Guides

Learn how to use production test data trends in TofuPilot to predict equipment failures and schedule maintenance before downtime occurs.

Design validation testing (DVT) confirms a product meets its design requirements. Learn how to structure DVT in Python and track results with TofuPilot.

Autonomous test closure uses AI to determine when a unit has been tested enough. Learn how it works, where it applies, and what data it needs.

Learn how to replace opinion-based test decisions with data-driven manufacturing quality control using TofuPilot's analytics platform.

Adaptive testing uses real-time data to adjust test sequences, skip redundant checks, and reduce cycle time. Learn how it works and where it applies.

Predictive quality uses production data to catch defects before they happen. Learn how it works, what data it needs, and how test results feed prediction.

An AI-native test station is built around data and inference from the start, not bolted on after. Learn what it means and how it changes manufacturing test.

A continuous test stack connects test development, execution, data collection, and analytics into one integrated workflow. Learn what it includes and how.

Learn how to use TofuPilot's test data to trace hardware failures back to their root cause using measurement trends and run comparisons.

A test copilot is an AI assistant that helps engineers write tests, analyze failures, and optimize limits. Learn what it does and where the technology is.

Generative test design uses AI to create test plans, scripts, and measurement strategies from product specifications. Learn where it works today and where.

Shift-left quality moves defect detection earlier in the manufacturing process. Learn how it reduces cost and how test data enables it.