alex nodeland

mathaudio dspdistributed systemsai

i build ai systems, mostly the parts nobody demos: agent orchestration, evaluation loops, and the semantic layer underneath that has to be right before any of it works. currently senior ai engineer at perch insights.

before that i co-founded archanan in singapore and ran it as ceo for four years — we built cloud emulators of supercomputers so people could develop at scale without waiting in a queue. then led engineering at musiio, a music-ml company soundcloud later acquired. earlier still: wavelet bases for audio compression at stony brook, and firmware for guitar pedals before that.

i write rust on weekends, mostly audio synthesis and probabilistic programming. the backgrounds on this site are live simulations rather than video — the gear icon opens their controls.

what i work on

ai systems
agent orchestration, rag, tool use, and the failure modes that only appear under real traffic
infrastructure
aws, kubernetes, infrastructure as code, and deploy pipelines that nobody has to babysit
data engineering
pipelines, semantic models, and lineage you can actually audit after the fact
evaluation & observability
eval sets, feedback loops, and catching a regression before a customer does
technical strategy
architecture review, build-vs-buy, and deciding which half of the roadmap to cut
creative technology
audio dsp, synthesis, and generative visuals

consulting

i take on a few engagements a year, split about evenly between two kinds of problem people bring me. the first is a team whose llm prototype demos well and falls over in production — i get called in after that happens, and the fix is almost never the model; it is the data model, the evals, or the failure handling. the second is the earlier question, before anything is built: what to build with ai, or whether to build it at all.