AI_AUTOMATION_SERVICES

RAG & LLM Infrastructure

Set up and optimize RAG and LLM infrastructure for private, on-premise AI applications.

15+years

ProductionAI systems

330+sites in production

When your sensitive data cannot leave your infrastructure, I build secure RAG and LLM solutions entirely on-premise or within your private cloud. This ensures your confidential information remains fully under your control.

I implement advanced RAG architectures using vector search with Pinecone and generate precise embeddings for your data. For LLMs, I deploy and fine-tune models like Qwen or Llama on your GPU infrastructure, managed efficiently with MCP servers.

You get a powerful AI system that leverages your proprietary data without compromising privacy or security. This allows you to build intelligent applications that understand your unique context, all while keeping your most valuable assets protected.

Problems it solves

Need for secure, private LLM solutions for sensitive data.
Challenges in deploying and managing self-hosted LLMs on GPU.
Difficulty implementing efficient RAG systems with vector databases.

What's included

Vector search implementation with Pinecone.
Deployment of self-hosted LLMs (Qwen, Llama) on GPU.
MCP server setup for AI workloads.
Integration of embedding models for RAG.

How it works

RequirementsDefine your data privacy and performance needs.

Infrastructure SetupConfigure Pinecone, GPU servers, and MCP.

LLM & RAG IntegrationDeploy LLMs, embedding models, and RAG pipelines.

Testing & OptimizationEnsure system stability, security, and performance.

Best fit

Businesses requiring robust, private, and scalable RAG and LLM solutions for on-premise deployment.

Stack

Pineconeembeddingsself-hosted LLMMCP

Describe your task

No spec needed. Just tell me the problem — I'll find a solution.