You will get a RAG AI Chatbot that answers questions from your documents
Project details
I will build a Retrieval-Augmented Generation (RAG) AI chatbot that can answer questions from your documents with high accuracy. This system allows users to upload multiple documents and interact with them through a simple chat interface.
The solution includes a document processing pipeline, vector embeddings, and semantic search, enabling the AI to retrieve relevant information before generating responses.
This is ideal for businesses that want to create AI document assistants, knowledge-base chatbots, contract analysis tools, or internal research assistants.
Depending on the package, the system can also support OCR for image-based PDFs, advanced retrieval optimization, and production-ready APIs for integration into existing applications.
With 5+ years of experience building production AI systems, I focus on delivering reliable, scalable, and practical AI solutions.
The solution includes a document processing pipeline, vector embeddings, and semantic search, enabling the AI to retrieve relevant information before generating responses.
This is ideal for businesses that want to create AI document assistants, knowledge-base chatbots, contract analysis tools, or internal research assistants.
Depending on the package, the system can also support OCR for image-based PDFs, advanced retrieval optimization, and production-ready APIs for integration into existing applications.
With 5+ years of experience building production AI systems, I focus on delivering reliable, scalable, and practical AI solutions.
AI Algorithms
Autoencoder, Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, Image Recognition, Machine TranslationAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, StreamlitAI Models
ChatGPT, DALL-E, GPT-3, GPT-4, GPT-Neo, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$110
|
Standard
$330
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 5 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 | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$10
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KC
Kailash C.
Dec 7, 2024
Prompt Engineer for Claude AI Model - PDF Data Extraction
About Shivangi
AI Engineer | GenAI | Agentic AI | RAG Systems | Automation
Noida, India - 12:08 am local time
Delivered AI solutions across FinTech, Enterprise SaaS, and EdTech, focusing on practical, deployable systems.
Expertise includes LLMs, LangChain, Agentic AI, RAG architectures, document processing pipelines, OCR systems, and backend AI infrastructure.
Professional Experience
American Express — Gurugram, India
SDE II (AI / ML / GenAI)
Oct 2025 – Present
Working on enterprise GenAI systems for contract intelligence and legal document analysis.
Key Contributions
• Designed a RAG platform for analyzing large volumes of third-party contracts.
• Built a multi-stage document pipeline (parsing, chunking, embeddings, semantic indexing).
• Developed agentic AI workflows for:
Contract Q&A
Contract summarization
Contract comparison
Clause detection and extraction
Key field extraction (payment terms, obligations, renewals)
• Implemented LLM orchestration using LangChain for tool-enabled AI agents.
• Built evaluation pipelines to reduce hallucination and improve retrieval accuracy.
• Developed production APIs for enterprise integration.
Magic Software — Noida, India
Machine Learning Consultant
Oct 2021 – Oct 2025
Built AI backend systems for document intelligence, OCR pipelines, and LLM-powered content generation platforms used in fintech and edtech.
Gender Bias Detection (BERT)
• Built a multi-label BERT classifier for detecting gender bias in textual datasets.
• Fine-tuned BertForMultiLabelSequenceClassification using BCEWithLogitsLoss.
• Developed NLP preprocessing and dataset annotation pipelines.
Document Intelligence & OCR Platform
• Built end-to-end document processing pipelines for invoices, financial forms, tax documents, and IDs.
• Combined OCR + NER models for structured data extraction.
• Integrated Detectron (layout detection) and Camelot (table extraction) for complex documents.
• Developed OpenCV modules for document preprocessing and checkbox detection.
• Built FastAPI + SQLAlchemy backend services exposing extraction APIs to enterprise systems.
Hewlett Packard Enterprise — Bangalore, India
Machine Learning Intern
Apr 2021 – Jul 2021
• Implemented GAN-based 3D reconstruction using StyleGAN2 and GAN2Shape.
• Built a Dash-based data visualization application.
• Developed Selenium pipelines for automated data extraction.
Core Skills
LLMs • GenAI • Agentic AI • RAG Systems • LangChain • AI Agents • Document Intelligence • OCR • NLP • BERT • FastAPI • Python • Vector Databases • Semantic Search • Computer Vision
Steps for completing your project
After purchasing the project, send requirements so Shivangi can start the project.
Delivery time starts when Shivangi receives requirements from you.
Shivangi works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Review & Document Analysis
I review the uploaded documents and understand the use case for the RAG chatbot system.
Document Processing & Embeddings
Documents are parsed, chunked, and converted into vector embeddings for semantic search.