What they built

ForgeOps — An agentic AI orchestrator for real-time manufacturing root-cause analysis and simulation

ForgeOps is an explainable, multi-agent manufacturing decision-intelligence platform that turns fragmented factory data into evidence-backed corrective actions. It addresses a major industrial challenge: traditional monitoring systems can detect faults but often cannot explain their causes, compare possible interventions, or justify recommendations. A Python orchestrator coordinates four specialized AI agents. The Planner defines the investigation, Research retrieves operational evidence, Analysis identifies causal relationships and evaluates scenarios, and Execution generates the final recommendation and business impact. The agents use NitroCloud’s deployed NitroChat inference layer with a configured Gemini model. They interact with 16 MCP operations—11 direct tools and five task workflows—to access MES events, sensor telemetry, maintenance history, quality inspections, supplier constraints, production timelines, causal graphs, simulations, and business-impact calculations. ForgeOps reconstructs incidents, connects anomalies across production stages, separates symptoms from root causes, and compares actions using predicted yield, cost, effort, downtime, and confidence. Engineers can investigate incidents through natural-language questions and what-if simulations. The React frontend communicates with the Python orchestration backend through HTTP APIs. The backend combines LLM reasoning with controlled MCP data retrieval and returns structured results containing evidence references, assumptions, confidence scores, agent status, and a live tool trace. Human approval remains the final gate before action. ForgeOps is not simply an LLM dashboard; it is an auditable agentic decision system that helps manufacturers reduce downtime, prevent costly misdiagnosis, improve production yield, and make faster, explainable operational decisions.