Geospatial Solutions
βš™οΈ
πŸ€– Powered by MCP Agents

Custom Solutions for Unique Challenges

If you need it, we'll build it

Varies by project
Time Saved
Varies by project
Cost Reduction
Tailored to requirements
Accuracy
Industries Served:
All industries - bespoke solutions

Overview

Specialized analyses, unique data pipelines, or entirely new geospatial platforms. Experience across renewable energy, real estate, environmental consulting, government, and more. Solutions that are robust, scalable, and maintainable for the long term.

Visual Workflow

How It Works: Visual Breakdown

See the complete automation workflow with diagrams and code examples

Automated Workflow Diagram
Visual representation of the MCP agent workflow from trigger to delivery

System Architecture

End-to-end custom solution development with MCP solution architect, YOLOv8 computer vision, PostGIS spatial analysis, and automated work order generation for rail inspection.

πŸ—οΈComponent Architecture Diagram
Visual representation of system components, data flow, and integrations

βš™οΈKey Components

MCP Solution Architect Agent

AI agent analyzing requirements and designing optimal system architecture

YOLOv8 Defect Detection

Computer vision model detecting cracks, corrosion, and misalignment

GPS Coordinate Matching

Geo-referencing detected defects to precise rail network locations

Severity Classification

ML model classifying defects as low, medium, high, or critical

PostGIS Spatial Storage

Storing defect locations with rail network topology

Priority Dashboard

Real-time visualization of defects sorted by severity and location

Automated Work Orders

Generating repair tasks with location, priority, and photos

Diagram Legend
MCP AI Agents
Processing/Storage
Output/Visualization
Analytics/Monitoring

Visual Examples

See the solution in action with real dashboard examples and visual comparisons

πŸ–ΌοΈRail Defect Detection Dashboard
Real-time dashboard showing detected rail defects with severity classification
πŸ—ΊοΈπŸ“Š

Rail Defect Detection Dashboard

Screenshot Placeholder

Image path: /mockups/rail-defects.png

Key Features:

βœ“Interactive map with defect locations
βœ“Color-coded by severity (low/medium/high/critical)
βœ“Defect type filter (crack, corrosion, misalignment)
βœ“Detection confidence score
βœ“Photo thumbnails with bounding boxes
βœ“Priority work order queue
πŸ–ΌοΈYOLOv8 Training Metrics
Model training dashboard showing accuracy metrics and sample predictions
πŸ—ΊοΈπŸ“Š

YOLOv8 Training Metrics

Screenshot Placeholder

Image path: /mockups/yolo-training.png

Key Features:

βœ“Training loss curve over epochs
βœ“Validation accuracy metrics (mAP, precision, recall)
βœ“Confusion matrix for defect classes
βœ“Sample predictions with confidence scores
βœ“Training time and GPU utilization
βœ“Export trained model for deployment

πŸ’‘Note: The dashboard screenshots above are placeholders. Actual screenshots will be added after deploying Streamlit dashboards or capturing real application screenshots. Image paths are specified for easy integration.

πŸ€– Agentic Workflow

Automated MCP Agent Workflow

Powered by n8n, Make.com, and Model Context Protocol agents

Workflow Trigger
How the automation starts

Bespoke client request submitted

1
Webhook Trigger
Receives custom requirements (use case, constraints, success criteria)
n8n
REST API
Email parsing
2
MCP Agent Solution Design
AI analyzes requirements, designs workflow, suggests tools, outlines implementation steps
MCP
Azure OpenAI
LangChain
MCP Agent Prompt:

β€œGiven these requirements for a custom geospatial solution: [Client needs real-time wildfire risk monitoring for 500 properties across California, integrating satellite imagery (MODIS), weather forecasts (NOAA), historical fire perimeters (CAL FIRE), and property boundaries. Needs automated daily risk scores, email alerts for high-risk properties, and interactive dashboard.] Design a custom workflow: suggest n8n architecture (trigger: daily 6 AM cron, data nodes: NASA FIRMS API for active fires, NOAA weather, CAL FIRE perimeters, PostGIS for buffers and risk scoring), recommend tech stack (n8n, PostGIS, Mapbox, Plotly, SendGrid), outline implementation steps (1. Set up data ingestion, 2. Build risk scoring algorithm, 3. Create dashboard, 4. Configure alerts), and estimate timeline (4-6 weeks) and cost ($25K-$35K).”

