// pillar 07 · ai-ready
GIS analysis. Map & report making.
GIS analysis. Map & report making.
DevOps.
All with AI assistance.
Honua treats AI as a workflow primitive, not a bolt-on chatbot. Analysts get spatial analysis from a sentence. Cartographers and app teams build maps, dashboards, and reports by describing them. Platform teams hand operations themselves to an AI DevOps agent. Same audit log, same governance, same MCP plumbing for all three.
mcp://honua.example.govanalysis · english → spatial query● EXAMPLE
USER
How many parcels in the SFHA flood zone, and what's the total acreage?
AGENT · tool_call
honua.spatial.query({ service: "Parcels", intersects: "FloodZones/SFHA", aggregate: ["count", "sum(area_acres)"] })
HONUA · result
{
count: 1284,
sum_area_acres: 518.4,
cited_services: ["Parcels@v8", "FloodZones@v3"]
}
AGENT
1,284 parcels intersect the SFHA flood zone — about
518.4 acres in total. Sources: Parcels v8, FloodZones v3. Want me to draft notification letters or render a map?
// the gis workflow
AI across the whole workflow.
Three jobs that used to need a specialist desktop tool, a developer, or an ops team — each one now AI-assisted on the same governed platform.
// surfaces underneath
What all three modes share.
| Surface | What it does | Used by |
|---|---|---|
| MCP server | Typed tool definitions for spatial query, render, geocode, route, and write-back. Service discovery built in. | Claude, ChatGPT, custom agents. |
| gRPC service | Streaming spatial queries, bulk operations, and back-pressure for long-running jobs. | Internal services, batch pipelines. |
| REST + SDK | Same surface as ArcGIS JS, plus modern Promise + typed responses for app code. | Web apps, mobile, scripting. |
| Governed write-back | Agent writes go through the same OIDC roles and audit log as human edits. | Field ops, analysts, agents. |
| Citations + provenance | Every response carries the source service, version, and query that produced it. | Compliance, auditing, evals. |