You will get a legal contract analyzer that extracts key clauses using AI
Project details
I'll build an AI-powered legal contract analyzer that
reads your PDF contracts and instantly answers your
questions in plain English — no legal expertise needed.
What you get:
✅ Upload any PDF — NDA, service agreements, contracts
✅ Ask questions in plain English, get accurate answers
✅ FAISS vector search for precise clause retrieval
✅ Source citations — know exactly where the answer came from
✅ Hallucination prevention — no made-up information
✅ Clean Streamlit UI — easy to use, no coding needed
✅ Full source code with detailed setup instructions
✅ README documentation included
How it works:
1. You upload your contract PDF
2. The AI indexes and understands the document
3. You ask questions like "What is the termination clause?"
4. Bot returns precise answers with source references
Perfect for:
→ Law firms handling multiple contracts
→ Startups reviewing vendor agreements
→ Freelancers checking client contracts
→ HR teams managing employment agreements
→ Anyone who needs fast contract review
Tech Stack:
Python · LangChain · FAISS · Groq Llama 3.3 70B · Streamlit
Delivery: Clean source code + README + setup instructions
reads your PDF contracts and instantly answers your
questions in plain English — no legal expertise needed.
What you get:
✅ Upload any PDF — NDA, service agreements, contracts
✅ Ask questions in plain English, get accurate answers
✅ FAISS vector search for precise clause retrieval
✅ Source citations — know exactly where the answer came from
✅ Hallucination prevention — no made-up information
✅ Clean Streamlit UI — easy to use, no coding needed
✅ Full source code with detailed setup instructions
✅ README documentation included
How it works:
1. You upload your contract PDF
2. The AI indexes and understands the document
3. You ask questions like "What is the termination clause?"
4. Bot returns precise answers with source references
Perfect for:
→ Law firms handling multiple contracts
→ Startups reviewing vendor agreements
→ Freelancers checking client contracts
→ HR teams managing employment agreements
→ Anyone who needs fast contract review
Tech Stack:
Python · LangChain · FAISS · Groq Llama 3.3 70B · Streamlit
Delivery: Clean source code + README + setup instructions
AI Algorithms
AdaBoost, Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, YOLOAI Applications
AI Chatbot, AI Text-to-Speech, Automatic Speech Recognition, Conversational AI, Image Processing, Machine Translation, Natural Language Understanding, Object Localization, Speech Synthesis, Synthetic Data Generation, Text Recognition, Time Series AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, Replit, Streamlit, TensorFlow, Word2vecAI Models
BERT, ChatGPT, GPT-3, LLaMA, Naive Bayes Classifier, OpenAI Codex, WhisperWhat's included $150
These options are included with the project scope.
$150
- Delivery Time 4 days
- Number of Revisions 1
- AI Model Integration
- Detailed Code Comments
- Natural Language Processing
- Prompt Engineering
- Setup File
- Source Code
Optional add-ons
You can add these on the next page.
Additional Revision
+$20
1 review
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
AS
Asif S.
Jun 23, 2026
Upwork Talent Accelerator: AI Chatbot Developer
Really impressed with the final results and the attention to detail throughout the project.
About Dhruv
AI Engineer | LangChain , LangGraph ,RAG , LLM Apps | Python & FastAPI
Delhi, India - 2:22 pm local time
LangGraph agents, and LLM-powered backends that actually
work in deployment, not just notebooks.
🔧 What I build:
- Agentic AI systems using LangGraph StateGraph
- RAG chatbots with FAISS + hybrid search (BM25 + semantic)
- Multi-agent pipelines with tool use & memory
- FastAPI backends with Groq / OpenAI integration
🚀 Recent work:
- Autonomous Data Scientist Agent — full ML pipeline
(EDA → model selection → SHAP explanations) using
LangGraph + Groq Llama 3.3 70B
- Clinical Discharge Agent — 12-node LangGraph pipeline
with OCR fallback, drug interaction checker & audit trace
- EduRAG — hybrid FAISS+BM25 educational chatbot for
NCERT Class 1–12, deployed on Vercel + Railway
🛠️ Stack:
Python • LangChain • LangGraph • FastAPI • FAISS •
HuggingFace • Groq • React 18 • Next.js 14
📄 Published researcher (2 papers, IF 5.6 & 8.1)
B.Tech AI & Data Science — GGSIPU Delhi (CGPA 9.1)
If you need an AI system built fast and deployed clean
— let's talk.
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.
Requirements & Document Collection
You share your PDFs and use case details
Conversation Flow Design
I design the RAG pipeline and chat flow