- What exactly do you do?
- I build and run production AI systems: LangGraph agents, RAG and GraphRAG pipelines, MCP infrastructure, and the evaluation and cost layer around them. I work as an independent contract engineer with founders, CTOs and AI product teams.
- Our agent works in the demo but fails with real users. Can you make it production-ready?
- Yes. That is the AI Agent Engineering Sprint: a fixed scope of 7–10 working days, starting at $1,200, covering LangGraph orchestration, state and retry handling, tool-calling hardening, MCP and RAG integration, evaluation tests, Docker deployment and documentation.
- Our RAG answers are wrong or hallucinated. Where do we start?
- Start with the AI / LLM Technical Audit: five working days, starting at $400. You get an architecture and retrieval review, a tool-calling and prompt review, hallucination, latency and token-cost analysis, an evaluation strategy and a prioritized implementation roadmap.
- How do you keep LLM costs predictable?
- With per-token cost accounting, semantic caching, complexity-based model routing and per-user quotas. I built and operate exactly this stack in OmidGPT, a multi-provider agentic AI platform.
- How do you measure whether an AI system is actually good?
- With evaluation as code: RAGAS, a claim-level LLM judge, post-generation grounding filters and controlled model comparisons, wired into tests that serve as acceptance criteria. Jozveh-AI, an eight-agent GraphRAG pipeline, runs 673 tests across 77 files.
- Do you work with companies outside Iran?
- Yes, remotely. I am based in Iran and work with clients whose compliance and banking arrangements permit contracting an Iran-resident engineer. I raise this on the first call so both sides can confirm feasibility before starting.
- What happens after the 20-minute call?
- You get a written diagnosis, then a fixed-scope proposal with scope, timeline and price. Implementation ends with a handover: code, documentation and a runbook your team can operate without me.
