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."

Adi B.

Elbit Systems

The Mission: High-Stakes Innovation.

As a global leader in defense technology, Elbit Systems operates in an environment where precision is not just a requirement, it is a matter of mission success.

Adi leads a high-level engineering team tasked with developing complex mechanical systems that must survive extreme conditions. Their daily reality involves solving for grueling constraints, such as designing mechanisms for linear motion that can handle a 12 mm stroke with 500 N loading, or even more intense requirements like an 8 mm stride with a massive 8 kN load.

The Bottleneck: The "Google Gap" and Information Silos.

Before integrating Leo, Bashari’s team was fighting a war against fragmented data. Traditional tools were failing the specialized needs of mechanical engineers:

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 Breakthrough: Leo as the "Standardized Hub".

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 Bashari 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 Bashari to brainstorm and expand his team's thinking into entirely new directions.