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Product

The Nexerios Way

An AI Yield Agent that autonomously manages demand, pricing, and incentives for underutilized assets.

An AI Yield Agent that autonomously manages demand,

pricing, and incentives for underutilized assets.

An AI Yield Agent that

autonomously manages

demand, pricing, and incentives

for underutilized assets.

Step 1

Ingestion Layer

Integrates with infrastructure APIs to pull real-time data on utilization, power load, demand, pricing, weather, and events.

Analyzing Current
Station

System check

Grid Check

Effectiveness

Maintenance

Power Saving

Analyzing Current
Station

System check

Grid Check

Effectiveness

Maintenance

Power Saving

Analyzing Current
Station

System check

Grid Check

Effectiveness

Maintenance

Power Saving

Step 2

Reasoning + Decision Engine

Predicts demand patterns, simulates pricing scenarios, and determines the optimal yield strategy with dynamic rates, incentives, and communication.

class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"
class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"
class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"

class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"
class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"
class AutomationTrigger:
def __init__(self, threshold):
self.threshold = threshold
self.status = "inactive"

def check_trigger(self, value):
if value > self.threshold:
self.status = "active"
return "Automation triggered!"
else:
return "No action taken."
def get_status(self):
return f"Status: {self.status}"

Step 3

Action Layer

Adjusts prices via APIs, launches localized offers, and sends personalized nudges to drivers.

Our solution

Your stack

Our solution

Your stack

Our solution

Your stack

Step 4

Learning Loop

Continuously monitors results, updates pricing strategies, and gets smarter over time.

Real Live Support

Efficiency will increase by 20%

Workflow system

Update available..

Sales system

Up to date

Real Live Support

Efficiency will increase by 20%

Workflow system

Update available..

Sales system

Up to date

Real Live Support

Efficiency will increase by 20%

Workflow system

Update available..

Sales system

Up to date

Powering growth across industries

Powering growth

across industries

RIO is an AI-powered platform

that unlocks revenue

potential and maximizes utilization

through intelligent pricing and rewards.

RIO is an AI-powered platform

that unlocks revenue

potential and maximizes utilization

through intelligent pricing and rewards.

RIO is an AI-powered platform

that unlocks revenue

potential and maximizes utilization

through intelligent pricing and rewards.

