AI & DATA SOLUTIONS · ENERGY & EV

AI & Data Intelligence for Energy and EV Leaders

From raw IoT telemetry to autonomous decision-making — we build the data foundations and AI systems that optimize energy grids, predict failures before they happen, and scale EV infrastructure with confidence.

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Grid & Fleet IntelligenceLive
Charging Station Status
CS-01
150
kW · Active
CS-02
95
kW · Active
CS-03
—
Idle
CS-04
120
kW · Active
CS-05
22
kW · Caution
CS-06
75
kW · Active
Fleet Avg SoC
78%
847 kW
Grid Load
5/6
Online
1.2M
Events/Day
Power Output (kW)
Azure IoT + Microsoft Fabric
At a Glance

We design and deploy integrated AI and data solutions for energy providers, EV operators, and industrial enterprises. By combining Microsoft Fabric, Azure AI, Databricks, and Power BI, we eliminate siloed sensor data, automate complex ETL pipelines, and infuse machine learning into the heart of your operations. From real-time telemetry dashboards to AI-powered predictive models and agentic workflows, we give engineering and operational leadership the intelligence to optimize grid performance, maximize fleet reliability, and drive sustainable competitive advantage.

The Energy & EV Intelligence Gap

Industrial enterprises and EV manufacturers generate immense volumes of telemetry data every second. The real challenge is not collecting that data — it is unifying, processing, and activating it in real time, and then layering AI on top to move from reactive operations to predictive, autonomous intelligence.

Siloed IoT & Telemetry Data

Sensor readings, factory floor metrics, and grid telemetry are often trapped in fragmented, vendor-specific systems. Without a unified data layer, neither human operators nor AI models can gain a complete picture of asset health — leading to hidden inefficiencies and missed optimization opportunities.

No Predictive or Prescriptive Intelligence

Manual data extraction and slow batch processing cannot power AI-driven maintenance models. Without real-time data pipelines feeding machine learning systems, organizations remain locked in reactive maintenance cycles — increasing downtime, repair costs, and unplanned outages.

Scaling EV Infrastructure Intelligently

Managing dynamic grid loads, charging station uptime, and battery state-of-charge requires not just immediate data processing — but AI models that can forecast demand, auto-route charging loads, and flag anomalies before they cascade into failures. Legacy infrastructure supports neither.

Legacy Systems Block AI Adoption

Aging on-premise servers lack the compute power to train machine learning models, run inference at the edge, or stream real-time IoT events. They stifle innovation and prevent organizations from deploying the AI-powered operational intelligence that modern energy and EV markets demand.

Siloed IoT & Telemetry Data

Sensor readings, factory floor metrics, and grid telemetry are often trapped in fragmented, vendor-specific systems. Without a unified data layer, neither human operators nor AI models can gain a complete picture of asset health — leading to hidden inefficiencies and missed optimization opportunities.

No Predictive or Prescriptive Intelligence

Manual data extraction and slow batch processing cannot power AI-driven maintenance models. Without real-time data pipelines feeding machine learning systems, organizations remain locked in reactive maintenance cycles — increasing downtime, repair costs, and unplanned outages.

Scaling EV Infrastructure Intelligently

Managing dynamic grid loads, charging station uptime, and battery state-of-charge requires not just immediate data processing — but AI models that can forecast demand, auto-route charging loads, and flag anomalies before they cascade into failures. Legacy infrastructure supports neither.

Legacy Systems Block AI Adoption

Aging on-premise servers lack the compute power to train machine learning models, run inference at the edge, or stream real-time IoT events. They stifle innovation and prevent organizations from deploying the AI-powered operational intelligence that modern energy and EV markets demand.

Energy & EV · AI & Data Solutions

AI & Data Solutions We've Delivered

These are proven AI and data architectures deployed for enterprise leaders in the energy and EV sectors. We measure our success by the intelligence, efficiency, and uptime we create for our clients.

Solution 01 / 04Energy & EV · AI & Data

Real-Time EV Fleet Telemetry & Charging Analytics Platform

What We Built

Engineered an event-driven streaming pipeline on Azure Data Factory and Databricks Structured Streaming to ingest continuous telemetry from EV charging stations and vehicle batteries — consolidating siloed IoT data sources into a unified, real-time analytics foundation with interactive Power BI dashboards tailored for fleet managers and grid operators.

