Browse categories
Explore
Fiverr Pro
English
$
USD
I will build a smart ai agent with custom rag pipelines
Utkarsh
About this gig
Stop relying on AI chatbots that hallucinate. To get real business value from Large Language Models, they need to understand YOUR data.
I engineer custom, production-grade AI agents and Retrieval-Augmented Generation (RAG) pipelines that securely integrate with your proprietary enterprise knowledge base.
As a Principal AI/ML Engineer, I specialize in moving AI from experimental notebooks into highly scalable software applications. In past commercial deployments, my custom LLM architectures boosted user engagement by 40%.
What I Build:
- Context-Aware RAG: Secure integration of your PDFs, wikis, or databases using Vector DBs so the AI answers accurately based on your data.
- Smart AI Agents: Conversational assistants capable of advanced reasoning and multi-step workflows using LangChain and LangGraph.
- Scalable Backends: Robust API layers built with FastAPI and Redis to handle massive loads (30+ RPS) with zero downtime.
- End-to-End MLOps: Cloud deployment and containerization via Docker and AWS.
Stack: Python, LangChain, OpenAI, FastAPI, Vector DBs, AWS.
Message me with your project goals, and let's get started!
Get to know Utkarsh
Utkarsh
Generative AI, RAG, and Edge Computing Expert
- FromIndia
- Member sinceApr 2023
- Avg. response time4 hours
- Last delivery1 year
Languages
English, Hindi
I'm an AI & ML Engineer and Principal Data Scientist turning models into scalable, revenue-driving systems. I build low-latency GenAI agents, RAG workflows, and FastAPI backends serving 30 RPS and 180 RPM. On the edge, I deploy YOLO and CNNs to Jetson and Raspberry Pi with 96% accuracy and 30% faster inference. My recommendation and prediction models have delivered 8% sales lifts and 22% better forecast accuracy for brands like Mondelez and Wipro. Stack: Python, PyTorch, LangGraph, OpenCV, Docker, AWS. Ready to build? Let's connect.
Other AI Development Services I Offer
FAQ
Is my proprietary data secure? Will it be used to train public models?
Yes, your data is completely secure. I build RAG pipelines using enterprise-tier APIs and dedicated Vector Databases. Your documents are used strictly for context retrieval and are never used to train base models like OpenAI's public GPT.
How is this different from just using standard ChatGPT?
Standard LLMs often hallucinate facts and have no knowledge of your internal business operations. A custom RAG pipeline restricts the AI to only pull answers from the specific documents or databases you provide, ensuring highly accurate and factual responses.
What types of data or documents can the AI agent process?
I can integrate a wide variety of data sources, including PDFs, CSV/Excel files, SQL databases, Word documents, and internal wikis like Notion or Confluence.
What technology stack do you use to build the backend?
I architect these systems using Python, LangChain, and LangGraph for the AI workflows. For the backend infrastructure, I use FastAPI and Redis to ensure the application can handle high traffic (30+ RPS) with low latency, containerized with Docker.
Do you help with deploying the agent to the cloud?
Yes. If you select the Premium Enterprise package, I provide a fully containerized Docker setup and deployment configuration. It will be completely ready to host on your preferred cloud provider, such as AWS, GCP, or Azure.
