Enterprises managing tens of millions of rows, with intricate row-level and column-level security, and dozens of datasets spanning multiple business domains, require AI-generated answers that are trustworthy, reproducible, and fast, all while consistently adhering to governance rules. While foundation models (FMs) excel at building systems for smaller datasets where business users can get answers in seconds, Amazon Quick extends this capability to transform large enterprise data into rapid and accurate AI-powered decisions. This post introduces five new capabilities of Amazon Quick designed to accelerate how data professionals deliver trusted AI-powered insights at enterprise scale.
Dataset Q&A: Talk to your data directly
When a VP asks, “How is churn trending for this product?”, obtaining that answer typically involves either locating the correct dashboard (if one exists for that exact slice) or waiting for an analyst to write and validate a query. This gap between posing a question and receiving a trustworthy answer can span hours or days, escalating with organizational complexity, more teams, more datasets, and more unique questions without pre-built views.
Dataset Q&A dramatically reduces this timeframe. Users can attach one or more datasets to a chat agent or a Quick Space with mixed assets, and then ask questions in natural language. The system generates SQL and executes it across the full dataset (millions of rows with no sampling) to return results in seconds.
Generating SQL from a question is the more straightforward aspect. The greater complexity lies in everything that informs *how* that SQL is written. When an analyst provides an answer, the implicit understanding is that they grasp the computations, filters, time horizons, and all other relevant context. The trust in the answer stems from the analyst’s expertise. Similarly, the system resolves ambiguity in the question itself (e.g., "growth" referring to transactions, customers, revenue, or units) by determining the correct fields, aggregations, and filters, and applying business definitions provided by analysts through dataset metadata. The resulting SQL aims to reflect your domain’s actual semantics, not merely a best-guess interpretation of column names.
Amazon Quick consistently applies the row-level and column-level access policies configured for dashboards to AI-generated queries, scoped to your identity. This means your existing security posture is seamlessly applied to conversational answers without additional configuration. Consequently, you can move from question to a verified answer without filing a ticket with business analysts, waiting for dashboard updates, or incurring pre-configuration overhead.
Explanations: Verifying the reasoning
While speed is essential, it is not sufficient. When computational accuracy in an answer is critical (as it typically is in enterprise analytics)…
