Casely vs TestMap.ai
Casely
casely.digitalTestMap.ai
testmap.aiSide by side
| Figure | Casely | TestMap.ai |
|---|---|---|
| Price from | — | $15/mo |
| Free plan | — | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | — | Yes |
| Plans published | 0 | 3 |
| Spec score · AI Test Case Generators | 7.5 (#21) | 7.9 (#10) |
| Purpose | Casely turns software requirements into QA test cases and exports them in a TestRail-ready Excel file. | TestMap.ai generates test cases from user stories and requirements and provides test management for software teams. |
| Security | The docs say generated API collections exclude credentials and use environment variables for secrets such as tokens. | The homepage states that data is encrypted in transit and at rest, isolated per organization, and never used to train AI models. |
Plans
In their own words
Casely
Casely turns software requirements into QA test cases through a conversation with Claude. It accepts PDF, DOCX, XLSX, TXT, and Markdown files, as well as pasted text. Before writing cases, Casely proposes coverage by module, estimates case counts, flags specification gaps, and waits for approval. Its test design uses equivalence partitioning, boundary value analysis, decision tables, state transitions, and error guessing. The tool can follow the columns, order, tone, and ID scheme of an existing test-case file. Exports include one Excel file and a Markdown file for each case; the Excel output is described as importable into TestRail, Qase, Zephyr, and Xray. When requirements identify API endpoints, it can create a Postman collection with an environment file and run instructions, without embedding credentials. Casely runs with Claude Code, claude.ai, and the Claude desktop app. A browser version with uploads and team review is in early access. The QA skill is free, open source, and MIT-licensed.
TestMap.ai
TestMap.ai generates structured software test cases from user stories and requirements, then provides tools to manage test work and results. Its AI can use project history, architecture decisions, domain language, documentation rules, and testing conventions as context. Generated cases include prerequisites, steps, expected results, and coverage labels, and can use traditional, BDD, or exploratory styles.
Teams can run tests, track results, create bug reports, and trace requirements through to test outcomes. Real-time comments, review, approval, and role-based permissions support collaboration. GitHub connections can import requirements and code context and sync generated cases back into a workflow; Jira is also listed as an integration. The Chrome extension records browser interactions, selectors, screenshots, and API requests, and recordings can be linked to cases. An MCP server and API let AI assistants search and retrieve test cases and access projects and test runs using API-key authentication and project scoping. Starter is free, with up to two projects and two team members. Pro costs $15 a month and supports up to 50 team members. The service is available on web, as an extension, and through an API.