Practice 01 / 05
AI Enablement & Private AI Deployments
Capable AI on your own infrastructure. Nothing leaves the building.
The situation
Your organization wants what large language models offer, like search over internal knowledge, drafting, summarization, and coding assistance, but your data cannot be sent to a third-party API. Or you have adopted AI tools ad hoc, and nobody can say what is running where, on whose data.
What we do
- Private and on-premise LLM deployments: model selection, hardware sizing, and a production-grade serving stack (Ollama, vLLM, and similar).
- Retrieval-augmented generation (RAG) over your documents and data: grounded answers, honest quality evaluation.
- AI environment setup for development teams: Claude Code, Copilot, and agentic workflows adopted with the quality-control discipline that makes the speedup stick.
- AI readiness assessments: where AI genuinely pays off in your workflows, what your data and systems can support today, and a sequenced roadmap.
- Governance foundations: access control, audit logging, and usage policy that satisfies compliance without strangling adoption.