Custom LLM Deployment

A custom installation means you own and control the entire AI stack—from the model weights to the vector database to the user access layer. Unlike public APIs, which require you to send data to someone else's cloud, our setup keeps everything in your environment. You avoid data leakage, ensure compliance, and can fully tailor the model to your business logic, internal systems, and workflows.

Yes. We specialize in secure, private deployments. Whether you prefer air-gapped servers, a VPC on AWS/Azure/GCP, or a hybrid infrastructure, we adapt the installation to your needs. Our team collaborates with your IT and security leads to align the setup with existing access controls, network policies, and compliance requirements.

We can install a wide range of open-source models like LLaMA, Mistral, or Mixtral, as well as support licensed models depending on your needs. If you already have a license for a proprietary model, we'll handle the setup and ensure it integrates with your systems securely. We help you choose the right model based on your performance, latency, and privacy requirements.

We securely ingest your documents—contracts, SOPs, EHRs, support tickets, spreadsheets, and more—and embed them into a private vector database. From there, we configure a RAG pipeline that allows the model to retrieve and reference this data in real time. The data is never used to train the base model unless explicitly requested, and everything remains encrypted and fully under your control.

Yes. After installation, we provide hands-on training for your admins and users, ensuring your team knows how to operate, manage, and expand your system. We also offer optional support packages for continued optimization, scaling, or future fine-tuning based on your evolving needs. You'll never be left guessing how your system works or how to improve it.

We evaluate vLLM, Ollama, HuggingFace TGI, and other serving runtimes against your specific hardware, concurrency targets, and model format. vLLM is typically recommended for high-throughput GPU clusters requiring continuous batching; Ollama suits lighter single-node installs or developer environments. The selection is driven by your production requirements, not a fixed preference.

Yes. Air-gapped deployments require all dependencies—model weights, container images, embedding models, vector databases, and package mirrors—to be pre-staged inside the secure enclave before the network boundary closes. We handle that pre-staging process end-to-end and validate the installation operates correctly with zero external egress.

We conduct a structured model selection workshop during the discovery phase, mapping your use case requirements (instruction following, document Q&A, code generation, multilingual support) to candidate open-source models such as Llama 3, Mistral, Mixtral, or Qwen. We then benchmark top candidates against your hardware and latency targets before committing to an architecture.

We can deploy into an existing Kubernetes cluster or provision a new one as part of the engagement. The deployment package includes Helm charts for the inference server and vector database, NVIDIA GPU operator configuration, autoscaling policies, and integration with your existing ingress and secrets management tooling. No prior GPU cluster experience on your team is required.

The inference infrastructure we install is the foundation on which agentic workflows and automation pipelines run. Once your private LLM is deployed and integrated with your internal data systems, we can layer orchestration logic—tool use, multi-step reasoning, scheduled automation—on top of the same private stack without routing any data through external APIs.