Tutorops gives TPOs the tools to assess, analyse, intervene and report. Anything still in development says so on the card.
Scores every student against every role you hire for, using the weights you set. Deterministic arithmetic, not a model guessing.
For each student and role, which skills are short and by how much — ranked by the readiness points closing them returns.
Multiple-choice papers from a tagged question bank. One link per student, no account needed, and levels are measured rather than typed in.
The shortest set of skills that clears the threshold, ordered by points gained per training hour, as a dated weekly plan.
Filter eligible students, rank by fit and export a list with skill evidence attached. Personal contact details never leave with it.
Readiness by department, batch and role, plus the gaps costing the whole cohort the most readiness points.
One spreadsheet. Column names are matched for you, bad rows are reported rather than silently dropped, and re-uploads update in place.
A drawer on every score showing the dimension, the weight and the skill level it came from. Defensible to a principal or a recruiter.
Read a recruiter JD into a draft requirement list that you confirm before it is saved. Extraction engine is built; the upload screen is not.
In developmentReadiness, eligibility, gap severity and plan selection are deterministic arithmetic. The same student and role always produce the same number, and every number traces to a recorded skill level. That is what makes a shortlist defensible.
The one job AI has is turning an unstructured job description into a draft requirement list, which a TPO confirms before anything is saved. Output is validated against a strict schema and malformed responses are rejected rather than guessed at.
Upload one spreadsheet and get your readiness picture in a few minutes. No sales call.