Every organization is sitting on years of data — tickets, logs, documents, records — and a mandate to do something with AI. The default path sends that data to someone else's cloud: a security review nobody enjoys, a compliance question nobody can close, and a per-query bill that compounds forever. For regulated, federal, and air-gapped environments, the default path isn't even an option.
ETHRX deploys open-weight language models directly onto your infrastructure — inside the perimeter, air-gapped if needed. Modern open-weight models on commodity hardware now rival hosted APIs for most enterprise workloads; we benchmark candidates against your actual use case so you see quality, latency, and cost side by side before anything is committed. Then we build the pipeline around the model — ingestion, retrieval, serving — in whatever language fits your stack. No vendor abstractions, no black-box frameworks. Production code you own.
Engagements start bounded: one corpus, one use case, and evals that show whether it works before you scale it. What hands off is documented, operable, and yours — the same standard as everything else we build.
What we deliver
Data Discovery
We map everything you have — databases, file stores, APIs, logs — profile it, and show you exactly what it can support.
Local Model Deployment
Open-weight language models running on your hardware, inside your perimeter — air-gapped if the environment requires it.
Benchmarking & Evals
Systematic evaluations of candidate models against your actual use case — quality, latency, and cost, side by side, before you commit.
Pipeline Engineering
Ingestion, retrieval, and model serving wired into your applications — written in your stack, built to run on your servers.
Where this fits
What's in your data that you can't ask a cloud about?
Talk to engineering →Why ETHRX
Local inference, provable.
No cloud APIs, no data egress — the security review is short because the architecture makes the question moot.
Your stack, your language.
Python, Go, Node.js — pipelines written the way your team already works, not in a framework you have to adopt.
Evals on your data.
We test against your corpus and your tasks, not leaderboard benchmarks that predict nothing about your use case.
No vendor lock-in.
Open-weight models on commodity hardware. Swap the model next year without rebuilding the pipeline.