Prasanna

Reliability engineers must turn sparse failure data into decisions that survive a design review: how long a product will last, whether a warranty is safe, and how to prove reliability before shipping. Today that means slow spreadsheet work across scattered documents — and a generic chatbot is worse, because it will fluently invent the very statistics the decision depends on. Reliability Copilot solves this with a tool-grounded agent: failure statistics are too important to be generated token by token, so the model never calculates alone. Deterministic Python tools, called through MCP, supply every engineering number: Weibull probability paper from failure and suspension data, distribution ranking against Kaplan–Meier, reliability growth, warranty-failure forecasts, and zero-failure demonstration-test plans and more, while the model, running on Meta Muse Spark and Muse Wonder, plans, retrieves citable evidence from engineering handbooks and local documents, and explains results whose numbers trace back to tool output. Every number traced, every claim cited.