You will get a deployed AI agent that searches PDFs and the web
Project details
You will get a fully deployed AI agent that can search your own documents and the live web to answer questions — with source citations every time. No hallucinations, no guessing. Every answer shows exactly where it came from: page numbers for PDFs, clickable URLs for web results.
I built ResearchMind (researchmind-sigma.vercel.app) using this exact stack — LangChain agents, FAISS vector search, Tavily web search, FastAPI backend, and React frontend. It is live and working right now. You can test it before hiring me.
What makes my approach different: I do not just wrap ChatGPT in a UI. I build proper agentic pipelines where the AI decides which tool to use, retrieves the right information, and generates cited answers. The result is a system that actually works in production, not just in demos.
I have delivered a live client website and multiple deployed AI projects. I write clean, tested Python code with proper error handling. You get source code, documentation, and post-delivery support.
I built ResearchMind (researchmind-sigma.vercel.app) using this exact stack — LangChain agents, FAISS vector search, Tavily web search, FastAPI backend, and React frontend. It is live and working right now. You can test it before hiring me.
What makes my approach different: I do not just wrap ChatGPT in a UI. I build proper agentic pipelines where the AI decides which tool to use, retrieves the right information, and generates cited answers. The result is a system that actually works in production, not just in demos.
I have delivered a live client website and multiple deployed AI projects. I write clean, tested Python code with proper error handling. You get source code, documentation, and post-delivery support.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging FaceAI Models
LLaMAWhat's included
| Service Tiers |
Starter
$150
|
Standard
$250
|
Advanced
$400
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | |||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | - | - | - |
Source Code |
Frequently asked questions
About Aniket
AI Engineer specializing in RAG LLM APIs FastAPI and Python
Gurgaon, India - 8:38 am local time
Steps for completing your project
After purchasing the project, send requirements so Aniket can start the project.
Delivery time starts when Aniket receives requirements from you.
Aniket works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery and requirements
Review client documents, understand the use case, and define the scope and tech approach.
Build and test
Build the RAG pipeline, integrate LLM APIs, and test with real documents from the client.