Outship provides an AI‑enabled technical screening platform that lets hiring teams evaluate engineers by having them work on real code from GitHub using coding agents such as Claude Code and Codex.
Funding
Funding not disclosed
Founders
Product
Problem
Technical hiring still relies on algorithmic puzzles and isolated coding tests that do not reflect how engineers work with AI‑assisted tools in modern development environments. This makes it difficult to assess a candidate’s true problem‑decomposition skills, ability to guide AI agents, and competence in delivering production‑ready code.
Solution
Outship offers an AI‑enabled technical screening platform that records every candidate interaction—prompts, edits, commands, and decisions—while they work on real codebases imported from GitHub. By having candidates solve authentic bugs or ship pull‑requests using coding agents such as Claude Code and Codex, the platform captures a complete, observable workflow. The recorded data is analyzed to surface how candidates decompose problems, steer AI assistance, and recover from errors, providing hiring teams with concrete evidence of engineering ability rather than superficial quiz scores.
Target Audience
Primary customers are engineering hiring teams at technology companies that need to evaluate senior or AI‑native developers, including engineering managers, talent acquisition specialists, and technical recruiters.
Features
- Integration with existing GitHub repositories to import real‑world interview projects or past bugs
- AI coding agents (e.g., Claude Code, Codex) embedded in the coding environment for candidate use
- Automatic capture of all prompts, code edits, terminal commands, and decision points during the task
- Analytics that evaluate problem decomposition, agent‑guidance handling, and test coverage quality
- Ability to assess candidates on authentic pull‑request work, including end‑to‑end shipping of code
- Optional GPU and specialized compute resources for resource‑intensive tasks