Career workflow product for role scanning, fit scoring, evidence review, Battle Cards, and application kit generation.
Context: Active daily-use job-search command center. Built as a practical operating layer for scanning roles, scoring fit, decoding job descriptions, preparing application materials, and keeping claims tied to source evidence.
Job Radar started from a practical problem: the job search is too important to run from scattered tabs, copied job descriptions, vibes, and half-finished resumes.
A strong search needs intake, scoring, prioritization, evidence, application materials, review, and follow-up. Job Radar turns the search into a working command center.
Most job searches decay into noise.
Good roles get lost. Weak roles get too much attention. Job descriptions are hard to compare. Application materials drift away from the source. Candidates end up rewriting the same story repeatedly without a clean record of why a role fits.
The problem is not effort. The problem is operating structure.
Job Radar is a daily job-search workflow product for scanning roles, scoring fit, reviewing evidence, decoding job descriptions, building Battle Cards, and producing application kits grounded in source material.
It combines a scanner, scoring layer, evidence bank, Decision Desk, local AI advisor, and export flow. The system helps prioritize which roles deserve attention and turns each strong role into a decision-ready packet.
Scans and stores roles from multiple job sources. Scores opportunities against target lanes and evidence. Separates elite, green, maybe, and trash roles. Decodes job descriptions into decision-ready briefs. Builds Battle Cards for role strategy. Generates application kits tied to source material. Tracks evidence so claims do not get invented. Supports local AI advising with deterministic fallback.
Job Radar proves that the same operating pattern works on the job search itself.
A career move is not just "apply more." It is a workflow: intake, score, prioritize, gather evidence, generate materials, review, send, follow up, and learn.
Builders, operators, and candidates whose proof-of-work is stronger than a normal resume can explain. The person who needs to turn evidence into targeted applications without inventing claims.