Local & Private AI
Not every problem should send its data to someone else's GPU. For regulated data, IP you can't leak, or workflows that have to keep running offline, local-first AI isn't a downgrade — it's the right architecture. We've spent real time on edge inference (down to NVIDIA Jetson-class hardware), and the trade-offs are now genuinely workable.
The honest caveat: local means you own the constraints. Smaller models, quantization, and careful scoping matter more than chasing the biggest checkpoint. The wins come from matching a tightly-scoped task to a model that fits the device — not from pretending a laptop is a datacenter.
The concepts below are local- and privacy-first ideas from our research council: cases where keeping inference on-device is the feature, not a limitation.
