Practical AI
Practical AI, on infrastructure you control.
Most AI proposals ask you to send your operating data to someone else's cloud and trust the result. We build the other kind: private models, cited answers, and measured accuracy — designed by an engineer who owns the hardware and has thrown out the models that didn't work.
Engineering in practice
Cemetery Brain: self-hosted knowledge infrastructure
We moved a stalled indexing workflow onto an NVIDIA-equipped server, restored unattended publishing, and measured approximately 16× higher embedding throughput in a bounded benchmark. The September 2 catalog contained more than 143,000 searchable passages.
Read the case studyWhy private
Your maintenance history, customer records, financials, and internal documents are the assets that make AI useful — and the ones you least want leaving your control. We design AI systems that run on hardware you or we operate directly. No third-party training on your data, no per-seat platform lock-in, no renegotiating when the vendor changes terms.
What we build
Private language model deployment
Open models running on owned or leased GPU infrastructure, sized to your workload and your budget, with your data staying inside your boundary.
Document and records intelligence
Search and question-answering over contracts, manuals, correspondence, and decades of records — answers that cite their sources, so staff can verify instead of trusting.
Forecasting and exception detection
Models measured against your own history before anyone relies on them. If a simple method wins, we ship the simple method and tell you why.
AI-assisted engineering and automation
Software and automation pipelines that use AI for the work and keep a human approval gate on anything that touches money, records, or production systems.
How we work
We measure before we recommend
Every model gets backtested against your own data. We have thrown out sophisticated approaches because a simpler baseline performed better — and reported that result rather than shipping the more impressive-sounding system.
Humans stay in the loop where it counts
AI drafts, analyzes, and flags. People approve anything that moves money, alters records, or changes production. That boundary is designed in from the start.
Grounded answers, not confident guesses
Retrieval systems cite sources and are built to say "not in the corpus" rather than invent an answer. A system that admits uncertainty is more useful than one that never does.
Engineering economics first
High-density compute is an electrical problem before a software one. Power, cooling, and total operating cost get modeled before capital is committed.
Capability described here reflects systems we design, build, and operate ourselves — scoped per engagement.
Wondering if AI is worth it for your operation?
That's the right question — and sometimes the honest answer is "not yet." Start with a direct conversation with the engineer who will tell you either way.