You will get a RAG AI assistant trained on your business documents
Rising Talent
Rising Talent
Project details
Most businesses sit on goldmines of knowledge locked inside PDFs, manuals, and docs — but finding answers takes hours of manual searching.
I build RAG AI assistants that turn your documents into an intelligent knowledge engine. Ask a question in plain English, get an instant answer with the exact source cited — no hallucinations, no guessing.
Built with LangChain, LangGraph, FAISS, and OpenAI GPT-4. Production-ready with confidence scoring, analytics, and a clean Streamlit UI.
What you get:
→ AI trained specifically on YOUR documents
→ Every answer includes source page citations
→ Sub-2 second response time
→ Full source code delivered to you
→ Setup guide + walkthrough video included
Ideal for: SaaS companies, law firms, insurance teams, customer support, internal knowledge bases, compliance teams.
I've built this for real — PolicyMind is a live example of exactly what I deliver.
I build RAG AI assistants that turn your documents into an intelligent knowledge engine. Ask a question in plain English, get an instant answer with the exact source cited — no hallucinations, no guessing.
Built with LangChain, LangGraph, FAISS, and OpenAI GPT-4. Production-ready with confidence scoring, analytics, and a clean Streamlit UI.
What you get:
→ AI trained specifically on YOUR documents
→ Every answer includes source page citations
→ Sub-2 second response time
→ Full source code delivered to you
→ Setup guide + walkthrough video included
Ideal for: SaaS companies, law firms, insurance teams, customer support, internal knowledge bases, compliance teams.
I've built this for real — PolicyMind is a live example of exactly what I deliver.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Gradio, Hugging Face, StreamlitAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 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.
Video walkthrough
(+ 1 Day)
+$50
Extra document set
(+ 1 Day)
+$75
AWS Depoyment
(+ 3 Days)
+$100Frequently asked questions
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MH
Mazhar H.
Jul 17, 2026
Fix AI Phone Bot Booking Issue
Saad quickly identified the root cause of my Vapi AI phone bot's date handling issue and fixed it efficiently. He explained everything clearly, communicated well, and treated the project with real professionalism. I'd definitely hire him again.
About Rana Saad
AI Agent Developer | RAG, n8n Automation, Vapi Voice Agents | Python
100%
Job Success
Lahore, Pakistan - 3:13 pm local time
Most AI projects stall at the same point: the demo works, then real documents arrive and the agent starts making things up. No logging, no fallback, nobody knows what broke. I build the part that comes after the demo.
✅ Production AI Agents: LangGraph, LangChain, Python, FastAPI
✅ RAG Systems grounded in YOUR data, with citations, not guesses
✅ Voice Agents: Vapi, Retell (build new ones, fix broken ones)
✅ Automation Workflows: n8n, APIs, PostgreSQL, Redis
➤ 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗜'𝘃𝗲 𝗯𝘂𝗶𝗹𝘁:
🔹 𝗣𝗼𝗹𝗶𝗰𝘆𝗠𝗶𝗻𝗱: RAG documentation assistant (LangGraph + FAISS). Answers come from the client's documents with citations, zero invented facts.
🔹 𝗟𝗲𝗮𝗱𝗣𝗶𝗹𝗼𝘁: Multi-tenant WhatsApp lead qualifier. LangGraph agent, FastAPI backend, PostgreSQL and Redis. Multiple client accounts, one system.
🔹 𝗟𝗟𝗠 𝗡𝗲𝘄𝘀 𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲: Live pipeline from Reuters/Eikon into an LLM judge with structured JSON output, then into Interactive Brokers with confidence-tiered order sizing. Includes offline auto-labeler and review UI, because a trading agent you can't audit is a liability.
🔹 𝗩𝗼𝗶𝗰𝗲 𝗔𝗴𝗲𝗻𝘁 𝗥𝗲𝘀𝗰𝘂𝗲: Client's Vapi phone bot was mishandling booking dates. Found the root cause, fixed it in a day. His words: "explained everything clearly, communicated well."
➤ 𝗘𝘃𝗲𝗿𝘆 𝗯𝘂𝗶𝗹𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀:
✔️ Error handling, retries, and logging, so failures surface instead of going silent
✔️ Human approval steps wherever the agent touches money or client data
✔️ Documentation + walkthrough recording, so your team runs it without me
✔️ Honest scoping first. If automation is the wrong answer, I'll say so before you spend
🛠️ 𝗦𝘁𝗮𝗰𝗸: Python · LangGraph · LangChain · FastAPI · n8n · PostgreSQL · Redis · FAISS · Claude API · OpenAI API · Vapi · Retell · REST APIs · Webhooks
🎓 I've taught Claude Code and AI automation to 100+ people across 11+ countries in live sessions. You get plain English, not jargon.
💬 Send me the process you want automated or the agent that's misbehaving. Within 24 hours I'll tell you how I'd build it, where it could fail, and what it costs, before anything is agreed.
Steps for completing your project
After purchasing the project, send requirements so Rana Saad can start the project.
Delivery time starts when Rana Saad receives requirements from you.
Rana Saad works on your project following the steps below.
Revisions may occur after the delivery date.
Document Processing
ingest and chunk your documents, clean the data, and build the vector database using FAISS.
RAG Pipeline Setup
configure LangChain + LangGraph retrieval chain with OpenAI, add confidence scoring and citations.