Unified LLM Evaluation Pipeline Platform
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Managed evaluation infrastructure that lets AI teams build, run, and monitor large-scale LLM eval suites to catch regressions and measure quality.
Added May 10, 2026
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The Problem
AI engineering teams across companies are independently building evaluation pipelines to measure model quality, catch regressions, and inform iteration decisions. This work is repetitive, infrastructure-heavy, and requires combining automated metrics with human feedback at scale across thousands of real user queries.
Potential Solution
A managed platform that provides the full evaluation stack: pipeline orchestration for running evals at scale, automated regression detection across prompt and model changes, human-in-the-loop feedback collection workflows, and dashboards that track quality metrics over time. Teams plug in their models and datasets instead of building bespoke eval frameworks from scratch.
Why Now?
Nearly every AI-forward company is now hiring engineers specifically to build evaluation pipelines, signaling that eval infrastructure has become a universal need rather than a bespoke concern, and existing tools like Braintrust validate buyer willingness to pay.
Program Manager, ML Data
Build Evals & Metrics: Design and implement ML evaluation frameworks. Identify key data-centric drivers of model performance and create the metrics that track ML quality at the data level.
Senior Machine Learning Engineer
Run Human Evaluations – Build scalable pipelines to collect structured human feedback, benchmark subjective quality, and inform model iterations.
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