Statistical validation, evaluation frameworks, and privacy-first LLM/RAG pipelines — defensible metrics for AI-driven products, from a team that's spent 15+ years doing exactly this kind of rigor for federally funded research.
The predictive modeling and A/B testing rigor behind AI evaluation is the same discipline we've applied to funded field research — the case study below is the closest direct analog; the methods below it (Bayesian estimation, power analysis, RCT-style comparisons) are what a real eval framework is built on.
Built and managed the data infrastructure for a large intervention study, contributing a 7% treatment-effect improvement through predictive modeling and A/B testing.
DASS did a fantastic job. They were patient, clear about the coding, and very responsive to my emails. They took the time to understand the overall goal before proceeding with each step. Highly recommend.
A free 30-minute consult — bring your eval design or results, and we'll tell you where it holds up and where it doesn't.
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