We Make It Work.

We help manufacturers bridge the gap between legacy operations and modern capability .

R Sigma WAS founded in 2023 by a team with over half a century of collective, hands-on experience in emerging technologies, program management, corporate strategy, and partnerships across leading aerospace and automotive OEMs.

R Sigma IS an engineering consulting and development firm that draws on operational expertise to deconstruct complex problems, establish ground truth, and bridge legacy systems with frontier technologies.

R Sigma HAS partnered with aerospace and semiconductor manufacturers to deliver scalable systems powered by machine learning, computer vision, robotics, and data science — modernizing operations and enabling industrial agility.

// Trusted by Precision Industries

// Field Notes

Real engagements, right-sized engineering.

Short notes from inside the work — how we choose what to build, what to leave alone, and where the actual leverage hides.

Aerospace White/black light nondestructive testing (NDT) of helicopter spars

Legacy systems can meet modern expectations — if you isolate what’s missing and rebuild only what matters.

A flight safety visual inspection system, critical to ongoing operations, had become outdated. While it still functioned, it lacked modern features such as intuitive camera layouts and the ability to record and display video simultaneously. Inspectors were forced to work around these limitations, slowing assessments and increasing cognitive burden. The original vendor no longer supported the platform, leaving no clear upgrade path.
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Semiconductor Assembly of extreme UV lithography reticle support system

Instrumentation-first automation delivers immediate safety and efficiency gains while laying the foundation for future robotics.

A high-precision assembly process required technicians to manually align and install sensitive components within a large workspace. Positional tolerances were sub-millimeter, and the work often demanded extended reaching, twisting, or working at height. While experienced teams performed the task reliably, the process carried ergonomic risks, relied heavily on verbal coordination, and lacked objective traceability.
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Aerospace Helicopter component design improvement and quality disposition

Digital twins of legacy parts don’t have to start from scratch — semantic overlays on trusted models enable focused transformation.

In a production environment, 3D models of large cast and machined parts were already in active use across engineering, manufacturing, and inspection. These models reflected years of refinement and embedded operational knowledge, but they lacked machine-readable metadata. As a result, inspection relied on manual checklists, and quality teams faced friction in linking process data back to models.
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