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How the AI sees a candidate

Skills extracted from what a candidate built. Not from what they wrote.

The AI reads the actual files — code, thesis, design, reports — and every skill it extracts names the project it was read from.

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Worked example

A README in, typed skills out

Input — project README excerpt

# Adaptive reuse of the ex-Falck industrial site

Master thesis · Politecnico di Milano · Architecture · 2025

Designed a mixed-use intervention on 12,000 m² of the
former Falck steelworks in Sesto San Giovanni. The plan
keeps three of the original blast-furnace structures
(listed under Italian heritage law D.Lgs. 42/2004) and
inserts new residential, retail, and public-space programs
around them.

Deliverables: 1:200 master plan, 1:50 sectional studies of
the heritage shells, full sustainability assessment under
LEED v4 BD+C, structural feasibility study with FEM
analysis on the reused frames.

Software: Revit (BIM model), AutoCAD, Grasshopper + Rhino,
Adobe InDesign for the boards. Worked with two structural
engineers on the FEM model and with the municipality's
heritage office on the listed-element constraints.

Final review: presented to the jury in English; final PDF
set in Italian for the regional planning authority.

Output — typed skill buckets

  • Hard skills

    • Revit (BIM)
    • AutoCAD
    • Grasshopper
    • Rhino
    • FEM analysis
    • Adobe InDesign

  • Soft skills

    • Cross-disciplinary collaboration
    • Heritage-office negotiation
    • Public review presentation
    • Multi-stakeholder coordination

  • Design skills

    • Master planning (urban scale)
    • Sectional design
    • Adaptive reuse
    • Material composition

  • Domain knowledge

    • Italian heritage law (D.Lgs. 42/2004)
    • LEED v4 BD+C
    • Industrial heritage / brownfield reuse

  • Languages

    • Italian (thesis, final PDF set)
    • English (jury presentation)

In the product, where the analysis cited evidence, the sentence it found is shown with the skill.

What we show

Every extracted skill names the work it came from

  • Every skill shows recruiters its tier: confirmed by an internship supervisor or a referee, evaluated by a teacher, extracted by the AI, or self-reported.
  • The project each extracted skill was read from, with the analysed files named when the student shares them

What we don't claim

What the AI can't do

  • It doesn't measure a candidate's potential — only the skills evidenced in what they've already built
  • It does not replace the interview; it only tells you what to ask about.
  • It doesn't certify the work was the candidate's own — a teacher who followed it can say so, in an evaluation the student asks for

Search candidates on evidence, not on claims.

Search by skill, open the work the AI extracted it from, and write to the student.

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