You will get AI-Powered SaaS MVP Architect | RAG · LLM Agents · FastAPI · Next.js
Project details
I build production-ready AI SaaS backends — not demos, not wrappers. Every system I ship is architected from day one for real users, real data, and real scale.
My core focus is RAG pipelines, LLM agent systems, and multi-tenant SaaS backends. That means vector search with hybrid retrieval, FastAPI backends with JWT auth and Supabase RLS, and clean Next.js frontends — all deployed, documented, and handed over with architecture diagrams and setup files.
What separates my work: I follow a phase-disciplined build process — scope, architecture, build, deploy — with explicit sign-off between phases. No surprise pivots, no scope creep. You know exactly what gets built before I write a single line of production code.
Proof: I built a multimodal interview intelligence platform (facial, speech, and answer scoring in one pipeline) and a face classification system hitting 99.22% accuracy with Grad-CAM explainability. Same engineering standard applied to every client project.
Stack: Python · FastAPI · Next.js 14 · Supabase · Pinecone · Qdrant · pgvector · LangChain · LangGraph · OpenAI · Claude · Docker · Vercel
If you're building a serious AI product and need it done right — let's talk.
My core focus is RAG pipelines, LLM agent systems, and multi-tenant SaaS backends. That means vector search with hybrid retrieval, FastAPI backends with JWT auth and Supabase RLS, and clean Next.js frontends — all deployed, documented, and handed over with architecture diagrams and setup files.
What separates my work: I follow a phase-disciplined build process — scope, architecture, build, deploy — with explicit sign-off between phases. No surprise pivots, no scope creep. You know exactly what gets built before I write a single line of production code.
Proof: I built a multimodal interview intelligence platform (facial, speech, and answer scoring in one pipeline) and a face classification system hitting 99.22% accuracy with Grad-CAM explainability. Same engineering standard applied to every client project.
Stack: Python · FastAPI · Next.js 14 · Supabase · Pinecone · Qdrant · pgvector · LangChain · LangGraph · OpenAI · Claude · Docker · Vercel
If you're building a serious AI product and need it done right — let's talk.
AI Algorithms
Convolutional Neural Network, Feedforward Neural Network, Large Language Model, Multilayer Perceptron, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Natural Language Understanding, Sentiment Analysis, Sequence Modeling, Synthetic Data GenerationAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$300
|
Standard
$500
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 10 days | 14 days | 24 days |
Number of Revisions | 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
+$30Frequently asked questions
About Karim
AI/ML Engineer
Cairo, Egypt - 5:05 am local time
I am a Machine Learning Engineer specializing in building AI-powered SaaS products from architecture to deployment. I design production-ready systems that are secure, scalable, and maintainable, helping founders and teams turn AI concepts into real applications.
I build:
• Retrieval-Augmented Generation (RAG) systems connected to proprietary data
• AI assistants with memory, tool-calling, and structured workflows
• Document Q&A and knowledge base platforms
• Resume analysis and semantic matching systems
• Custom LLM pipelines and AI automation backends
Tech stack includes FastAPI, Next.js, Supabase, PostgreSQL, vector databases (pgvector, Pinecone, Qdrant), OpenAI, and Claude APIs.
Every project includes clean architecture, secure authentication, API documentation, and deployment setup. My focus is building reliable backend systems that power real AI products — not experimental prototypes.
If you're building a serious AI product or SaaS MVP, I can help you architect and deliver it properly.
Steps for completing your project
After purchasing the project, send requirements so Karim can start the project.
Delivery time starts when Karim receives requirements from you.
Karim works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements review & scope confirmation
Client submits requirements, I review and confirm scope, timeline, and deliverables.
Architecture design & tech stack sign-off
I design the system architecture and get client approval before any code is written.