Practice 01 / 05
AI Enablement & Private AI Deployments
Capable AI on your own infrastructure. Nothing leaves the building.
Engagement 0391|Pilot sent customer data to a public model
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.