The Outcome

Operations teams gained always-on, millisecond-level visibility into battery state-of-charge, grid load distribution, and charging station health — eliminating manual data pulls and enabling real-time fleet decision-making across the entire charging network.

Azure Data FactoryDatabricks StreamingPower BI
Results
Real-TimeFleet Battery & Charging Visibility
100%IoT Sources Unified Into Single Platform
ZeroManual Data Extracts for Fleet Operations
Solution 02 / 04Energy & EV · AI & Data

AI Battery Degradation Forecasting & Anomaly Detection

What We Built

Deployed machine learning models on Azure ML and Databricks MLflow trained on vehicle telemetry and charging cycle data to forecast battery degradation curves, detect electrical anomalies, and surface prescriptive alerts — weeks before physical symptoms appear in the field.

The Outcome

Fleet operators moved from reactive troubleshooting to proactive, AI-guided asset management. At-risk batteries were identified and scheduled for intervention before causing downtime, significantly reducing unplanned vehicle-out-of-service events and extending overall fleet lifespan.

Azure MLDatabricks MLflowStructured Streaming
Results
95%+Anomaly Detection Accuracy on Battery Sensors
Weeks EarlierFailure Prediction Before Physical Symptoms
ReducedUnplanned Fleet Downtime Events
Solution 03 / 04Energy & EV · AI & Data

Predictive Maintenance for Grid Equipment & Energy Assets

What We Built

Built a machine learning pipeline on Databricks and Azure ML that continuously analyzes sensor streams from turbines, transformers, and grid equipment. Models trained on historical failure patterns identify early-warning signatures and surface prescriptive maintenance recommendations through automated alerts and operational dashboards.

The Outcome

Asset operations teams shifted from calendar-based maintenance to condition-based, AI-driven schedules. Mean time between failures improved measurably, unplanned outage events declined, and maintenance crews were dispatched with precision — reducing operational costs and extending critical asset lifespan.

DatabricksAzure MLIoT Sensor Analytics
Results
20–30%Reduction in Unplanned Outages on Critical Assets
ExtendedAsset Lifespan Through Condition-Based Scheduling
LowerMaintenance Costs vs. Reactive Calendar Model
Solution 04 / 04Energy & EV · AI & Data

AI-Augmented Grid Operations & Power BI Command Center

What We Built

Delivered Power BI dashboards enhanced with AI-generated insights, natural language Q&A, demand forecasting models, and automated alerting for grid operators and energy leadership — integrating telemetry from turbines, substations, and EV charging networks into a single operational intelligence layer via Microsoft Fabric.

The Outcome

Grid operations teams moved from dense backend outputs to a prescriptive command center — seeing what is happening, predicting what will happen next, and receiving AI-recommended next actions. Operational decision speed improved significantly across both grid and fleet management functions.

Power BIAzure AIMicrosoft FabricDelta Lake
Results
AI-DrivenPrescriptive Insights, Not Just Historical Charts
Real-TimeGrid Load & Demand Forecasting
SingleUnified Command Center for Grid & EV Fleet
Enterprise Clients

Trusted Enterprise Clients

We partner with industry pioneers to solve their most complex data challenges.

Renewable Energy

Suzlon Energy

Empowered their renewable energy operations by unifying distributed turbine sensor data into a governed, scalable cloud architecture — enabling advanced performance analytics and AI-driven turbine health monitoring.

DatabricksMicrosoft FabricTurbine Analytics
Unified distributed turbine data across all facilities
Voltera Power — EV charging infrastructure
EV Charging Infrastructure

Voltera Power

Enabled real-time visibility and predictive intelligence across their EV charging infrastructure, leveraging streaming data pipelines and ML models to optimize facility operations and maximize fleet charging reliability.

Azure Data FactoryPower BIReal-Time Streaming
Real-time fleet & charging visibility at enterprise scale
Renewable Energy

Suzlon Energy

Empowered their renewable energy operations by unifying distributed turbine sensor data into a governed, scalable cloud architecture — enabling advanced performance analytics and AI-driven turbine health monitoring.

