Low-code platform for building AI-powered internal software and agents
Retool is a low-code application platform where developers assemble apps, agents, and workflows by combining pre-built components with custom code and LLM integrations. The stack reveals a mature, infrastructure-heavy organization—Kubernetes, Terraform, Datadog, Grafana, WebAssembly, and seccomp all point to a platform managing complex isolation and observability at scale. Adopting RAG signals deepening AI capabilities. The hiring mix is engineering-forward (13 roles) with concurrent sales expansion (10 roles), reflecting a shift from product-led growth toward hunting larger enterprise deals.
Retool provides a low-code development platform for building internal software, agents, and data workflows. The platform abstracts datasource connectivity (Salesforce, NetSuite, Stripe, Databricks, and others) and integrates LLMs to accelerate deployment of internal tools and automation. Founded in 2017 and based in San Francisco, the company operates at 201–500 employees and is privately held. The active project portfolio—ETL optimization, AI-enabled accounting automation, customer health metrics, revenue strategy tooling, and audit logging—reflects a customer base using Retool to solve operational and financial processes. Active hiring across engineering, sales, and product in the US and UK indicates growth-stage scaling.
Retool's core stack includes JavaScript, TypeScript, Node.js, React (frontend), Python (backend), and Docker / Kubernetes (infrastructure). For data, the platform integrates Kafka, Databricks, dbt, and Apache Airflow. Observability runs on Datadog and Grafana. The platform adopts RAG for AI features.
Recent projects include AI-enabled automation for accounting, ETL optimization, customer health metrics definition, audit logging, segmentation model design, and larger enterprise deal closures. Pain points include resource constraints for internal tooling, security governance gaps, and clunky legacy internal software.
Retool's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.