Muzakir U.

AI agents, RAG systems and full-stack SaaS, built to hold up on real users and real data. Most AI features look great in a demo and fall apart in production: messy documents, edge cases, rising token bills. I design for that from the first line, because I came to AI from years of building production web platforms. What I build: ✔ AI Agents & Workflow Automation: LangGraph agents that call your tools and APIs, keep memory, and pause for human approval where it matters. Live example: the onboarding and brand agents behind INFIN8. ✔ RAG Systems & Semantic Search: hybrid retrieval (vector + keyword) with cited answers and evaluation. My open-source PolicyIQ scores Recall@5 of 1.0 and 94% faithfulness on its RAGAS test set. ✔ LLM Integration: OpenAI, Claude and Gemini in real products, with structured outputs, streaming and guardrails. ✔ AI SaaS Platforms: multi-tenant products with auth, roles, billing and dashboards in NestJS, FastAPI, React and Next.js. ✔ Backend Architecture: microservices, CQRS, queues, PostgreSQL, MongoDB and Redis on AWS with Docker and CI/CD. Recent work: Auxee (agentic market-intelligence analyst in Microsoft Teams), INFIN8 (AI creator marketplace), enterprise document intelligence over thousands of files, and an ESG SaaS serving 600+ concurrent users. 100% Job Success on Upwork, including a 15-month full-time engagement with one client. Tell me what you're building, what data it needs, and where you are now. I'll tell you honestly what the right approach is and what the first step should be.