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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
7 signals
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.
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.
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.
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.
Run Human Evaluations – Build scalable pipelines to collect structured human feedback, benchmark subjective quality, and inform model iterations.
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