A managed platform for building, running, and monitoring large-scale evaluation pipelines for AI systems across automated metrics and human feedback.
Added May 23, 2026
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Medium opportunity
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Companies deploying LLMs? and ML? models struggle to systematically measure quality, catch regressions, and distinguish models that benchmark well from ones that actually work in production. Teams are repeatedly building bespoke evaluation pipelines in-house, combining automated metrics, human feedback collection, and regression detection across prompt and model changes.
A turnkey evaluation platform that lets AI teams define eval suites, run them at scale against thousands of real user queries, and track quality metrics over time. It bundles automated grading, structured human-feedback collection pipelines, regression alerts on prompt/model changes, and data-centric drill-downs to identify where models fail.
Nearly every AI-shipping company now lists evaluation pipeline construction as a core engineering responsibility, and tooling like Braintrust is gaining traction but the space remains fragmented. As LLM?-powered products move from demo to production, rigorous evals have become the bottleneck for safe iteration.
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Lead continuous evals and quality hill climbing by overseeing state-of-the-art evaluation pipelines—combining Large Langauage Model (LLM) autoraters, human-in-the-loop reviews, and inline citations—to ensure robust factuality, grounding, tone, and low latency.
Designed evaluations for LLM or agent systems - eval sets, quality metrics, human-expert and/or LLM-judge pipelines. You have instrumented, monitored, and debugged live AI services (e.g., OpenTelemetry, Arize Phoenix, Langfuse, Datadog, or similar).
Lead continuous evaluations and quality hill climbing by overssing state-of-the-art evaluation pipelines—combining Large Language Model (LLM) autoraters, human-in-the-loop reviews, and inline citations—to ensure robust factuality, grounding, tone, and low latency.
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