Mostafa M.

I build custom AI backends, RAG systems, and knowledge graph pipelines that help teams turn large, messy datasets into reliable, searchable, and explainable AI products. My work goes beyond simple chatbot wrappers. I focus on building production-ready systems with clean backend architecture, accurate retrieval, strong data pipelines, and practical cost control. I can help you with: Knowledge Graphs & GraphRAG I design and build knowledge graph systems using tools like Neo4j and Cypher. This includes entity extraction, relationship mapping, deduplication, and turning complex documents or domain-specific text into structured, connected data. Custom RAG Pipelines I build retrieval systems using vector databases such as Qdrant and FAISS, combined with metadata filtering, semantic search, reranking, and source-grounded answers. The goal is to make retrieval accurate, explainable, and useful in real business workflows. AI Backend Development I develop clean and scalable backend services using FastAPI, async Python, Docker, and modular architecture. I can integrate multiple LLM providers such as OpenAI, Claude, DeepSeek, or local models, while keeping the system flexible and easy to maintain. Custom Multi-Agent Systems When needed, I build lightweight multi-agent orchestration in plain Python instead of relying heavily on frameworks. This gives better control over logic, prompts, token usage, retries, routing, and cost. AI System Optimization I can review or rebuild existing AI prototypes to reduce hallucinations, improve latency, lower API costs, and make the architecture easier to debug, monitor, and scale. Why work with me? I care about building AI systems that are reliable, maintainable, and practical. That means I focus on: -Clear backend architecture -Accurate retrieval and source citations -Cost-efficient LLM usage -Deterministic and testable pipelines -Clean code that can be extended later -Real production needs, not just demos Typical projects I can help with include: -Building a domain-specific Q&A system over documents -Creating a Knowledge Graph from unstructured text -Improving an existing RAG pipeline -Migrating an AI prototype into a production-ready backend -Designing custom AI workflows for research, legal, technical, educational, or internal business data -Auditing your current AI architecture for accuracy, cost, and scalability