Platform Features
SKILL GAP ANALYSIS
- For each matched job, we surface the specific skills and competencies the role requires versus what your resume demonstrates — so you know exactly what you bring and what you don't before you invest time in an application.
- Job requirements are cross-referenced against Department of Labor occupation data from O*NET — the federal standard for occupational benchmarks. This tells you whether an employer's posting covers the canonical skills for the role, or whether it is a thin description that may not reflect the actual position.
Know Where You Stand Before You Apply
Federal Occupation Benchmarks
RESUME INTELLIGENCE
- Upload your resume once. Our NLP pipeline parses your skills, experience level, job titles, and professional profile — no manual tagging required. Your extracted profile becomes the benchmark against which every job listing in the database is evaluated. Update your resume any time to recalibrate your matches.
Automated Profile Extraction
JOB SEARCH
- Full-text and semantic search powered by PostgreSQL and pgvector. Queries run against a continuously updated index of listings from public sector, nonprofit, and technology job boards — results in milliseconds. Search returns ranked results, not an unsorted dump: every listing in the result set is pre-scored against your profile so the most relevant opportunities surface first.
- Job data is sourced from IdealistJobs, CareerOneStop, and the federal O*NET occupation database and refreshed on a continuous basis. You search one place and get curated coverage across public sector, nonprofit, and technology roles — without manually checking multiple boards.
Speed and Precision
Broad Coverage, One Query
JOB MATCHING
- Every listing is scored against your resume using natural language processing and machine learning. A numeric relevance score tells you how closely a role's requirements match your background before you read a single line of the description. Your matched jobs are sorted by score — highest relevance first.
- Matching goes beyond keyword overlap. Sentence-transformer embeddings capture the meaning of your experience and the job description, so roles that describe your skills differently still surface as strong matches. A job that says 'data pipeline engineering' and a resume that says 'ETL development' are understood as related — not missed.