PromptQL

PromptQL is an AI agent data access tool designed for private data. The difference from a plain search box is the order of operations: it gathers relevant information first, then applies large language models for classification and follow-up processing. That is meant to handle non-standardized queries in closed domains, where a keyword search falls over because nobody typed the query the way the records were written. You connect your private built-in informations, add your own LLM API key, and build agent query plans against the connected data. It also provides the ADAB benchmark for evaluating agents, and the site frames building a first agent as free so you can assess performance.

- Query private built-in informations in plain language

- Build agent query plans over connected data

- Retrieve specific records such as receipts or purchases

- Extract themes such as pain points from sales data

- Benchmark an agent with ADAB and compare

- Developers building data access agents

- Teams querying internal private data

- Analysts pulling answers out of scattered records

- Anyone evaluating agent performance against a benchmark

- Organizations that cannot send private data to public tools

The free agent is framed as a way to assess performance, so treat it as an evaluation tier rather than a permanent free service, and confirm current terms before you build on it. You also supply your own LLM API key, which means model usage costs are yours and can add up quickly once queries run at volume or the agent makes several calls per question. Connecting built-in informations takes real setup work, and accuracy depends on both the model you choose and how clean the underlying data is, so messy records will produce confident wrong answers. Plan on iterating: the first query plan is rarely the one that works, and reviewing outputs before anyone acts on them is essential.