Foretoken AI

Most technical assessment tools solved the wrong decade's problem. They optimized for scoring isolated function-writing under time pressure, which is further from the actual job every year that AI assistants become standard equipment. Foretoken AI inverts that: candidates do a job-shaped task — debug a repository, add a feature, build an app — using whatever tools they'd normally use, and the platform captures the process, not just the artifact. That's the real product. The evaluation layer looks at reasoning, documentation, and tool usage, and the output report includes screen recordings, GitHub links, and the AI prompts the candidate wrote. For a hiring manager, seeing the prompt history is often more predictive than seeing the diff, because it shows how the person decomposes an unfamiliar problem. The screen, audio, and keystroke recording is also the mechanism for trust: you don't have to take the submission's word for it that it was the candidate's own work. Where it earns its keep: senior and mid-level engineering roles in companies that have already decided AI-assisted development is the workflow, remote-first teams running asynchronous pipelines, and organizations that want the same problem given to every candidate to reduce whiteboard-style bias. Where it doesn't: entry-level screening that depends on algorithmic fundamentals, roles where AI use is prohibited, and mass-hiring funnels that need instant automated pass/fail scoring with no human review. The honest limitation is that simulation quality is a setup problem — if you write a vague task, you get a vague signal. Budget real time to design the project. Integration details aren't publicly documented, so confirm ATS compatibility before committing. Standardized benchmark comparisons across candidates aren't what this produces; it produces depth per candidate, which is a different trade.