Private and On-Premise AI Engineering | CalQuity Labs

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Understanding the problemWhat makes an LLM private?Privacy is established by evidence about access, retention, training use, egress, logs, keys, and deletion, not by a hosting label.8 min readRead
Understanding the problemLocal LLMs are not automatically HIPAA compliantRunning a model on your own hardware satisfies none of the Security Rule by itself. The obligations attach to access control, audit logging, integrity, transmission security and the agreements covering anyone who touches the data, and self-hosting changes where those controls live rather than whether you need them.11 min readRead
Comparing the approachesHow much hardware does your on-premise LLM need?Size hardware from in-flight model requests, context length and response deadlines. Test both memory capacity and serving latency before pricing the configuration.11 min readRead
Comparing the approachesDoes our data have to leave the building?No, but keeping it in-house costs hardware, power and a person, and in-house is a claim to be proved, not a label to be accepted.4 min readRead
Judging the evidenceHow to prepare company documents for AI searchChoose authoritative sources, assign owners and test permissions and updates before expanding internal AI search.6 min readRead
Building and operating itHow to preserve RAG access controls end to endEnforce access rules across indexes, embeddings, caches, logs and evaluation data. Test permission changes at every retained copy, not only the user-facing query.9 min readRead

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