You will get a RAG chatbot that answers with citations and extracts data from your files
Project details
I build a RAG (Retrieval-Augmented Generation) system that answers questions from your documents with citations, and extracts the data you need into a clean, usable format.
Your knowledge is likely spread across PDFs, wikis, and files a normal chatbot can't access or gets wrong, or your team spends hours manually typing details into other systems. I use hybrid retrieval (keyword plus semantic search) with reranking for accurate, cited answers, and can add structured extraction of key fields (invoices, contracts, forms) into JSON, a spreadsheet, or your database.
You'll get a working system connected to your documents, a clean API or chat interface, and I handle ingestion, embeddings, the vector database, and answer generation or extraction end to end.
I work with Python, LangChain, OpenAI or Claude, and vector databases like Milvus, Qdrant, or pgvector, on a Django or FastAPI backend, with security in mind throughout including safe API key handling and access control.
I share progress regularly and check in before major steps. Tell me whether you need Q&A, extraction, or both, and I'll recommend the right approach.
Your knowledge is likely spread across PDFs, wikis, and files a normal chatbot can't access or gets wrong, or your team spends hours manually typing details into other systems. I use hybrid retrieval (keyword plus semantic search) with reranking for accurate, cited answers, and can add structured extraction of key fields (invoices, contracts, forms) into JSON, a spreadsheet, or your database.
You'll get a working system connected to your documents, a clean API or chat interface, and I handle ingestion, embeddings, the vector database, and answer generation or extraction end to end.
I work with Python, LangChain, OpenAI or Claude, and vector databases like Milvus, Qdrant, or pgvector, on a Django or FastAPI backend, with security in mind throughout including safe API key handling and access control.
I share progress regularly and check in before major steps. Tell me whether you need Q&A, extraction, or both, and I'll recommend the right approach.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Image Analysis, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Models
ChatGPTWhat's included
| Service Tiers |
Starter
$800
|
Standard
$2,200
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 7 days | 16 days | 25 days |
Number of Revisions | 2 | 3 | 2 |
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.
Fast Delivery
+$150 - $600
Additional Revision
+$100
Custom chat UI
(+ 3 Days)
+$400
Cloud deployment and monitoring
(+ 3 Days)
+$350
Evaluation harness and report
(+ 5 Days)
+$500About Dhruv
RAG, Agentic AI & Multi-Agent Developer | LangChain, LangGraph, LLMs
Ahmedabad, India - 3:05 pm local time
I'm a Python and AI backend developer who works on real, live LLM systems, not just demos. On a production RAG SaaS product, I built the document ingestion pipeline, set up embeddings, and implemented real-time question answering with source citations so users could verify every response. I also added multi-provider LLM support, allowing conversations to continue even if one provider becomes unavailable or costs increase. My focus is on building AI systems that are accurate, reliable, and ready for production.
𝐖𝐇𝐀𝐓 𝐈 𝐂𝐀𝐍 𝐇𝐄𝐋𝐏 𝐘𝐎𝐔 𝐖𝐈𝐓𝐇
1. RAG Chatbots & Knowledge Assistants
Answer from your own documents with citations using hybrid retrieval (BM25 + dense embeddings) and reranking. I also build structured data extraction pipelines for invoices, contracts, forms, and other business documents.
2. Agentic AI & Multi-Agent Systems
Build LangGraph-based agents with tool calling, human approval workflows, and multi-step task automation for complex business processes.
3. LLM Integration for Your App or Product
Integrate OpenAI, Claude, and Gemini with streaming responses, multi-provider fallback, and production-ready backend architecture.
𝐓𝐎𝐎𝐋𝐒 𝐀𝐍𝐃 𝐒𝐓𝐀𝐂𝐊 𝐈 𝐔𝐒𝐄
• Backend: Python, Django, Django REST Framework, FastAPI, Node.js
• Frontend: React.js
• AI / Agent Frameworks: LangChain, LangGraph, n8n
• Async & Data: Celery, Redis, PostgreSQL
• Vector Databases: Milvus, Qdrant, pgvector
𝐇𝐎𝐖 𝐈 𝐖𝐎𝐑𝐊
Every successful AI project starts with understanding the business problem, not choosing the latest framework or model.
I first understand your data, users, and business goals before deciding on the right retrieval or agent architecture. Then I build the solution step by step, validate it with real-world scenarios, and prepare it for production with proper logging, monitoring, and error handling.
Security is part of the implementation from day one, including API key management, document access control, and protection against prompt injection. Throughout the project, I communicate clearly, share regular progress updates, and raise potential risks early so there are no surprises later.
You get clean, documented code, an architecture that's easy to maintain, and AI systems that behave predictably when real users arrive.
If you're building a RAG assistant, an agentic AI agent, or a multi-agent system. Send me a short note about what you're building and the outcome you want. I'll tell you how I'd approach it, what challenges I see, and what's realistically achievable.
Steps for completing your project
After purchasing the project, send requirements so Dhruv can start the project.
Delivery time starts when Dhruv receives requirements from you.
Dhruv works on your project following the steps below.
Revisions may occur after the delivery date.
Review documents and goals
I review your documents, business goal, and use case to understand what the chatbot needs to know and who it's for, so the approach fits your actual needs from the start.
Set up ingestion and vector database
I build the pipeline that processes your documents, generates embeddings, and stores them in a vector database, so your content is ready for accurate retrieval.