Overview

Sweepster Dashboard

A portfolio-style product architecture case study for a GenAI career intelligence system that turns raw vacancies into explainable, scored matches against a candidate capability profile.

117
sample roles in review
45
capabilities detected from profile
10+
modules in the explainable pipeline

Why it matches the AI deployment domain

Customer reality → AI workflow

Sweepster starts with a messy human workflow: job search, career transition and role ambiguity. It converts that workflow into a structured GenAI decision system.

Architecture-aware product thinking

The design separates extraction, validation, mapping, scoring, questions and learning so every decision has one owner and can be tested independently.

Core differentiators

  • No “open to everything” matching state — the system requires a user direction.
  • Unknowns are never guessed into a score.
  • Open questions are asked only when they can change the decision.
  • Feedback learning is visible, reversible and user-confirmed.
GenAI product architecture Human-in-the-loop Explainable scoring Schema-first implementation Responsible AI