NEXT-GENERATION AUTONOMOUS CONTROL LAYER

S Y N Λ P T I X

The Future of Industrial Control

We help businesses unlock their system's full potential through adaptive, intelligent, model-based process optimization

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PROBLEM

Untapped Performance Potential

Hidden economic gains within industrial systems limited by legacy control strategies and delayed decision-making.

Reactive Operations Are Expensive

Most production systems still operate in reactive mode. Instability is detected after performance drops. Shutdowns occur after warning signals are missed. Operators intervene manually when systems fall outside limits. This leads to avoidable production loss, downtime, and labor cost.

Reactive Operations Are Expensive

Variability Reduces Throughput

Flow instability, suboptimal tuning, and heuristic-based decisions quietly reduce output. Systems drift from optimal setpoints. Slugging, oscillations, and inconsistent control degrade performance. The loss is often accepted as normal, but it is measurable and recoverable.

Shutdowns Rarely Happen Without Warning

Most unplanned shutdowns are preceded by detectable patterns. Static models and alarm-driven workflows fail to act early. By the time action is taken, production is already lost. Earlier detection enables preventive intervention instead of recovery.

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.

Adjust process variables in real time. Stabilize the flow, eliminate variability, and maximize production.

Predictive Downtime Prevention.

Detects precursor patterns before performance degradation occurs, reducing avoidable shutdown events

a bunch of blue wires connected to each other

Autonomous Decision Intelligence.

Transforms workflows into predictive guidance, reducing manual intervention and freeing operators to focus on high-value tasks.

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.

Adjust process variables in real time. Stabilize the flow, eliminate variability, and maximize production.

Predictive Downtime Prevention.

Detects precursor patterns before performance degradation occurs, reducing avoidable shutdown events

Autonomous Decision Intelligence.

Transforms workflows into predictive guidance, reducing manual intervention and freeing operators to focus on high-value tasks.

AI Automation Services

Make Business Ease: Simplify your process with automation.

Insights Drive Growth: Leverage actionable data to scale with AI.

Data-driven

Turn raw data into actionable insights that smarter decisions and measurable growth.

Efficient Growth

Work smarter, not harder. Unlock faster results and lower costs with AI-powered efficiency.

Workflow Automation & Optimization.

Streamline repetitive tasks & Keep your business running on autopilot.

Custom AI Model Development

Get AI solutions built around your unique needs.

Name :

AI_development.py

class AutomationAgent:
def __init__(self, activation_limit):
self.activation_limit = activation_limit
self.current_mode = "idle"

def evaluate_task(self, workload_value):
if workload_value > self.activation_limit:
self.current_mode = "engaged"
return "Automation agent has been successfully activated!"
else:
return "No activation needed. Agent stays idle."
def get_current_mode(self):
return f"Current operational mode: {self.current_mode}"

Name :

AI_development.py

class AutomationAgent:
def __init__(self, activation_limit):
self.activation_limit = activation_limit
self.current_mode = "idle"

def evaluate_task(self, workload_value):
if workload_value > self.activation_limit:
self.current_mode = "engaged"
return "Automation agent has been successfully activated!"
else:
return "No activation needed. Agent stays idle."
def get_current_mode(self):
return f"Current operational mode: {self.current_mode}"

Chatbots & virtual assistants

Engage customers 24/7 with intelligent Virtual assistants.

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Data-Driven Decisions
Personalized Experiences
Automation
Scalable Solutions
Real-Time Insights
Virtual Assistance
Cost Effective
Faster Innovation

Data-Driven Decisions
Personalized Experiences
Automation
Scalable Solutions
Real-Time Insights
Virtual Assistance
Cost Effective
Faster Innovation

Data-Driven Decisions
Personalized Experiences
Automation
Scalable Solutions
Real-Time Insights
Virtual Assistance
Cost Effective
Faster Innovation

TEAM

Meet the Team

Our team brings together industry professionals, researchers, and MIT graduates to build the next generation of intelligent industrial control systems

Arman Shantayev
MIT SM'26
He brings over a decade of experience in upstream oil and gas across multinational exploration and production business units, leading initiatives to maximize production throughput and operational efficiency. At MIT, he also served as a graduate researcher studying how immersive technologies improve operator performance in manufacturing environments.
Samuel Gomez
MIT SM '25, PhD '28
Samuel brings 14 years of experience across industrial manufacturing, consumer packaged goods, and energy. He has helped launch and scale five startups, including serving on the executive team of a company that became a global leader in its agriculture segment. He is now pursuing a PhD in Mechanical Engineering.
Morgen Pronk
MIT SM'25
Morgen brings 10 years of professional experience in oil and gas with Chevron. His work spans manufacturing operations, machine learning, and AI-driven solutions to improve process performance and operational efficiency. He has focused on applying advanced analytics and intelligent systems to optimize industrial workflows and production outcomes.
Brian Anthony
Principal Reseach Scientist
Dr. Anthony is the Co-Director of MIT’s Medical Electronic Device Realization Center and Associate Director of MIT.nano. With over 25 years of experience in product realization, Dr. Anthony designs instruments and techniques to monitor and control physical systems. His work involves systems analysis and design, calling upon mechanical, electrical and optical engineering, along with computer science and optimization.