MyInfo Copilot brings trusted Teradyne expertise directly into the engineering workflow and lays the groundwork for agent-enabled test development.
Semiconductor test engineering has never been more demanding. As devices grow more complex, leveraging technologies like heterogeneous integration, advanced packaging, and AI-integrated architectures, test engineers are navigating an increasingly intricate landscape. But complexity isn’t the only challenge. The information test engineers need to do their jobs well is often fragmented across documentation systems, shared drives, legacy repositories, and the institutional knowledge of a small number of senior engineers.
The Challenge: Fast Timelines, Scattered Knowledge
Test engineers are expected to move fast. Bring-up windows are shortening, characterization cycles are compressed, and production ramps leave little room for debugging. Yet the practical reality for many engineers is that finding the right answer for a given Teradyne platform can require extensive search across disparate systems. When documentation is unclear or scattered, engineers may resort to suboptimal approaches to find the expertise they need. In high-stakes environments, these inefficiencies carry real consequences.
The problem isn’t that the knowledge doesn’t exist. The challenge is getting the right knowledge to the right engineer at the right moment in the workflow. These are sophisticated, capability-rich systems, and using them effectively requires access to deep, platform-specific knowledge that general-purpose AI tools are simply not equipped to provide.
Closing the Gap: Introducing MyInfo Copilot
MyInfo Copilot is a Teradyne-specific AI knowledge assistant built for test engineers. Rather than surfacing generic answers, it searches approved Teradyne knowledge sources, retrieves relevant content, and generates concise, source-cited responses. Engineers working on test program development, platform bring-up, characterization, or production debug can ask natural-language questions and receive answers grounded in actual Teradyne documentation, with citations back to the source material, so they can verify recommendations and maintain the traceability that production environments demand.
For teams working across IG-XL and EV-MST, MyInfo Copilot functions as a trusted, always-available technical reference that understands Teradyne platforms, not just general programming patterns.
Source-Cited Answers Engineers Can Actually Trust
In test engineering workflows, engineers need to know where the answer came from. When writing test code that will run on production lines, the ability to trace a recommendation back to its source to verify it against official documentation is essential.
MyInfo Copilot surfaces citations alongside answers, giving engineers the ability to validate quickly and move forward with confidence, rather than second-guessing AI-generated output or spending additional time independently verifying claims against scattered sources.
Scaling Expertise Across Teams
One of the less-discussed costs of test complexity is the concentration of knowledge in a small number of experienced engineers. As platforms evolve and teams grow, the gap between what senior engineers know and what others can readily access becomes a meaningful barrier. New engineers spend longer reaching productive output. Experienced engineers field more interruptions. Documentation search eats into development time at every level.
MyInfo Copilot provides a way to scale embedded expertise across the organization. By making Teradyne-specific knowledge accessible in everyday engineering workflows, it allows engineers at every experience level to access platform-accurate information without waiting for expert availability or independently navigating complex documentation hierarchies. For technical leads responsible for accelerating test development, preserving institutional knowledge, and reducing reliance on tribal expertise, this capability has immediate practical value.
From Answer Retrieval to Agent-Enabled Engineering
AI’s role in software development is evolving rapidly. Engineers increasingly work alongside AI coding agents, like GitHub Copilot, that can generate code, suggest implementations, and assist in debugging complex workflows. But for these agents to be genuinely useful in Teradyne test environments, they need to understand Teradyne platforms.
Currently in beta, Enterprise MCP (Model Context Protocol) extends MyInfo Copilot from a knowledge retrieval tool into a structured interface for AI coding agents. It provides a standardized, governed pathway for AI agents to access proprietary Teradyne data, with all the control that enterprise environments require for data security. As engineering teams integrate AI coding agents into their development workflows, those agents can operate with the same Teradyne-specific context that MyInfo Copilot already delivers. For teams focused on building AI-assisted workflows that produce trustworthy and repeatable output, Enterprise MCP provides the architectural foundation without any data leaving the customer environment.
The Road Ahead
The semiconductor industry’s pace of change shows no signs of slowing. As device complexity increases and test requirements grow more demanding across every Teradyne platform, the ability to quickly access trusted, accurate, platform-specific knowledge becomes a competitive advantage in itself. MyInfo Copilot and Enterprise MCP reflect Teradyne’s commitment not just to delivering capable test platforms, but to ensuring the engineers who work with those platforms have the tools and knowledge infrastructure they need to use them effectively. In an industry where test engineering speed and accuracy directly affect time-to-market and yield, that foundation matters more than ever.
Watch this video to learn more about MyInfo Copilot.
Log in to eKnowledge to explore MyInfo Copilot today.
Reed Axman is a senior product manager at Teradyne responsible for the Gen AI software product strategy and roadmap. Prior to Teradyne, Reed was a senior partner business manager at MathWorks. He holds a Bachelor of Science degree in biomedical engineering from Union College and a Master of Science degree in Robotics and AI from Arizona State University.