Learn more

Blogs

Smart Load Balancing Cut Wait Times by 45%
VoltNet, a regional charging network, faced frequent bottlenecks during peak hours. By deploying Nexerios’ AI-driven load balancing, they were able to predict demand surges and redirect vehicles efficiently.
Impact :
45% Reduction in peak-hour wait times
35% Increase in charger utilization
25% Growth in customer satisfaction scores
50+ Stations optimized in real time
Predictive Maintenance Reduced Downtime by 60%
ChargeHub, a large fleet operator, was losing revenue due to sudden charger breakdowns. Nexerios introduced predictive maintenance powered by Industrial Foundation Models, flagging faults before they occurred.
Impact :
60% Drop in unexpected downtime
70% Faster repair response
40% Lower maintenance costs
90% Charger uptime across the fleet
Data-Driven Routing Saved Drivers 20 Minutes Per Trip
EcoDrive, an urban EV taxi service, struggled with inefficient charging stops. By integrating Nexerios’ intelligent routing platform, drivers were guided to the optimal charging point with real-time availability.
Impact :
20 Minutes saved per trip on average
25% Reduction in charging-related cancellations
15% Increase in fleet daily mileage
10,000+ rides optimized per week
Reward-Linked Charging Boosted Station Use by 30%
GreenGrid, a mid-sized operator, faced underutilized stations in suburban areas. Nexerios enabled a dynamic rewards system tied to off-peak charging, driving higher customer engagement.
Impact :
30% Increase in off-peak charging sessions
50% Growth in loyalty program sign-ups
22% Higher station revenue per user
5,000+ active reward redemptions in 3 months
Smart Load Balancing Cut Wait Times by 45%
VoltNet, a regional charging network, faced frequent bottlenecks during peak hours. By deploying Nexerios’ AI-driven load balancing, they were able to predict demand surges and redirect vehicles efficiently.
Impact :
45% Reduction in peak-hour wait times
35% Increase in charger utilization
25% Growth in customer satisfaction scores
50+ Stations optimized in real time
Predictive Maintenance Reduced Downtime by 60%
ChargeHub, a large fleet operator, was losing revenue due to sudden charger breakdowns. Nexerios introduced predictive maintenance powered by Industrial Foundation Models, flagging faults before they occurred.
Impact :
60% Drop in unexpected downtime
70% Faster repair response
40% Lower maintenance costs
90% Charger uptime across the fleet
Data-Driven Routing Saved Drivers 20 Minutes Per Trip
EcoDrive, an urban EV taxi service, struggled with inefficient charging stops. By integrating Nexerios’ intelligent routing platform, drivers were guided to the optimal charging point with real-time availability.
Impact :
20 Minutes saved per trip on average
25% Reduction in charging-related cancellations
15% Increase in fleet daily mileage
10,000+ rides optimized per week
Reward-Linked Charging Boosted Station Use by 30%
GreenGrid, a mid-sized operator, faced underutilized stations in suburban areas. Nexerios enabled a dynamic rewards system tied to off-peak charging, driving higher customer engagement.
Impact :
30% Increase in off-peak charging sessions
50% Growth in loyalty program sign-ups
22% Higher station revenue per user
5,000+ active reward redemptions in 3 months
Smart Load Balancing Cut Wait Times by 45%
VoltNet, a regional charging network, faced frequent bottlenecks during peak hours. By deploying Nexerios’ AI-driven load balancing, they were able to predict demand surges and redirect vehicles efficiently.
Impact :
45% Reduction in peak-hour wait times
35% Increase in charger utilization
25% Growth in customer satisfaction scores
50+ Stations optimized in real time
Predictive Maintenance Reduced Downtime by 60%
ChargeHub, a large fleet operator, was losing revenue due to sudden charger breakdowns. Nexerios introduced predictive maintenance powered by Industrial Foundation Models, flagging faults before they occurred.
Impact :
60% Drop in unexpected downtime
70% Faster repair response
40% Lower maintenance costs
90% Charger uptime across the fleet
Data-Driven Routing Saved Drivers 20 Minutes Per Trip
EcoDrive, an urban EV taxi service, struggled with inefficient charging stops. By integrating Nexerios’ intelligent routing platform, drivers were guided to the optimal charging point with real-time availability.
Impact :
20 Minutes saved per trip on average
25% Reduction in charging-related cancellations
15% Increase in fleet daily mileage
10,000+ rides optimized per week
Reward-Linked Charging Boosted Station Use by 30%
GreenGrid, a mid-sized operator, faced underutilized stations in suburban areas. Nexerios enabled a dynamic rewards system tied to off-peak charging, driving higher customer engagement.
Impact :
30% Increase in off-peak charging sessions
50% Growth in loyalty program sign-ups
22% Higher station revenue per user
5,000+ active reward redemptions in 3 months
Smart Load Balancing Cut Wait Times by 45%
VoltNet, a regional charging network, faced frequent bottlenecks during peak hours. By deploying Nexerios’ AI-driven load balancing, they were able to predict demand surges and redirect vehicles efficiently.
Impact :
45% Reduction in peak-hour wait times
35% Increase in charger utilization
25% Growth in customer satisfaction scores
50+ Stations optimized in real time
Predictive Maintenance Reduced Downtime by 60%
ChargeHub, a large fleet operator, was losing revenue due to sudden charger breakdowns. Nexerios introduced predictive maintenance powered by Industrial Foundation Models, flagging faults before they occurred.
Impact :
60% Drop in unexpected downtime
70% Faster repair response
40% Lower maintenance costs
90% Charger uptime across the fleet
Data-Driven Routing Saved Drivers 20 Minutes Per Trip
EcoDrive, an urban EV taxi service, struggled with inefficient charging stops. By integrating Nexerios’ intelligent routing platform, drivers were guided to the optimal charging point with real-time availability.
Impact :
20 Minutes saved per trip on average
25% Reduction in charging-related cancellations
15% Increase in fleet daily mileage
10,000+ rides optimized per week
Reward-Linked Charging Boosted Station Use by 30%
GreenGrid, a mid-sized operator, faced underutilized stations in suburban areas. Nexerios enabled a dynamic rewards system tied to off-peak charging, driving higher customer engagement.
Impact :
30% Increase in off-peak charging sessions
50% Growth in loyalty program sign-ups
22% Higher station revenue per user
5,000+ active reward redemptions in 3 months