How Data Culture measured its way from zero trust to a shippable AI analytics agent

Data Culture developed an evaluation-driven framework to establish trust in AI analytics agents before stakeholder deployment, responding to widespread concerns about hallucinated or incorrect data. Its approach uses carefully validated “golden questions,” compares query results rather than SQL syntax, and applies a layered comparison system that accommodates harmless differences such as alternate column names, extra context, and rounding. Most evaluations are handled through deterministic checks, while an LLM judges more subjective cases for groundedness, relevance, and completeness. In deployments for e-commerce and healthcare clients, accuracy rose from 0 percent on raw data to more than 90 percent after adding modeled dbt tables, column descriptions, business rules, default filters, verified queries, and named metrics, without changing the underlying model. The company argues that curated data models and semantic context are more durable assets than any particular AI model, and it has open-sourced its evaluation pipeline and related templates.

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