Mo A.

You know "AI consultants" leave you with $15,000 monthly token bills and 8-second response times. I build lean, high-throughput LangGraph architectures that cut API latency by 40% and double inference speeds so your AI scales without burning your run-rate. Bridging the gap between brittle LLM prototypes and deterministic, enterprise-grade AI. I architect stateful multi-agent workflows, low-latency RAG pipelines, and scalable FastAPI backends that integrate directly into your databases, CRMs, and internal systems. CORE AI CAPABILITIES • Agentic Workflows & Multi-Agent Orchestration: Designing stateful, fault-tolerant agent architectures using LangGraph, CrewAI, and the Model Context Protocol (MCP) for complex decision-making. • Enterprise RAG & Advanced Retrieval: Implementing hybrid search, GraphRAG, dynamic re-ranking, and context compression to minimize hallucinations and cut token costs. • Production AI Backends & Microservices: Architecting high-throughput FastAPI systems with async execution, streaming responses, structured JSON output, and failover routing. • LLM Observability & Evaluation: Setting up automated evaluation pipelines, prompt optimization, telemetry, and monitoring using LangSmith to ensure production reliability. TECH STACK I WORK WITH AND HANDS ON • Agent Frameworks: LangGraph, CrewAI, Model Context Protocol (MCP), AutoGen • LLMs & RAG Engine: OpenAI API, Anthropic Claude, Gemini, LlamaIndex, LangChain • Vector Stores & Databases: Pinecone, Qdrant, Weaviate, PGVector, Redis • Backend & Cloud: Python (Async), FastAPI, PostgreSQL, Docker, AWS, GCP, CI/CD • Observability & Eval: LangSmith, DeepEval, Guardrails AI ⚠️ THE 3 PRODUCTION FLAWS I CONSTANTLY FIX IN EXISTING REPOS If your AI system worked in local dev but broke in production, you likely hit one of these: 1. "Naive RAG Syndrome": Dumping raw chunks into vector databases without dynamic re-ranking or metadata filtering, resulting in high latency and low accuracy. 2. Unhandled State Collapses: Single-prompt agents getting stuck in infinite loops without state machine controls or deterministic fallbacks. 3. Token Bloat: Over-stuffing system prompts with uncompressed context, driving up monthly API bills by 300%. I restructure these brittle setups into deterministic, production-ready microservices built for heavy load. 💡 GOT A SLOW OR EXPENSIVE AI PIPELINE? GET A 5-MINUTE AUDIT Already have an AI agent or RAG system in staging/production, but it's slow, expensive, or hallucinating? Instead of scheduling a long discovery call, send me a quick message with a brief overview of your tech stack or prompt/RAG architecture. I will send back a free, personalized 5-minute video breakdown showing: 1. Where your pipeline is leaking token costs. 2. How to shave 30%+ off your API latency. 3. The exact architectural tweak needed for deterministic outputs. Click "Invite to Job" or send a message to request your audit or discuss a new project setup.