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§ CASE / 002 · RECRUITMENT · AI & DATA

HR crawler

Connected Python pipelines crawl open-web registries and third-party databases, surfacing verified candidates through Metabase and a dedicated recruiter web app.

CLIENT
HR crawler
VERTICAL
Recruitment
CATEGORIES
AI & Data · SaaS
YEAR
2025
STATUS
Active
STACK
5 tools
HR crawler
§ 01
Context

Sourcing licensed specialists across a distributed network is slow and patchy.

Hiring managers had to comb through public registries by hand, and once a name surfaced there was no clean way to verify work history or find a warm-intro path.

§ APPROACH
Approach

Connected Python pipelines. Two ways in.

L1 · Crawl A graph of connected Python scripts against public licensing registries Python + MySQL Authoritative baseline
L2 · Enrich Joins against professional networks and third-party contact databases Python + MySQL Work history + warm-intro paths
L3 · Surface Metabase for analysts, a dedicated web app (recruiter dashboard, user management, activity log) for operators Metabase · Vue · Nest One source of truth
§ OUTCOME
Outcome

A live, fully-automated sourcing pipeline.

Weeks of manual trawling replaced with fresh monthly leads — each candidate arrives with verified credentials, a reconstructed work history, and a warm-intro path. Analysts slice data in Metabase; recruiters work cases from a dedicated dashboard with full user management and activity logging.

§ 02
Stack
PYTHON NEST VUE MYSQL METABASE
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