Start here
What is edge AI? A practical guide to local inference
Learn how edge AI runs inference closer to users, devices, and data, and when local deployment is the right architecture for an AI product.
Read the guideEvergreen guides for taking AI from pilot to production.Read the latest
NYR field notes
Practical ideas for AI that runs closer.
Clear, durable guidance on edge AI, distributed inference, and rolling out reliable AI experiences across customer locations.
Architecture & operations
Foundational guides
029 min read
Architecture
Edge AI vs. cloud AI: how to choose the right architecture
Compare edge AI and cloud AI across latency, privacy, availability, cost, operations, and personalization to choose the right deployment model.
0310 min read
Deployment guide
How to plan a multi-site AI deployment
A practical framework for taking an AI workload from one successful pilot to a reliable, private, locally tuned rollout across customer sites.
The local AI brief
Four questions to ask before your next deployment.
- 01How fast must the experience respond?
- 02Where is data allowed to travel?
- 03What must work when connectivity fails?
- 04What needs to change at every site?
From reading to rollout
Bring your local AI requirements.
We’ll map the workload, locations, latency target, availability needs, and data boundaries, then show you where NYR fits.
Review my rollout