Understudy offers a platform that captures traces from existing LLM production workflows, benchmarks model performance, and automates A/B‑tested model switching to ensure each new deployment outperforms the previous baseline. The solution integrates via a single install into the coding agents teams already use, with optional hosted infrastructure for cloud training or serving, and reports up to 13% higher evaluation scores, 5× lower latency, and dramatically reduced token costs.
Funding
Funding not disclosed
Founders
Product
Problem
Teams that rely on large language model (LLM) agents often face high inference latency, excessive token costs, and unpredictable performance when switching to newer models. These issues limit the scalability and economic viability of production LLM pipelines.
Solution
Understudy provides an end‑to‑end platform that captures execution traces from existing coding agents, establishes performance benchmarks, and automates A/B testing of candidate models. New models are only deployed when they surpass held‑out evaluations, ensuring consistent or improved evaluation scores. The platform can run locally within the user’s existing workflow or optionally use hosted infrastructure for training and serving. By feeding production data back into the training loop, Understudy continuously compounds performance gains, delivering higher scores, lower latency, and reduced token costs.
Target Audience
Primary customers are AI product teams and engineering groups that build and operate LLM‑driven coding agents or other agentic workflows requiring predictable performance and cost efficiency.
Features
- Single‑install trace capture that integrates with coding agents and environments already in use
- Benchmarking framework that defines success criteria and runs automated A/B tests for each model candidate
- Conditional deployment pipeline that promotes a new model only after it beats the held‑out evaluation
- Optional cloud services for large‑scale training or serving while keeping the core workflow on‑premise
- Continuous feedback loop where production data informs subsequent model training for incremental improvements