Pi Labs provides a tunable scoring system powered by the Pi Scorer model that evaluates LLM outputs on both natural‑language quality and code correctness. Users can define custom dimensions, calibrate scores with their own data, and integrate the fast, sub‑100 ms API into any workflow—from spreadsheets to AI agent pipelines—while keeping inference costs low.
Funding
Funding not disclosed
Founders
Product
Problem
Developers and AI teams lack a flexible, fast, and cost-effective way to evaluate large language model outputs across both natural language quality and code correctness, often relying on expensive, generic LLM judges that do not align with specific product goals or user preferences.
Solution
Pi Labs offers a tunable scoring system built around the Pi Scorer model, which combines natural‑language and code‑based criteria to generate deterministic, sub‑100 ms scores on dozens of custom dimensions. Users can calibrate the metrics with their own labeled data, preferences, and user feedback, creating evaluations that closely match human judgment and specific application requirements. The platform is framework‑agnostic, allowing seamless integration into existing workflows such as spreadsheets, Promptfoo, CrewAI, or any custom tool via a simple API. By running at the speed and cost of a compact model while delivering accuracy comparable to larger LLM judges, Pi enables continuous, granular assessment for model optimization, reward modeling, and agent control without incurring high inference expenses.
Target Audience
Primary customers are AI product teams, ML engineers, and developers who need precise, customizable evaluation metrics for LLM outputs, including those building agents, reward models, or automated content generation pipelines.
Features
- Customizable scoring specifications that blend soft (natural language) and hard (code correctness) measures
- Deterministic scoring across 20+ dimensions in under 100 ms per evaluation
- Calibration loop that incorporates user‑provided labels and preferences to align scores with human judgment
- Framework‑agnostic API and ready‑to‑use integrations for Google Sheets, Promptfoo, CrewAI, and other tools
- Cost‑effective inference, delivering performance comparable to larger models at a fraction of the price
- Continuous model monitoring and updates to maintain scoring quality over time