Private AI Agents

The whole agent, not just the model, inside your environment

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

What's the difference between a private LLM and a private AI agent?

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.

Capabilities

What you get

Full orchestration layer

Call routing, conversation state and decision logic all run inside your environment.

Private knowledge base

Documents and structured data the agent references stay on infrastructure you control.

Tool calls stay internal

CRM lookups, database queries and API calls the agent makes route through your own network.

Workflow engine included

Multi-step call and messaging flows run on the same private deployment, not a separate cloud service.

Conversation logs stay put

Transcripts, summaries and analytics are stored and queried within your own boundary.

Consistent access control

The same role-based access rules your team already uses extend to agent configuration and logs.

Use cases

Who this is built for

Regulated call centers

Run a full inbound/outbound agent stack without any conversation data crossing a third-party boundary.

Internal enterprise tools

Build employee-facing agents that query internal systems without exposing them externally.

Multi-brand deployments

Run isolated agent environments per brand or business unit on shared private infrastructure.

FAQ

Common questions

Do we need a private LLM to use private AI agents?

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.

Can we self-host this ourselves?

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.

Ready to deploy private ai agents?

Talk to our team about your infrastructure, compliance and scale requirements — we'll design the deployment around them.

Book a demo →