Custom AI Agent Development
Enterprise-ready AI assistants tailored to your workflow
Overview
Build secure AI agents powered by Azure, AWS, or Google—integrated with your real data. Automate document summaries, reporting, geoprocessing, and routine operations. Agents connect with databases, APIs, business logic, and file systems to deliver reliable, actionable output.
How It Works: Visual Breakdown
See the complete automation workflow with diagrams and code examples
System Architecture
Custom AI agent development framework using LangChain, MCP protocol, multi-LLM support (GPT-4, Claude), and tool integration for GIS-specific workflows.
⚙️Key Components
Python framework for building LLM-powered applications with chains
Model Context Protocol for standardized agent-tool communication
Azure OpenAI GPT-4 and Anthropic Claude with automatic fallback
Optimized prompts for spatial reasoning and GIS operations
Conversation history and context retrieval for multi-turn interactions
PostGIS, GDAL, and API tools accessible to agents
Deploy agents as n8n workflow nodes for easy orchestration
Visual Examples
See the solution in action with real dashboard examples and visual comparisons
Key Features:
Key Features:
💡Note: The dashboard screenshots above are placeholders. Actual screenshots will be added after finalizing native in-app dashboards or capturing real application screenshots. Image paths are specified for easy integration.
Automated MCP Agent Workflow
Powered by n8n, Make.com, and Model Context Protocol agents
User query or scheduled task
“Act as a geospatial assistant for solar development. When asked about a project, retrieve its latest stats from PostGIS (project area, buildable acreage, permit status), query NOAA API for weather alerts, check NREL solar resource data, summarize findings in executive language, and answer follow-up questions with context from previous queries. Maintain conversational memory for multi-turn interactions.”
- Natural language responses (conversational)
- PDF reports (generated on demand)
- Maps and charts (Mapbox, Plotly)
- Database updates (audit trails)
- Action execution (send emails, create tasks)
- Conversational memory (multi-turn context)
Key Features
Natural language interface (Slack, Teams, web chat)
Multi-turn conversations (context memory)
Database integration (PostGIS, SQL Server, MongoDB)
API integration (50+ services)
Document retrieval (SharePoint, Dropbox)
Map and chart generation (Mapbox, Plotly)
Role-based access control
Audit trails (all queries logged)
Customizable business rules
Technology Stack
API Integrations
Real-World Results
Challenge
Analysts spend 5-10 hours per week answering routine questions from executives: 'What's our pipeline in Texas?', 'Which sites are awaiting permits?', 'Show me flood risk for property XYZ'. Manual queries of PostGIS database, Excel reports, and manual map generation.
Our Solution
Custom MCP agent deployed as Slack bot. Natural language interface: '@geobot Show pipeline in Texas'. Agent queries PostGIS, retrieves 23 properties, calculates total acreage and buildable area, checks permit status from project management database, generates Mapbox map with color-coded markers, and responds in Slack with summary + map image. Follow-up questions: 'Which have flood risk?' Agent re-queries FEMA data, highlights 4 properties, explains risk levels.
Results Achieved
Flexible Pricing Options
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 SalesFrequently Asked Questions
Common questions about our GIS automation solutions
Still have questions?
Our GIS automation experts are here to help. Schedule a free consultation to discuss your specific needs.
Schedule Free ConsultationReady to Transform Your GIS Workflows?
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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