srvtech.dev service
AI Infrastructure Deployment (On‑Premise)
AI infrastructure is needed when data control, security and independence from external APIs matter.
We design on-premise solutions: self-hosted LLMs, RAG search, vector databases, APIs, task queues, monitoring, access control and enterprise integrations.
Business outcomes
01
Data control: documents, prompts and models stay inside your infrastructure.
02
Self-hosted LLM, RAG, vector databases, APIs and automation workflows.
03
Architecture designed for security, scaling, monitoring and support.
Use cases
01
Enterprise search and Q&A across documents, policies and archives.
02
Internal AI services for support, analytics, legal, HR and engineering teams.
03
Isolation of sensitive data from public AI APIs and cloud tools.
How we implement it
01
Analyze security requirements, data, load, hardware and deployment constraints.
02
Design models, RAG, vector storage, APIs, access control and monitoring.
03
Deploy MVP, test quality, load and security, then hand over documentation.