Geospatial Solutions
Utility and energy operations demo

Energy Operations Intelligence Workspace

Identify which assets and regions require action using geospatial risk scoring, demand pressure, source confidence, and portfolio context.

Built for utility program managers
Facilities and sustainability reporting
Export-ready operational briefings

Loading energy portfolio data and geospatial layers...

What this energy operations workflow demonstrates

Energy portfolios often combine asset registries, demand indicators, maintenance records, geographic exposure, and source systems with different update schedules. This demonstration shows how those inputs can be normalized into one review workflow without hiding uncertainty or replacing engineering judgment.

Teams can narrow the portfolio by region and risk, inspect an individual asset, record an operational response, review data provenance, and prepare an export for the next decision-maker. The interface is a representative workflow; production implementations are configured around each organization's assets, thresholds, governance rules, and reporting requirements.

Questions the dashboard can help answer

  • Which assets combine elevated operational risk with growing regional demand pressure?
  • Which records need better source coverage or human validation before action is assigned?
  • How can portfolio findings move from a map into an accountable owner, priority, and service-level target?
  • What evidence and filters should accompany a CSV, PDF, briefing, or downstream system handoff?

From demonstration to a production dashboard

Connect operational data

Integrate asset, meter, work-order, billing, SCADA, sustainability, or facilities data with consistent identifiers and refresh rules.

Define defensible scoring

Translate business and engineering criteria into documented thresholds, confidence labels, exception handling, and review queues.

Deliver accountable outputs

Route findings into reports, GIS layers, action registers, APIs, or existing enterprise systems with the context needed for follow-through.

Implementation stack

Technologies We Work With

We choose the stack around the job: browser maps for decision surfaces, spatial databases for reliable data, Python and GIS libraries for analysis, and annotation tools when imagery needs reviewer-ready labels.

QGIS

GIS Software

ESRI ArcGIS

GIS Platform

PostgreSQL

Database

PostGIS

Spatial Database

AWS

Cloud Platform

Google Cloud

Cloud Platform

DuckDB

Analytics Database

OpenAI

AI Platform

Claude AI

AI Assistant

CVAT

Annotation Tool

Python

Programming

React

Frontend

Node.js

Backend

Docker

Containerization

Kubernetes

Orchestration

Azure

Cloud Platform

TensorFlow

Machine Learning

Pandas

Data Analysis

NumPy

Scientific Computing

Jupyter

Data Science

Git

Version Control

Linux

Operating System

Ubuntu

Operating System

Mapbox

Mapping Platform

Leaflet

Web Mapping

Fastapi

API Framework

GeoPandas

Geospatial Analysis

GDAL

Geospatial Library