Infill AI™
AI Compute at The Edge of The Edge™
Infill AI is a new class of urban AI infrastructure optimized for the inference era.
Infill AI harnesses underutilized urban space and fragmented urban power to deploy autonomous neighborhood-scale inference sites.
These sites operate together as resilient city-scale inference clusters.
The result is Local Inference Infrastructure-as-a-Service (LaaS).
Inference is rapidly becoming the dominant AI workload—and demand is concentrating in cities.
Distributed inference — AI demand is shifting from centralized training toward inference workloads deployed across many locations closer to users and data
Latency, locality, reliability — Inference workloads require fast response times, proximity to users and data, and predictable operation
Urban readiness — Cities contain the buildings, power, and fiber needed for AI infrastructure but lack purpose-built inference infrastructure
Local Inference Infrastructure-as-a-Service (LaaS):
Dedicated urban inference infrastructure for institutional AI workloads
Infill AI deploys city-scale clusters of neighborhood-scale AI sites designed specifically for inference workloads. Each site contributes inference capacity to a city-scale cluster that delivers on-premises-like latency and control without requiring customers to own or operate AI infrastructure themselves.
Urban & repurposed — Existing buildings leveraged as AI infrastructure
Proximate — Inference compute located near users, data, and institutions
Autonomous — AI-operated infrastructure with minimal on-site staffing
Scalable — Cluster expansion city by city
Designed for cities. Built for autonomy.
Infill AI focuses on cities with active business communities, academic institutions, healthcare systems, and municipal operations—places where AI demand exists but hyperscale infrastructure is distant or constrained.
Each Infill AI site is created as a “building within a building.” Standardized GPU pods, power, cooling, and networking are deployed inside existing buildings — enabling rapid activation while minimizing disruption to surrounding communities.
Neighborhood-scale — 0.25–2MW GPU-power urban sites sized for low-latency inference, not remote hyperscale training
Autonomous operations — Fully AI-operated infrastructure that reduces staffing and operating costs
Clustered resilience — City-level resilience through coordinated clusters of sites rather than a single massive facility
Fragmented Urban Power → City-Scale AI infrastructure that works with cities, not against them.
Infill AI is designed to integrate cleanly into urban environments rather than overwhelm them. By embedding AI inference compute across smaller sites, Infill AI reduces strain on city infrastructure, turning inference compute into essential urban infrastructure.
Lower urban stress — Smaller, distributed sites reduce pressure on power infrastructure, cooling water, and permitting pipelines
Reuse over sprawl — Reactivates underutilized urban real estate instead of consuming new land
Practical energy reuse — Closed-loop cooling enables reuse of GPU waste heat for co-located tenants and adjacent buildings
Powered by Arctevity.
Infill AI city clusters are created and operated by Arctevity, a technology company specializing in autonomous building systems that unify physical infrastructure and intelligent operations. Arctevity’s ArcX™ platform is the foundation that makes fully AI-operated facilities possible at dramatically lower cost and complexity.
For more information see www.arctevity.com.
Infill AI is the urban infrastructure for the inference era—deployed quietly, incrementally, and in the cities where AI is used.
If you are a building owner, developer, city stakeholder, utility, potential partner, or interested in using Infill AI for low-latency inference compute, we welcome a conversation. Email us at info@arctevity.com.