We architect the infrastructure behind AI-powered spatial intelligence β designing, migrating, and operating ArcGIS Enterprise, then building the agentic layer on top.
Six services covering the full life of an Enterprise GIS platform β designing it, moving it, running it, extending it with AI.
Esri built geospatial AI on three tiers: GeoAI models, assistants inside the apps your teams already use, and agentic AI reaching ArcGIS through Model Context Protocol. Most of it is early, and lands in ArcGIS Online first. Making it work inside a governed Enterprise deployment is the part nobody ships for you.
“Which transformers within 500 m of the outage haven't been inspected in two years?”
The agent plans the query, calls the right feature services with spatial and attribute filters, and returns an answer on a map. No one had to know which layer it lived in.
This picture changes with every ArcGIS release. Tracking it against your deployment is part of what we do β ask us where yours stands.
Campuses, airports, hospitals, and corporate real estate hold spatial data that never reaches the enterprise. We bring it in β a floor-aware foundation first, positioning and integration as operations demand.
Delivered with a BIM and digital-construction partner, so the handoff to operational GIS is engineered rather than redrawn.
Modernization gets deferred because nothing forces it. Increasingly something does. Esri publishes retirement schedules well ahead, so the work is plannable, budgetable, and defensible to anyone asking why now.
The money is committed. The architecture underneath it mostly isn't. That distance is the work.
Source: Deloitte survey of US investor-owned utility executives, 2026
Depth in a few industries beats breadth across many. These are the environments our architects have spent their careers inside.
Nobody goes looking for "enterprise GIS consulting." They go looking because something specific broke. These are the six we hear most.
The most common failure mode in enterprise GIS. We document the architecture, write the runbooks, and provide support that doesn't walk out the door.
Retirements arrive on a schedule. Knowing which apply to you turns a looming problem into a planned project.
AI readiness is a data and metadata problem long before it's a model problem. The gap is measurable, and fixable.
Undocumented dependencies are why modernization stalls. We map services, apps, integrations, and lineage before anything moves.
Value shows up when GIS connects to what the business runs on β work orders, assets, engineering, finance.
Lifting and shifting rarely right-sizes a deployment. Architecture, storage, and compute are where the bill actually lives.
Every engagement moves through the same five stages, each producing something concrete before the next begins.
Each one is a defined piece of work with a defined end β no open-ended hours, no discovery that quietly becomes the project. Most clients start here, then decide what comes next.
Founded in 2020 by Enterprise GIS professionals who spent their careers inside large, complicated ArcGIS environments β utility networks with millions of assets, nationwide telecom infrastructure, statewide government platforms.
The premise was simple: organizations deserve a partner who goes deep on architecture and engineering, not just configuration. As Esri turns ArcGIS into a geospatial AI platform, that premise points somewhere new.
Whether your ArcGIS environment is mature and multi-tier or you're just beginning to map out a GIS strategy, we meet you where you are. Tell us what you're running and where you want AI to take it β we'll come back with an honest read: a modernization plan, a migration path, or the shortest route to a production-ready agent.