If you need it, we'll build it
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.
See the complete automation workflow with diagrams and code examples
End-to-end custom solution development with MCP solution architect, YOLOv8 computer vision, PostGIS spatial analysis, and automated work order generation for rail inspection.
AI agent analyzing requirements and designing optimal system architecture
Computer vision model detecting cracks, corrosion, and misalignment
Geo-referencing detected defects to precise rail network locations
ML model classifying defects as low, medium, high, or critical
Storing defect locations with rail network topology
Real-time visualization of defects sorted by severity and location
Generating repair tasks with location, priority, and photos
See the solution in action with real dashboard examples and visual comparisons
Rail Defect Detection Dashboard
Image path: /mockups/rail-defects.png
YOLOv8 Training Metrics
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π‘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.
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Bespoke client request submitted
βGiven 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).β
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)
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.
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).
Choose the plan that fits your needs
Test the solution with a limited scope project to validate ROI before full deployment.
Get StartedFull production deployment with hosting, monitoring, and ongoing updates included.
Schedule DemoWhite-label solutions, multi-tenant deployments, SLA guarantees, and dedicated support.
Contact SalesSchedule a free 30-minute consultation to see how Custom Solutions for Unique Challenges can deliver measurable ROI for your organization.
Leveraging cutting-edge technologies and industry-leading tools to deliver exceptional geospatial solutions and data analytics services.
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