DatabricksMicrosoft FabricTurbine Analytics
Unified distributed turbine data across all facilities
Voltera Power — EV charging infrastructure
EV Charging Infrastructure

Voltera Power

Enabled real-time visibility and predictive intelligence across their EV charging infrastructure, leveraging streaming data pipelines and ML models to optimize facility operations and maximize fleet charging reliability.

Azure Data FactoryPower BIReal-Time Streaming
Real-time fleet & charging visibility at enterprise scale

How We Do It

The AI & Data Architecture of a Modern Energy Enterprise

We precisely align the Microsoft, Azure AI, and Databricks technology ecosystem to the exact operational stage where it will create the greatest measurable business value — from raw data ingestion all the way to autonomous AI-driven decision-making.

01

Data Ingestion & IoT Streaming

Azure Data Factory
The Process

Capturing continuous telemetry from factory floor machines, smart grid sensors, and remote EV charging stations.

The Challenge

Legacy systems rely on scheduled batch extracts, meaning critical asset performance data is already outdated by the time it reaches the database.

Our Solution

We deploy automated, cloud-native ETL pipelines using Azure Data Factory to reliably ingest massive volumes of raw IoT data without manual intervention — creating the clean, structured foundation that AI models require to operate at scale.

01

Data Ingestion & IoT Streaming

Azure Data Factory
The Process

Capturing continuous telemetry from factory floor machines, smart grid sensors, and remote EV charging stations.

The Challenge

Legacy systems rely on scheduled batch extracts, meaning critical asset performance data is already outdated by the time it reaches the database.

Our Solution

We deploy automated, cloud-native ETL pipelines using Azure Data Factory to reliably ingest massive volumes of raw IoT data without manual intervention — creating the clean, structured foundation that AI models require to operate at scale.

02

AI Modeling & Predictive Analytics

Azure ML · Databricks MLflow
The Process

Training and deploying machine learning models on processed time-series and telemetry data to predict failures, forecast demand, and surface actionable recommendations.

The Challenge

Raw data alone cannot prevent failures or optimize performance. Without purpose-built ML models and a governed feature store, operational teams are left interpreting dashboards rather than acting on intelligence.

Our Solution

We build, validate, and deploy predictive models using Azure Machine Learning and Databricks MLflow — covering use cases from battery degradation forecasting and anomaly detection to grid load prediction and preventive maintenance scheduling.

03

Enterprise Storage, Governance & Feature Engineering

Microsoft Fabric · Delta Lake
The Process

Maintaining a centralized, secure repository of historical operational data, maintenance logs, and grid performance metrics.

The Challenge

Making IoT data accessible to analysts while maintaining strict governance and security over proprietary operational infrastructure.

Our Solution

We implement the Microsoft Fabric Lakehouse and Delta Lake architecture to store processed telemetry, maintain data lineage, enforce governance policies, and curate the ML feature tables that power your AI models in production.

04

AI-Augmented Visibility & Autonomous Operations

Power BI · Azure AI
The Process

Delivering actionable operational intelligence to plant managers, fleet operators, and engineering leads.

The Challenge

Dense backend data outputs are inaccessible to the operational leaders who need to make immediate decisions on the factory floor or grid control room.

Our Solution

We deliver Power BI dashboards enhanced with AI-generated insights, natural language Q&A capabilities, and automated alerting — giving operations teams a command center that not only shows what is happening, but recommends what to do next.

FAQs

Frequently Asked Questions

See How We Turn Complex IoT Data Into Real Operational Intelligence

Download our in-depth case study to discover how we architected end-to-end AI and data pipelines that transformed EV fleet telemetry, deployed predictive maintenance models, and delivered AI-augmented executive dashboards — driving measurable uptime, cost savings, and fleet performance.

NCompas TechnologyCase Study

EV Fleet & Grid AI: From Telemetry to Predictive Intelligence

Azure Data Factory · Azure ML · Databricks · Microsoft Fabric · Power BI

Fleet SoC Visibility
91%
Grid Load Optimization
78%
Predictive Accuracy
94%
BI Adoption
88%
34%
Fleet Uptime ↑
$3.1M
Cost Saved
94%
Pred. Accuracy

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