Approved catalog
We define which models are allowed, where they run, how they are updated, and who approves changes before usage spreads.
Ollama and LM Studio deployments
Ollama and LM Studio deployments that teams can operate
We standardize local AI environments so engineering, IT, and operations teams can use approved models without configuration drift, unmanaged downloads, or unclear support responsibility.
Problem and risk
Local AI tools need an operating model
Ollama and LM Studio often start as individual experiments. Without a shared standard, teams accumulate unapproved models, inconsistent settings, unknown data paths, and support gaps that make the environment hard to secure or reproduce.
Repeatable deployment
Workstations, Apple Silicon pilots, NVIDIA servers, and local endpoints need documented baselines rather than ad hoc configuration.
Clear handoff
Monitoring, operating commands, incident handling, and rollback steps make local AI usable by internal IT instead of a one-person experiment.
Fit and delivery
Deployment planning for teams standardizing local AI
The engagement turns tool choice into an implementable topology, support model, and deployment backlog.
Who this is for
Engineering, IT, security, operations, and AI pilot teams that need supportable local models without letting configuration drift define the architecture.
What we deliver
Deployment topology, approved model catalog, access controls, hardening steps, monitoring points, hardware assumptions, ownership recommendations, and runbooks.
How engagement works
We size real workloads, document constraints, recommend a topology, and validate a controlled pilot against speed, reliability, data handling, and supportability.
Move from local AI experiments to a supportable environment
Plan the model catalog, access controls, deployment topology, and operating runbooks before the tool spreads informally.