How hiring teams can use PraxisAI.
These are illustrative scenarios, not customer stories: we are new, and we would rather show you how the product works than invent quotes. Each one is built from signals the report genuinely records.
Screening 200 applicants for an AI-native backend role
A small team needs a shortlist from a large applicant pool without spending engineer hours on every first round. A general assessment link and the candidate board do the first pass.
- Role
- Backend engineer, Python
- Assessment
- Real-repo bug fix, medium difficulty band, 60 minutes
- Read the scenario →
Replacing a LeetCode round with a real-repo round
An algorithm puzzle tells you little about someone who will spend their days steering an agent through a large codebase. A real-repo round tests the job as it is now done.
- Role
- Full-stack and platform engineers
- Assessment
- Real-repo bug fix, a fixed issue per opening, 45 to 90 minutes
- Read the scenario →
Calibrating a take-home for senior engineers
Senior candidates resent unbounded take-homes, and teams cannot tell how long they really took or how much was the agent. A 24-hour recorded build fixes both.
- Role
- Senior and staff engineers
- Assessment
- Take-home project, up to 24 hours, with a rubric shown up front
- Read the scenario →
Want to run one of these on your roles?
Tell us about the roles you hire for and the assessment format you want to improve.