Quell

Quell is an AI agent platform for user acceptance testing. Its role-based agents read acceptance criteria from tickets and designs, generate and execute test cases on live builds, and return screenshot and video evidence with tickets filed automatically. It targets fintech and other regulated product teams that need audit-ready UAT.

Quell is a user acceptance testing platform built around a team of AI agents. Instead of a single scripted test runner, Quell deploys role-specific agents (product, design, QA, compliance, marketing, operations) that review a build from several viewpoints at once. Each agent reads the acceptance criteria, generates test cases, executes them against a live deployment, and reports back what failed. The stated goal is an 80 percent reduction in UAT cycle time. The workflow is designed to fit around existing tooling rather than replace it. Quell connects to issue trackers, repositories and deployment environments, then pulls acceptance criteria straight from a Linear or Jira ticket or a Figma design. Tests can be triggered automatically when a ticket changes status, or run manually. Results come back with screenshots and short screen recordings attached to each test, and the agent can open tickets for the problems it finds, tagging team members and collecting launch approvals along the way. Quell is aimed at fintech and other regulated product teams where UAT is a compliance obligation as much as a quality gate. The appeal is audit readiness: every requirement is validated and documented, reports are generated for compliance reviews, and the company states it is pursuing SOC 2 Type 2 certification. Cross-functional groups (product managers, QA engineers, developers, compliance officers) are meant to share one workspace rather than trade spreadsheets. Against scripted automation frameworks, Quell trades fine-grained control for setup speed: there is no scripting, and the agents infer coverage from written requirements. Teams that need pixel-level assertions or complex test data fixtures will still want a conventional framework alongside it. Pricing runs on task credits, where a task equals one test case generated or executed, so high-volume regression suites will consume the allowance quickly. Quell is a young product and its roadmap depends on the agent team staying accurate across varied applications. It suits teams that want broader, faster acceptance checks than a manual pass allows, not teams replacing unit and integration testing.