claude -p as a 24/7 AI ops system: self-healing, local RAG memory, cron automation — $0 extra on your Claude Max subscription

AI/ML Engineer — Decision Ops • LLM Observability • GenAI/RAG Systems

A practical guide to AI engineering — LLMs, RAG, agents, evals, and production ops. Built for engineers who ship AI systems.

Production-grade Agentic RAG system featuring self-correcting LangGraph agents, Milvus vector search, MLflow monitoring, A/B testing, and automated CI/CD quality gates with Ragas.

A FastAPI-based system that indexes microservice logs with FAISS vector search and enables natural-language root cause analysis using LLMs.

Anvil: Ops Knowledge Base RAG System - Full Lifecycle

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CRAG-Ops is a full-stack self-correcting RAG system that answers questions using PDFs or web research. It combines FastAPI, React and LangGraph to retrieve, evaluate, rerank, and ground responses, improving answer quality with corrective retrieval checks and traceable workflows for reliable answers.

This is an automated AI auditor that fetches public financial documents, processes them through a distributed vector pipeline, and uses multiple AI agents to cross-examine the data for anomalies, summarize risks, and generate compliance reports.

RAG-Ops is a production-style Retrieval-Augmented Generation (RAG) system designed with an Ops-first mindset, combining agentic orchestration, vector databases, and LLMs with observability, retries, throttling, and scalable deployment on AWS.

PHI-Safe Clinical Ops Copilot (Agentic RAG System) A production-ready, regulated-domain-safe agentic RAG system that orchestrates multiple specialized agents to provide clinical operations guidance with strict safety, grounding, and auditability requirements.

AI-driven system that converts user requirements into production-ready technical architecture, selecting the right patterns, agents, and tools across RAG, automation, and AI-Ops.

RAG for operational intelligence: Complete vector search + knowledge retrieval pipeline across runbooks/docs/code. Transforms incidents → hypothesis + safe actions, ops requests → API procedures, queries → system explanations. LLM reasoning ready, cloud integration pending.

A production-grade Prompt Engineering Library with A/B tested prompt templates, hallucination tests, adversarial tests, and automated LLM evaluation tools. Built for real-world GenAI applications, RAG systems, and LLM-Ops workflows.

A local‑only AI Ops Dashboard powered by a multi‑agent system, shared vector memory, and a RAG pipeline. Includes five domain agents, a Supervisor agent, automated workflows, and a functional dashboard UI built entirely with free, open‑source tools.

Internal Knowledge Base Research Agent — a multi-agent AI system (Planner → Worker → Evaluator) that answers employee questions by searching internal company documentation. Built with RAG-style retrieval, safety evaluation, and structured reasoning, this agent reduces IT/HR/Ops support workload and provides fast, accurate internal Q&A.

Master the Anthropic API: A developer's guide to Claude AI integration. Covers fundamental API ops, prompt engineering with XML, tool use, RAG systems, and Model Context Protocol (MCP). Learn to architect agent-based workflows, multimodal processing, and UI automation using Computer Use and Claude Code with Python.