A private LLM alone isn't a private deployment — the agent's knowledge base, call routing, tool integrations and conversation logs all need to sit inside the same boundary. Lira deploys that entire layer, not just the model.
Enterprise deployments · dedicated onboarding · custom SLAs
A private LLM is just the language model. A private AI agent is the complete system around it — the knowledge base it retrieves from, the tools it calls (CRM lookups, calendar booking), the conversation history it stores, and the orchestration logic that decides what to say next. For a deployment to be genuinely private, all of that needs to run inside your infrastructure, not just the model inference step.
Call routing, conversation state and decision logic all run inside your environment.
Documents and structured data the agent references stay on infrastructure you control.
CRM lookups, database queries and API calls the agent makes route through your own network.
Multi-step call and messaging flows run on the same private deployment, not a separate cloud service.
Transcripts, summaries and analytics are stored and queried within your own boundary.
The same role-based access rules your team already uses extend to agent configuration and logs.
Run a full inbound/outbound agent stack without any conversation data crossing a third-party boundary.
Build employee-facing agents that query internal systems without exposing them externally.
Run isolated agent environments per brand or business unit on shared private infrastructure.
It's the common pairing, since running the orchestration layer privately while calling out to a public model API defeats much of the purpose — but the two are configured independently if you have a specific reason to mix them.
Yes, with our team's support for initial setup and ongoing updates — this is designed for teams with existing infrastructure capability, not a fully managed black box.
Talk to our team about your infrastructure, compliance and scale requirements — we'll design the deployment around them.
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