On-prem LLM deployment

An on-prem LLM that remembers how your work actually connects.

Oryne brings LLM inference, embeddings, document retrieval, and graph memory behind your firewall so teams can use AI on sensitive operational context.

Start with OryneCompare options
on-prem LLMon premise LLMenterprise local LLMself hosted LLMon-prem AI assistant

Primary search intent

on-prem LLM

Security, legal, healthcare, finance, government, and executive teams that cannot send internal context to external AI providers.

Deploy on local machines, private servers, or Kubernetes clusters
Run with open-weight models such as Llama, Mistral, Qwen, or custom endpoints
Keep embeddings, vectors, transcripts, and graph data inside your environment

What teams get

Searchable memory that turns context into action.

Launch private AI workflows without changing data-residency rules

Let teams ask across internal knowledge with audit-friendly provenance

Reduce vendor exposure for confidential documents and conversations

Full memory pipeline on-prem

Oryne is not just a hosted chat UI with a privacy promise. It is designed so the memory pipeline can run wherever your organization already secures sensitive data.

  • Local ingestion for mail, messages, docs, and meetings
  • Private embeddings and hybrid retrieval
  • Graph storage for people, projects, commitments, and sources

Model choice without data egress

Teams can use local model runtimes or private endpoints based on their hardware, compliance boundaries, and latency requirements.

  • Bring your own LLM endpoint
  • Operate in disconnected or restricted networks
  • Tune deployment shape for desktop, server, or cluster use

Enterprise controls

On-prem AI needs more than inference. Oryne focuses on access control, retention boundaries, audit trails, and source-backed answers.

  • Role-aware memory access patterns
  • Retention policies for sensitive source systems
  • Traceable answers with supporting context

Questions

Common evaluation questions.

Can Oryne run without a cloud LLM provider?

Yes. Enterprise deployments can be configured around local or private model endpoints so the assistant does not require sending prompts to public LLM APIs.

Who needs an on-prem LLM assistant?

Organizations with regulated data, sensitive negotiations, privileged legal material, proprietary research, or strict data-residency requirements are strong fits.

Does on-prem mean slower search?

Not necessarily. Oryne uses hybrid retrieval and graph traversal so common recall workflows can stay fast even when the data remains local.

Continue researching Oryne

Air-gapped AIEnterprise AI SearchDocument RAG and OCR

Compare alternatives

Oryne vs SupermemoryOryne vs Mem0Oryne vs Zep / Graphiti