Everything required to run AI privately — and well.

We install, configure and secure open-weight language models on your own hardware or private cloud, so AI capability is available to your organization without data leaving your environment.

We build Model Context Protocol (MCP) servers that expose approved actions from your internal systems to AI assistants — with authentication, per-user authorization, audit logging and human approval built in.

We fine-tune open-weight models on customer-approved data using parameter-efficient methods such as LoRA and QLoRA, then measure the improvement against the base model before anything is deployed.

We build internal assistants that answer questions from your policies, records and knowledge bases using retrieval-augmented generation — with source citations and document-level permissions.

We build agents that perform multi-step workflows across your systems — triaging requests, preparing documents, updating records — with explicit scopes and human approval for sensitive steps.

We implement the controls that make AI defensible to your security, privacy and audit functions: identity-aware access, audit logging, data protection, usage policy and ongoing evaluation.

We tune existing AI infrastructure for speed, capacity and cost — through model selection, quantization, batching, caching and right-sizing of GPU resources.

We deliver practical, role-specific workshops that teach staff how to use your internal AI tools in their daily work — and how to use them safely.

We train your infrastructure and operations staff to administer, troubleshoot, update and secure the AI platform we deploy — so you are not dependent on us.

We operate your private AI platform on an ongoing basis — monitoring, patching, model upgrades, backups and support — under a defined service agreement.