The Whole Stack,
Inside Your Walls
Models, retrieval, storage, and serving — deployed on hardware you own, on-prem, in your private cloud, or fully air-gapped. Your data never leaves, there is no per-token bill, and the system keeps running with no outbound connectivity. Built from hardened runbooks on NVIDIA DGX and Red Hat Enterprise Linux.
A rented model means your data lives on someone else's servers.
For a chatbot answering strangers, that is fine. For an agent reasoning over your formulas, part costs, supplier terms, and customer records, it is a compliance problem and a business risk. NIS2 now names manufacturers and logistics operators directly — on top of GDPR, HIPAA, and SOC 2.
We remove the question entirely. The model, the vector store, the graph, the serving layer, and the retrieval that ties them together all run on hardware you own. Nothing sensitive crosses your boundary, and the system works whether or not the internet does.
The hard problem isn't storage — it's retrieval →Your data never leaves
Regulated and industrial operations cannot ship formulas, BOMs, cost structures, and customer records through a third-party API. We deploy the whole stack inside your walls — on-prem, private cloud, or fully air-gapped — so training data and every inference stay under your control by design.
NIS2 now names manufacturers and logistics operators directly, on top of GDPR, HIPAA, and SOC 2. On-prem is the deployment that satisfies all of them at once.
Own the whole stack
Model weights, vector store, graph database, serving layer, and the retrieval that ties them together — all running on hardware you own. No per-token bill, no vendor deprecating the model you built on, no rate limit between your agents and the work.
The system keeps running if the internet goes down, if a vendor changes terms, or if you are on a plant floor with no outbound connectivity.
Runs at the edge of the plant
A tuned model on your own GPUs answers in milliseconds, not over a WAN round-trip to someone else's cloud. When an agent makes thousands of decisions a day, latency and cost are the difference between a pilot and a system you can afford to scale.
Local inference on a single GPU node turns a 15-second frontier API call into a sub-second answer — and removes the metered cost entirely.
Air-gap capable from day one
For the most sensitive environments we build fully disconnected: models and weights staged, dependencies mirrored, and the entire stack brought up with no outbound connectivity. Every runbook is written to work online or air-gapped without changing the architecture.
SELinux enforcing, firewalld on, and scoped service accounts are the baseline, not an afterthought. A full air-gapped build lands in 6 to 9 days.
Six Layers, One System
The full production stack — inference, retrieval, orchestration, storage, compute, and interface — each deployed from a hardened runbook that takes it from bare hardware to fully operational.
Inference & Serving
vLLM · NVIDIA NIM microservices · KServe · llm-d distributed inference · NVIDIA Run:ai · Ray clusters · LiteLLM proxy
Retrieval & Knowledge
Haystack pipelines · Qdrant vector search · Neo4j graph reasoning · custom XML & object-storage ingestion · embedding pipelines
Orchestration Platform
Red Hat OpenShift AI · NVIDIA GPU Operator · GPU node pools & hardware profiles · Kueue distributed workloads · KServe LLMInferenceService
Storage & Data
NetApp AFX · ONTAP SVM · NetApp Trident on OpenShift · ONTAP CLI & REST operations · NetApp DataOps Toolkit
Compute & Network
NVIDIA DGX systems · DGX as OpenShift worker nodes · GPU platform baseline · BlueField DPUs & the DPF operator
Interface & Access
Open WebUI · OpenShift AI identity & access · systemd service standards · role-based access & scoped credentials
Runs on RHEL 9.4+ / RHEL 10 and NVIDIA DGX OS 7 / Ubuntu 24.04. SELinux enforcing, firewalld on, air-gap capable. A full build lands in 3 to 5 days online, 6 to 9 days air-gapped.
Sized to Your Operation
Single GPU node
vLLM or NIM serving a tuned model on one DGX or GPU server, with Qdrant and Neo4j alongside and Open WebUI on top. The fastest path to a running on-prem system for a scoped set of agents.
Containerized RAG stack
The full retrieval stack in Docker — serving, vector, graph, and ingestion — reproducible and portable across your RHEL or DGX OS hosts. One command brings the system up, the same way every time.
OpenShift AI cluster
Production Kubernetes: GPU Operator, KServe model serving, Kueue for distributed workloads, NetApp Trident for storage, and DGX nodes as workers. Scales across many GPUs and many teams.
Hardware to Handoff
Size the hardware
We size GPUs, memory, storage, and network to your models and throughput — DGX, standard GPU servers, or your existing fleet — and lay out the platform baseline before anything is racked.
Stand up the stack
Serving, retrieval, storage, and orchestration deployed from hardened runbooks. SELinux enforcing, firewalld on, systemd service standards, and scoped access from the first host.
Integrate & validate
We wire Open WebUI and your agents to the serving and retrieval layers, load your models and knowledge, and validate the whole system end to end against your real workloads.
Hand off with runbooks
You get the operator runbooks: how to update the OS and models, monitor the GPUs, back up the data, and recover the system. Documented so your team can run it, not just watch it.
Your models, your hardware, your walls.
We stand up the full stack in your environment, wire your tuned models and agents into it, and hand you the runbooks to run it. On-prem, private cloud, hybrid, or air-gapped — sized to your compliance and your load.
Pair it with a model tuned on your operation and a team of agents built to run the work, and the whole system lives where your compliance team needs it.