Shobhraj S.

I build production AI systems that run on your own infrastructure: enterprise RAG chatbots, on-premise and self-hosted LLM deployment (Llama, Mistral, vLLM, Ollama), fine-tuning, and AI agents. Built for teams that can't send data to outside APIs.

If your security or compliance team has ruled out ChatGPT and other external AI APIs, that's the situation I work in every day. I help companies in healthcare, finance, legal, and other regulated industries run LLMs with full data residency. Private cloud or on-premise, HIPAA and GDPR aligned, on servers you control.

WHAT I DELIVER

► Private and on-premise LLM deployment. Local, open-source LLMs like Llama, Mistral, and Qwen, served with vLLM or Ollama in your VPC, on your own servers, or fully air-gapped. I handle GPU sizing, quantization, and cost planning up front, so you know what hardware you actually need before spending on it.

► Enterprise RAG and knowledge-base chatbots. Chat with your documents and get answers you can trust. I deal with the messy parts: PDFs, scans, wikis, SharePoint exports, hybrid search with reranking, citations on every answer, and retrieval evals so we can prove the accuracy instead of guessing at it.

► LLM fine-tuning. LoRA and QLoRA training on your domain data. Most projects don't need it, and I'll tell you if prompting or RAG will get you there cheaper.

► AI agents and workflow automation. Agents that use your internal tools and APIs, with guardrails and state management, measured on whether they actually complete the task end to end.

WHY CLIENTS PICK ME

Most AI projects look great in the demo and never make it to production. The boring parts kill them: no eval harness, weak retrieval, hallucinations nobody caught, zero monitoring. That's the 80% I focus on. I'm also the founder of Abstrabit Technologies, a 20-person AI development agency, so you get one senior architect making the decisions and a full team behind the build.

Recently shipped: a RAG assistant over 40,000 internal documents for a fintech client. 92% retrieval accuracy on their own eval set, deployed entirely inside their AWS VPC

Tech: Python, vLLM, Ollama, Hugging Face, LangChain / LangGraph, Qdrant, pgvector, Pinecone, Weaviate, FastAPI, Docker, Kubernetes, AWS / GCP / Azure, n8n.

HOW WE START

Send me your use case and constraints: data sensitivity, cloud or on-premise, budget. I'll reply within 24 hours with an honest read, including whether you need a self-hosted model at all or whether something simpler will do the job. Most clients start with a fixed-scope architecture review or a small proof of concept, so you can judge the work before committing to a bigger build.