, Inc.
