Retool

Retool is a developer tool that builds AI apps, workflows and agents on top of your production data. Retool AI turns your production data into working AI apps, workflows and agents, with enterprise security and audit trails built in. A strong fit for internal tooling teams that need governed AI rather than another prompt playground.

Retool AI is the artificial intelligence layer of the Retool platform, aimed at teams that want to move LLM features past the prototype stage and into governed internal software. Rather than handing you a chatbot or a prompt sandbox, it generates full stack applications scaffolded from your live production data, including Postgres, Databricks and Salesforce, so the resulting tool already reflects your real schema, roles and permissions from the first render. The build experience is grounded rather than conversational. You can click a component, @mention a resource or select a region of the app and prompt a change against that specific context, which keeps edits anchored to the actual app state instead of relying on a chat thread to infer intent. Generated apps inherit existing SSO, role based access control and data level permissions by default, so admins do not have to re-implement security on every new tool. Beyond apps, the platform covers Workflows for chaining AI actions with data transformations and business rules, complete with monitoring and audit logs that record what each run did. Agents extend this to reasoning over connected business systems, and one-click RAG injects your own data into model calls. Retool states that any model provider works, whether direct from OpenAI, Anthropic or Google, through a cloud provider such as AWS or Azure, or a model you bring yourself. It fits organisations that already run internal tooling on Retool, or teams with strict compliance requirements that rule out unmanaged AI builders. The catalogue of prebuilt patterns (support agents, dashboards driven by natural language, approval routing, report generators, internal search) shows the intended shape of the work: operational software, not customer facing creative products. Smaller teams without existing data infrastructure will find the value depends heavily on what they can connect.