Real-Time Fraud Detection Backend: FastAPI, Kafka & PostgreSQL

Real-Time Fraud Detection Backend: FastAPI, Kafka & PostgreSQLReal-Time Fraud Detection Backend: FastAPI, Kafka & PostgreSQL

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PRANAV K P

• Mar 30

Built a real-time fraud detection backend system using FastAPI, Kafka, and PostgreSQL.

The system processes transactions in real-time, detects anomalies, and ensures high performance under load. I optimized database queries to improve response time by 60% and designed a scalable architecture for handling continuous data streams.

Key features: • High-performance REST APIs using FastAPI • Real-time processing with Kafka • Optimized PostgreSQL queries • 85%+ test coverage for reliability • Dockerized deployment with CI/CD pipelines

This project demonstrates my ability to build scalable, production-ready backend systems.

Completed work

Backend Development Kafka Database Engineering AI Development Docker Python

Ste D'Elboux

• May 25

What if CapCut had launched during the Y2K internet era? 💿✨

I reimagined CapCut as CapCut.exe, an alternate digital universe inspired by chrome aesthetics, retro interfaces, CDs and early internet nostalgia.

Built using CapCut Design Studio... from idea generation and visual exploration to refining the final creative world.

🔗 https://www.capcut.com/ai-design?shareId=7643629371705720839

capcutdesignstudio AI VIsual Designer AI Art AI Development CapCut Google Gemini

Dylan Siegel

• May 26

This is so cool!

A Intan

• May 26

Introducing CodeShip — reimagining AI development. Built with full-codebase intelligence, automated execution, and a unified approach to code review, planning, and shipping. Check out the details here : https://dribbble.com/shots/27403069-CodeShip-Landing-Page Have an idea or a project in mind? Let’s build something meaningful together.

Figma Web Design Framer

Hardik Paghdar

pro

• May 25

AI demos are easy.

Production AI is where engineering begins.

Anyone can connect an LLM API and make something that looks impressive in a demo.

But real businesses need systems that survive messy users, bad inputs, edge cases, and scale.

Production AI means:

✅ Grounded retrieval (RAG) ✅ Reliable tool/API execution ✅ Structured outputs ✅ Agent workflows with business logic ✅ Monitoring, evals, and feedback loops ✅ Infrastructure that scales in production

The difference between an AI demo and an AI product?

Architecture.

That’s where most “AI startups” break.

I build AI systems that go beyond prompt wrappers — production-ready LLM agents, RAG pipelines, and full-stack AI products.

If you're building something real, build it like software.

#AIEngineering #LLMAgents #RAG #GenerativeAI #LangChain #LangGraph #OpenAI #ClaudeAI #Python #FastAPI #AWS #VectorDatabase #AIProduct #SaaS #ArtificialIntelligence #Contra #FreelanceDeveloper #AIConsultant