Faheem K.

I take AI from demo to production: AI agents, RAG systems, voice AI and LLM pipelines built into real software, with evals, guardrails and cost control from day one. ⭐ Top Rated Plus · 100% Job Success. Most AI projects stall after the demo. The prototype impresses in a meeting, then breaks on real users, real data and real costs. Closing that gap is my job. I work directly with your team from scoping to rollout and own the whole build: the AI layer, the backend, the UI and the deployment. 📈 RESULTS FROM RECENT WORK - LLM assessment pipeline (in production): automated grading of spoken answers, dictations and translations against checkable ground truth. Feedback went from ~5 minutes of instructor time per answer to ~5 seconds. Critical facts are enforced in code, so they stay correct whatever the model does. Live regression suite: 22/22 passing, zero regressions. - Real-time voice AI: cut round-trip latency from 9.5s to 2.99s (5.5x) on the same models through streaming and sentence-level pipelining, and tracked cost per session to find where the money actually went. - Agentic RAG eval harness (open-source, in progress): a complete agentic RAG system, benchmarking private self-hosted LLMs against API-based models on accuracy, cost, latency, PII handling and guardrails. Every result is measured and public, so the private-vs-API decision rests on data, not opinion. - Full-stack SaaS: rebuilt a legacy desktop app as an offline-capable web platform (40+ screens) and migrated a WordPress plugin to a scalable SaaS on Azure (50+ screens). ⚙️ WHAT I BUILD - AI agents and multi-step workflows (LangChain, LangGraph, MCP) that work with your tools and data - RAG assistants that answer accurately from your own documents - Voice AI Agents: speech-to-text → LLM → text-to-speech pipelines tuned for low latency - LLM features inside your existing app or SaaS (OpenAI, Claude, Gemini, open-source models) - Workflow automation with n8n, APIs and custom code - Private, self-hosted LLMs when your data can't leave your infrastructure 🧭 HOW I WORK 1. Scope first: we agree on the outcome and how we'll measure it. 2. Build with evals: a test set and regression checks from the start, so quality is measured, not guessed. 3. Ship production-ready: error handling, guardrails, PII handling, cost tracking, and deployment on your cloud or on-prem. 4. Hand over cleanly: documentation and a system your team can run and extend. 🧰 STACK Python, TypeScript/React, C#/.NET, Blazor · OpenAI, Claude, Gemini, Ollama, Hugging Face · LangChain, LangGraph, MCP, n8n · Pinecone, Qdrant, Chroma, FAISS · Azure, Microsoft Foundry, Vertex AI, Docker, SQL 🎓 MS in Computer Science (Data Science & AI), LUMS. Send me a message with what you're building, your current stack and the one outcome you need. I'll reply with a short plan and a quote.