THE FUTURE OF INDUSTRIAL PROCESS CONTROL

S Y N Λ P T I X

The Next-Generation Autonomous Control Platform

Unlocks the full potential of industrial manufacturing systems through intelligent modeling, adaptive control, and real-time process optimization.

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PROBLEM

Untapped Performance Potential

Manufacturing systems often leave significant value on the production floor when legacy control strategies cannot adapt to changing process dynamics. Synaptix recovers that value by predicting process behavior, reducing variability, and enabling optimal control in real time.

Reactive Operations Are Expensive

Reactive control leaves margins on the table. Most production systems still rely on static control strategies, heuristic tuning, and manual intervention. When disturbances occur, operators often respond after quality, throughput, or stability has already degraded. The result is lost profits in the form of: avoidable scrap, rework, downtime, and lost production capacity.

Reactive Operations Are Expensive

Variability Reduces Throughput

Small deviations compound across continuous processes. Satisfactory performance, input variation, material changes, wear-and-tear, equipment drift, nonlinear disturbances, unpredicted changes, and suboptimal controller settings can push systems away from ideal operating conditions. These losses are often accepted as normal, but they are measurable, predictable, and recoverable.

Shutdowns Rarely Happen Without Warning

Most unplanned shutdowns and performance losses are preceded by detectable patterns, but static models, alarms, and dashboards often show what is happening rather than predict what will happen next. Experienced operators remain critical, but they need better predictive insight and autonomous systems to act before production is lost.

PRODUCT

From Static Control to Adaptive Optimization

Synaptix integrates data-driven system identification and physics-informed digital models with intelligent control into a continuous optimization engine.

Adaptive Throughput Optimization

Predictive Downtime Prevention

Autonomous Decision Intelligence

Adaptive Throughput Optimization

Continuously optimize process variables to keep manufacturing systems stable, efficient, and operating at peak performance.

Predictive Downtime Prevention

Identify precursor patterns before performance degrades, enabling earlier intervention and reducing avoidable downtime, scrap, and rework.

a bunch of blue wires connected to each other

Autonomous Decision Intelligence

Transform process data into predictive, model-based guidance that reduces manual intervention and helps operators focus on higher-value decisions.

PRODUCT

From Static Control to Adaptive Optimization

Synaptix integrates data-driven system identification and physics-informed digital models with intelligent control into a continuous optimization engine.

Adaptive Throughput Optimization

Predictive Downtime Prevention

Autonomous Decision Intelligence

Adaptive Throughput Optimization

Continuously optimize process variables to keep manufacturing systems stable, efficient, and operating at peak performance.

Predictive Downtime Prevention

Identify precursor patterns before performance degrades, enabling earlier intervention and reducing avoidable downtime, scrap, and rework.

Autonomous Decision Intelligence

Transform process data into predictive, model-based guidance that reduces manual intervention and helps operators focus on higher-value decisions.

TEAM

Meet the Team

Synaptix is built by the team uniquely equipped to redefine industrial control. We combine deep manufacturing experience, advanced automation expertise, AI and controls research, and firsthand knowledge of how production systems actually behave—bridging the gap between factory-floor reality and intelligent, self-optimizing operations.

Arman Shantayev
MIT Dual SM'26
Arman Shantayev is a engineering and operations leader with more than a decade of experience improving performance across upstream oil and gas assets. He has worked across multinational exploration and production business units, leading initiatives in production optimization, operational efficiency, asset reliability, and field performance. His experience spans complex industrial operations where disciplined execution and cross-functional collaboration are essential to improving throughput and reducing constraints. Arman holds dual master’s degrees from MIT in Engineering and Management and Mechanical Engineering. At MIT, his research in the Device Realization Laboratory focused on immersive manufacturing technologies and virtual models to improve operator training, decision-making, and performance.
Samuel Gomez
MIT SM '25, PhD '28
Samuel Gomez is an engineer, technologist, and entrepreneur focused on advancing the future of smart manufacturing. Over the past 15 years, he has helped launch and scale companies across industrial manufacturing, consumer packaged goods, and energy. He is the Founder and CEO of SAGO Technology, an engineering and technology company building intelligent Industry 4.0 solutions for manufacturing enterprises. Prior to SAGO, Samuel served on the executive management team and led the engineering and technology division for a company that scaled from a startup to a global leader in its agricultural segment. He is currently pursuing a PhD in Mechanical Engineering at MIT, where his research focuses on system dynamics, control systems, AI, and autonomous manufacturing.
Morgen Pronk
MIT SM'25
Morgen Pronk is an engineer with deep expertise in applied machine learning, modeling, and optimization for industrial systems. He brings nearly a decade of oil and gas experience across production operations, reliability engineering, facilities, pipeline projects, maintenance, and data-driven performance improvement. At MIT, he earned a Master of Science in Engineering and Management, where his research focused on hybrid physics-neural network models for complex nonlinear industrial processes. Morgen combines field experience with AI, advanced analytics, and control systems to improve the performance, reliability, and efficiency of industrial assets.
Dr. Brian Anthony
MIT Principal Reseach Scientist & Director
Dr. Brian Anthony is a researcher, educator, and technology innovator with more than 25 years of experience in product realization, computational instrumentation, and information-enabled manufacturing. He is a Principal Research Scientist in MIT Mechanical Engineering, Associate Director of MIT.nano, Director of MIT’s Master of Engineering in Manufacturing Program, and Co-Director of the Medical Electronic Device Realization Center. His work focuses on designing instruments and techniques to measure, monitor, and control complex physical systems across manufacturing, medical diagnostics, imaging, and advanced sensing. He holds 20 patents, has published more than 100 peer-reviewed articles, and received an Emmy Award for technical innovation in sports broadcasting.