Qquest

Qquest is an AI data analyst that lets business users query warehouses, databases, ad platforms, analytics tools and CRMs in plain text, with a customizable agent and a Chrome extension.

Qquest is an AI assistant built for asking questions of business data in natural language. The product positions itself as a set of virtual data analysts: instead of filing a ticket with the analytics team and waiting, a user types a question and Qquest returns the insight. The homepage frames this around speed, promising quick answers rather than dashboards built in advance. The tool is aimed at three distinct audiences, and the site is organized around them. For business stakeholders such as project managers and non-technical staff, Qquest offers code-free querying, so a recurring business review or a backfill target list does not require SQL. For data leaders, it works as a customizable AI agent that can be tuned to the specifics of a company's customers, products and business dynamics. For the wider organization, the pitch is collaboration: marketing, sales and operations each get answers shaped to their function, which is meant to close the gap between data teams and the rest of the business. In practice, the workflow starts with connecting a data source. Qquest lists data warehouses (Snowflake, Redshift, BigQuery), databases (MySQL, PostgreSQL, SQL Server), ad platforms including Google, Meta and TikTok, analytics tools such as Mixpanel, Amplitude and Google Analytics, and CRM systems such as Salesforce, Hubspot and Braze. From there, users query by text, and the site also advertises querying files alongside data. A Chrome extension is the entry point for many users, and a separate dashboard handles login and administration. Among text-to-SQL and conversational analytics tools, Qquest's differentiator is the framing of a trainable analyst rather than a generic chatbot on top of a database. It competes with BI copilots and warehouse-native query assistants. The product is labeled beta on Product Hunt, so teams should expect a maturing feature set and validate accuracy against their own schemas before relying on it for reporting.