The private mandate.
Language models, research agents, and decision systems — designed around your data, your obligations, and your taste. Privately hosted. No off-the-shelf integrations.
Adoption is visible. Deployment is deliberate.
Most firms adopt AI. They connect a vendor API, run a pilot, publish an internal memo. What they rarely do is deploy it — build systems around their own data with their own constraints, governed by their own standards rather than a platform’s defaults. The distinction matters. Adoption is a subscription. Deployment is infrastructure.
Nemco builds applied AI under private mandate. We host models on infrastructure our clients control, train retrieval against data they own, and design decision systems that answer to their judgment — not to a vendor’s roadmap. The work is bespoke, slow by industry standards, and built to last.
The four questions
we ask first.
Data Provenance
Where trust begins. Language models are only as reliable as the data they reference. We audit source quality, establish retrieval boundaries, and build systems that cite their sources — so the human in the loop can verify before they decide.
Model Governance
Where control must be explicit. Who accesses the model. What it can see. How its outputs are logged. Governance is not a compliance checkbox — it is the architecture that determines whether AI is a tool you control or a dependency you cannot audit.
Integration Depth
Where value compounds. A model that answers questions is useful. A system that retrieves, evaluates, and routes decisions across your operations is transformative. The difference is integration — and integration requires understanding the business, not just the technology.
Organizational Readiness
Where adoption succeeds or stalls. AI deployments fail when the organization is not prepared to trust, verify, and maintain what was built. We assess readiness before we build, so the system we deliver is one the team will actually use.
Four stages. One system.
We work sequentially — from understanding to deployment. Each stage produces a documented artifact, so the system we build is governed from inception.
A private inventory
We study the data, the workflows, and the decisions that AI will touch. Not a capability demo — a disciplined assessment of where intelligence adds value and where it adds risk.
A governed architecture
We specify the system — model selection, hosting infrastructure, retrieval boundaries, access controls, output logging. Every design decision is documented and defensible.
A private instance
We build and deploy on infrastructure our clients control. Private hosting, private data, private models. The system is theirs — we are the architects, not the landlords.
A knowledge transfer
We train the team that will maintain the system, document every integration point, and establish the review cadence that keeps the deployment aligned with evolving needs.
Intelligence that answers to judgment,
not to a vendor’s roadmap.
Applied AI is not a product we sell — it is a capability we build. Each engagement produces a system that runs on our client’s infrastructure, trained against their data, governed by their policies. When the engagement ends, the system stays. No subscription, no platform dependency, no data leaving premises.
Kayphi, our own technology venture, emerged from this practice — a private tool built with the same rigor we bring to client work. It is proof of method, not a prerequisite for engagement.
Introductions are
by request.
We accept a small number of new engagements each year. If your work requires a standard higher than the one available to you today, we would welcome a conversation.