As advanced manufacturing investment accelerates across the U.S., a quieter challenge is emerging: many small manufacturers still lack the data, quality systems, and shop-floor infrastructure needed to scale.
America’s manufacturing comeback is often discussed in terms of megaprojects: semiconductor fabs, battery plants, defense facilities, pharmaceutical campuses, and industrial reshoring commitments. The capital is visible. The construction is visible. The policy support is visible.
What is less visible is the operational layer required to make these facilities, and the thousands of smaller suppliers around them, work at world-class standards.
For Maheepsai Jinka, a manufacturing technologist and quality systems practitioner who came up through Intel’s semiconductor manufacturing environment before moving into industrial IoT and AI-enabled hardware production, the issue is not simply that manufacturers need more software. It is that many growing manufacturers lack the operational foundation that large, high-discipline production environments often take for granted.
“I have seen what manufacturing looks like when it is done right at the highest level, and I have seen what it looks like for most small American manufacturers,” Jinka says. “The distance between those two realities is exactly the problem I am trying to solve.”
Jinka’s perspective comes from working across two very different manufacturing worlds. At Intel, he was exposed to one of the most demanding production environments in global industry, where semiconductor manufacturing requires rigorous process discipline, traceability, and yield control. He worked on new product introductions into fabrication plants and supported international technology transfers, helping ramp high-volume manufacturing in response to demand from data centers and advanced computing markets.
That experience shaped how he thinks about production. In high-performing manufacturing systems, problems are not merely fixed after they occur. They are anticipated, measured, and engineered out of the process wherever possible.
“Semiconductor manufacturing teaches you to think in systems,” Jinka says. “You are not just asking why a defect happened. You are asking what part of the process allowed it to happen, how early you could have detected it, and what data should have existed before the failure became visible.”
When he later joined Gatekeeper Systems, a U.S.-based manufacturer of AI cameras and IoT-enabled shopping cart technology, Jinka encountered a very different operating reality. The company was building advanced hardware, but many of the core manufacturing systems that support scalable production had to be built from the ground up. There was no mature quality management structure, no production data infrastructure, and no clear yield-tracking framework in place.
Rather than treating those gaps as isolated process issues, Jinka saw them as symptoms of a broader problem facing small and mid-sized manufacturers.
“The American manufacturing conversation focuses heavily on the fabs, the OEMs, and the publicly traded giants,” he says. “But the backbone of this country’s industrial output is thousands of small and mid-size manufacturers that are being asked to meet modern quality and traceability expectations without having modern tools.”
At Gatekeeper, Jinka built quality systems, new product introduction processes, and eventually a proprietary full-stack Manufacturing Execution System, or MES, that digitized the company’s manufacturing operation end to end. According to Jinka, the system saves the company approximately $500,000 annually.
The achievement is significant not only because of the cost savings, but because it demonstrates a common blind spot in industrial transformation. Many small manufacturers do not need sprawling enterprise systems designed for multinational corporations. They need practical, affordable, shop-floor-native tools that give them visibility into production, quality, yield, and traceability without requiring a year-long implementation cycle or a budget that only large companies can absorb.
That is where Jinka believes the next phase of manufacturing software must evolve.
“Big enterprise software can cost hundreds of thousands of dollars and take months or years to implement,” he says. “A 20-person manufacturing shop cannot operate that way. They need something lean, affordable, and usable by the people actually running the floor.”
This is also why Jinka is building a startup focused on affordable manufacturing software for small and startup manufacturers. The goal, he says, is not to replicate enterprise MES platforms at a lower price point, but to design around the actual constraints of smaller production environments: limited headcount, limited implementation bandwidth, uneven data discipline, and processes that often still live in the heads of experienced operators.
In many ways, the timing is favorable. Manufacturing leaders increasingly recognize that automation and AI are only as useful as the data infrastructure beneath them. A plant cannot meaningfully apply AI to quality control if it does not first capture clean, consistent production data. It cannot improve yield trends if yield is not being measured in a structured way. It cannot satisfy modern customer traceability requirements if product movement, defects, rework, and process deviations are still being recorded manually or inconsistently.
Jinka argues that this is where the talent conversation and the software conversation intersect. America does not only need more manufacturing workers. It needs more engineers and operators who understand how to translate world-class manufacturing discipline into environments that were not built with world-class systems from day one.
“The hardest part of America’s manufacturing resurgence is not just building factories,” Jinka says. “It is finding the people who know how to run them properly. The skills exist, but they need to be applied where the need is greatest.”
His own career reflects that transition. After earning a competitive International Outreach Scholarship from the University of Cincinnati, where he completed a master’s degree in mechanical engineering, Jinka entered the U.S. manufacturing industry without an inherited network or a clear roadmap. He describes the scholarship as a formative moment, not only because it provided financial support, but because it represented institutional belief in his potential.
That background now informs how he thinks about leadership. Jinka does not frame himself only as a manager or software founder, but as a practitioner who has built systems directly, worked inside demanding production environments, and understands the human side of manufacturing operations.
“I want to be visible to engineers from international and underrepresented backgrounds,” he says. “Not as an exception, but as an example of what is possible when the door is open and the work ethic is there.”
For manufacturers, the broader lesson may be that competitiveness is not determined by capital investment alone. A factory with advanced machines but poor process visibility is still fragile. A supplier with experienced operators but no data infrastructure is still limited. A growing hardware company without a quality system may be able to ship product, but it will struggle to scale predictably.
Jinka’s work points to a practical middle ground: take the discipline learned from the most rigorous manufacturing environments and make it accessible to the companies that cannot afford to build those systems the traditional way.
If America’s manufacturing resurgence is to last, that may be one of the most important challenges ahead. The country does not only need more factories. It needs more operating systems behind them, and more practitioners who know how to build those systems from the inside.