3
Discovery Workshop
1-2 hour workshop with client to refine requirements and validate approach
Zoom
Miro
FigJam
Calendly
4
Prototyping
Build proof-of-concept with core functionality
Varies by project
5
Client Feedback
Demo prototype, gather feedback, iterate on design
Zoom
Loom videos
6
Production Build
Full implementation with testing, documentation, and training
Varies by project
7
Deployment
Deploy to production, monitor, provide handover training
Azure
AWS
GCP
On-premises
Deliverables
What you receive automatically
  • Custom geospatial solution (fully tailored)
  • Documentation (architecture, API, user guides)
  • Training for end users and admins
  • Source code (optional, per agreement)
  • Maintenance and support plan
  • Post-launch monitoring (30-90 days)

Key Features

Fully customized to your requirements

Scalable architecture (cloud or on-premises)

Integration with existing systems

Modern tech stack (Next.js, React, PostGIS, etc.)

Comprehensive documentation

User training and admin training

Post-launch support (30-90 days included)

Source code delivery (optional)

Technology Stack

Automation
n8n
Make.com
Power Automate
Apache Airflow
Prefect
GIS & Mapping
PostGIS
QGIS
ArcGIS
Mapbox
Leaflet
Deck.gl
GeoPandas
GDAL
AI & Analysis
Azure OpenAI
Microsoft Copilot
MCP Agents
LangChain
LlamaIndex

API Integrations

Success Story

Real-World Results

Environmental Consulting Firm

Challenge

Needed custom pipeline for processing 10K+ drone images per week (wetland delineation projects). Manual workflow: 40 hours/week for QA, georeferencing, classification, vectorization, and report generation. Client required automated pipeline with 95% accuracy and 24-hour turnaround.

Our Solution

Custom n8n workflow with 100+ nodes: file upload triggers workflow, MCP agent analyzes image quality and suggests processing parameters, GDAL georeferencing and orthorectification, Azure Computer Vision for wetland classification (trained custom model), PostGIS vectorization and topology cleaning, automated QA (comparing to ground truth samples), PDF report generation with maps and statistics, upload to client SharePoint, email notification. End-to-end processing: 2 hours (unattended).

Results Achieved

40 hrs/week β†’ 2 hrs/week (95% reduction)
Turnaround: 3-5 days β†’ 24 hours
Accuracy: 97% (exceeded 95% target)
Cost: $45K development + $800/month (vs $104K annual labor)
ROI: 5 months payback
Scaled to process 15K images/week with no additional labor
Client won 3 new contracts due to faster turnaround
Implementation Timeline

Flexible Pricing Options

Choose the plan that fits your needs

Pilot Project
Perfect for testing the solution
$10,000-$25,000 (proof-of-concept, 2-4 weeks)

Test the solution with a limited scope project to validate ROI before full deployment.

Get Started
Most Popular
Monthly Subscription
Ongoing automation & support
Varies (depends on complexity and hosting needs)

Full production deployment with hosting, monitoring, and ongoing updates included.

Schedule Demo
Enterprise
Custom solutions at scale
$50,000-$250,000+ (full custom platform, 8-16 weeks)

White-label solutions, multi-tenant deployments, SLA guarantees, and dedicated support.

Contact Sales

Ready to Transform Your GIS Workflows?

Schedule a free 30-minute consultation to see how Custom Solutions for Unique Challenges can deliver measurable ROI for your organization.

Technologies We Work With

Leveraging cutting-edge technologies and industry-leading tools to deliver exceptional geospatial solutions and data analytics services.

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