Most voice agents reach production tested the same way: the person who built it calls it a few times, it sounds good, and it ships. Then real customers show up with accents, interruptions, bad connections, and questions nobody scripted, and the distance between “sounded good in a demo” and “works at scale” turns into hallucinated policies, missed disclosures, and calls that quietly fall apart.
We built RubricHQ to close that gap. It is a testing platform for voice AI: simulate thousands of real calls against your agent, score every one on the metrics that matter, optimize your prompts with evidence instead of vibes, and keep watching once you are in production. It is the rigor software teams have had for decades, brought to conversational AI.
What we will write about here
This is where we share what we learn doing that. Practical pieces on simulation and scenario design, evaluation metrics and LLM-as-judge scoring, prompt optimization, and production monitoring. Some hands-on how-to, some opinions about where voice AI testing is heading. All of it from real calls, real failures, and real fixes.
If you are building a voice agent and you want it to hold up when it matters, you are who we are writing for. Welcome.
