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Private VPC RAG & Vector Engines

Akshora AI Labs deploys private retrieval-augmented generation (RAG) inside your own VPC. Self-hosted Qdrant or Milvus vector clusters and quantized Llama 3 models let teams search engineering schematics, SOPs and internal knowledge with zero cloud egress, so no document content is sent to an external API.

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What's included

  • Self-hosted Qdrant or Milvus vector clusters
  • Quantized Llama 3 models running locally
  • Semantic search over engineering schematics and operational SOPs
  • Deployment inside your private enterprise perimeter with zero external cloud API leakage
  • Full Git repository transfer and architecture runbooks at handoff

Common questions

Does any data leave our network with private RAG?

No. With Akshora AI Labs' private RAG, both the vector database and the language model run inside your VPC or on-prem perimeter, so there is zero cloud egress and no external API leakage.

Which vector database and language model do you use?

Akshora AI Labs uses self-hosted Qdrant or Milvus for vector search and quantized Llama 3 models, such as Llama 3 8B, for generation. The exact stack is chosen during discovery from your data volume and hardware.

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