You will get a production RAG chatbot with OpenAI, FastAPI, and vector search
Project details
You'll get a production-ready RAG chatbot that answers questions from your documents with grounded, cited responses no hallucinations. Built on the same stack as TheGoodScholar (thegoodscholar.com), a live multi-agent RAG system with real users.
I've built RAG systems at HyperionLabs (250k+ transactions, 95% time savings) and Goldman Sachs (document automation, 80% reduction in manual entry).
This isn't a demo it's production-grade code you can deploy and rely on.
You'll receive clean, documented, Docker-ready code with full deployment instructions.
I've built RAG systems at HyperionLabs (250k+ transactions, 95% time savings) and Goldman Sachs (document automation, 80% reduction in manual entry).
This isn't a demo it's production-grade code you can deploy and rely on.
You'll receive clean, documented, Docker-ready code with full deployment instructions.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Mobile App Development, AI-Generated Code, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, PyTorchAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$300
|
Standard
$500
|
Advanced
$900
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 0 |
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 |
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FN
Faisal N.
Jun 12, 2026
AI-Assisted Editorial Review Tool for Word Documents
Its a pleasure to working with Muhammad. They built our Python-based document processing and AI annotation tool exactly as requested, with strong attention to detail and high-quality code. Communication was professional throughout the project, deadlines were met, and they clearly knew what they were doing. I would highly recommend them and would happily work with them again.
MA
Muhammad A.
May 28, 2026
AI Chatbot Developer — RAG System with OpenAI & FastAPI
Shoaib built a RAG-based chatbot for our platform using FastAPI and OpenAI. He understood the requirements quickly, asked the right clarifying questions upfront, and delivered clean, well-documented code. Communication was clear throughout and he flagged a potential issue with our document chunking strategy before it became a problem. Would hire again for AI engineering work!
TY
Tsviatko Y.
Apr 5, 2022
React/React Native developer with strong node.js experience
TY
Tsviatko Y.
Nov 29, 2021
React/React Native developer with strong node.js experience
About Muhammad Shoaib
AI Engineer | RAG & LangGraph Expert | OpenAI | FastAPI | Python
100%
Job Success
Birmingham, United Kingdom - 6:48 pm local time
RAG pipelines, LangGraph agents, LLM integrations, and full-stack backends. Live examples:
→ TheGoodScholar: multi-agent RAG system, LangGraph + Pinecone + GPT-4, deployed on AWS.
Grounds every response in 28k+ authenticated Islamic texts with citations. Real users, real queries.
→ HyperionLabs: technical lead on AI financial platform. RAG agent processing 250k+ crypto transactions, confidence scoring, human escalation paths, audit trails. 95% time savings, 90%+ accuracy in production.
→ Goldman Sachs: document automation pipeline using AWS Textract + GPT-3.5. 80% reduction in manual data entry, 95%+ accuracy on financial documents.
→ BBC Bitesize: React components and Node.js services for one of the UK's largest digital platforms.
What I specialise in:
AI & LLM Engineering:
OpenAI, Anthropic Claude, LangChain, LangGraph, RAG systems, vector databases (Pinecone, pgvector, Chroma), prompt engineering, multi-agent orchestration, confidence scoring, human-in-the-loop workflows.
Backend Development:
Python (FastAPI, Django REST), Node.js (NestJS, Express), PostgreSQL, Redis, WebSockets, microservices, REST APIs.
Cloud & DevOps:
AWS (ECS/Fargate, Lambda, S3, Textract, RDS), GCP (Cloud Run, Vertex AI), Docker,
Kubernetes, GitHub Actions, LangSmith, Datadog.
Frontend:
React, Next.js, TypeScript, Tailwind CSS, Redux, responsive design.
I work best with clients who have a real problem to solve and want someone who takes ownership from architecture decisions through to deployment and monitoring. I'll push back on bad approaches early rather than build something that doesn't hold up.
9 years production experience. Goldman Sachs, BBC, funded startups. Clean code. Clear communication. No hand-holding needed.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Shoaib can start the project.
Delivery time starts when Muhammad Shoaib receives requirements from you.
Muhammad Shoaib works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery
Share your documents and answer 4 quick questions about your requirements, platform, and API keys
Development
I build the RAG pipeline, vector store, FastAPI endpoints, and OpenAI integration