Pi Copilot

Product Introduction

1. Pi Copilot is an AI-powered evaluation system designed to automate and optimize the creation of performance metrics for AI applications. It eliminates manual prompt refinement by auto-generating evaluation criteria (evals) based on user feedback, product requirements, and contextual prompts. The system integrates seamlessly with tools like Google Sheets, PromptFoo, and GRPO while offering a free tier of 25 million tokens for scalable testing.
2. The core value of Pi Copilot lies in its ability to deliver highly accurate, consistent, and fast evaluations for AI models and agents. It ensures reliable scoring across custom dimensions, enabling developers to validate performance in real-world scenarios without sacrificing speed or integration flexibility.

Main Features

1. Pi Copilot automatically generates evaluation frameworks (evals) by analyzing user feedback, prompts, and product documentation, reducing the need for manual refinement. This feature supports dynamic adjustments to criteria based on evolving application requirements or new data inputs.
2. The system integrates natively with productivity tools like Google Sheets, PromptFoo, and GRPO, allowing users to deploy scoring models directly into existing workflows. Exported evals can be converted into code for offline evaluation or embedded into live inference pipelines.
3. Pi Scorer, the underlying foundation model, outperforms GPT-4.1 and Deepseek in accuracy while operating at the speed and size of lightweight models like GPT Mini and Gemini Flash. It evaluates 20+ custom metrics in under 100 milliseconds, enabling real-time observability and agent control.

Problems Solved

1. Pi Copilot addresses the inefficiency of manual prompt refinement and inconsistent LLM-as-judge evaluations, which often produce variable or unreliable results. Traditional methods require extensive trial-and-error to align metrics with application goals.
2. The product targets AI developers, product managers, and engineering teams building LLM-powered agents, chatbots, or content-generation tools. It is particularly relevant for applications requiring rigorous performance validation, such as trip planning agents or marketing copy generators.
3. Typical use cases include benchmarking AI-generated blog posts against brand guidelines, comparing product marketing agents for accuracy, and validating agent responses in customer service workflows. It also streamlines compliance checks for enterprise-grade AI deployments.

Unique Advantages

1. Unlike competing tools that rely on uncalibrated LLM judges, Pi Copilot uses purpose-built scoring models trained for precision and consistency. This eliminates output variability common in GPT-4 or Claude-based evaluations.
2. The system uniquely combines automatic eval generation with multi-platform interoperability, allowing a single Pi Scorer to power offline testing, online monitoring, and agent control logic. No retraining or adapter layers are needed for integration.
3. Competitive advantages include sub-100ms latency for complex evaluations, a 25M-token free tier for large-scale testing, and proprietary calibration techniques derived from the founders’ Google Search and GenAI expertise. These ensure enterprise-grade reliability at consumer-friendly scalability.

Frequently Asked Questions (FAQ)

1. How does Pi Copilot integrate with existing tools like Google Sheets? Pi Copilot provides prebuilt connectors for Sheets, PromptFoo, and GRPO, enabling direct import/export of evaluation data. Scores update in real time, and users can trigger evals via API or spreadsheet formulas.
2. What makes Pi Scorer faster than GPT-4 for evaluations? Pi Scorer uses a distilled architecture optimized for scoring tasks, reducing computational overhead while maintaining accuracy. It processes 20+ metrics per API call without sequential model queries.
3. Can Pi Copilot handle custom evaluation criteria? Yes, the system auto-generates criteria from PRDs, user feedback, or chat-based input, then fine-tunes them using proprietary calibration datasets. Users can manually adjust weights or add new dimensions.
4. Is the free tier suitable for enterprise use? The 25M-token free tier supports small to mid-scale testing, but enterprises can upgrade for unlimited tokens, SLA-backed uptime, and dedicated model instances.
5. How does automatic eval generation work? Pi Copilot analyzes prompts, historical user feedback, and success metrics to propose evaluation rubrics. It iteratively refines these based on scoring results and new data inputs.