People lose jobs to ATS & signals — AI career intelligence to surface fit

Many qualified candidates never reach a human recruiter because automated screening and opaque signal-based ranking in ATS and job platforms filter or deprioritize resumes; this problem affects roughly 200 million working professionals globally, especially mid-career role-changers, return-to-work parents, and underrepresented job seekers who lack recruiter networks. The economic weight of this friction is reflected in a roughly $30 billion addressable market for HR/job-search SaaS and career services (200M professionals × $150/yr), with measurable personal and organizational costs in time-to-hire and missed matches. The product would be an AI career-intelligence layer that extracts and models signals from ATS, job boards, LinkedIn, and outreach channels, then generates prioritized interventions—resume rewrites, timing recommendations, outreach sequences, and company-fit scores—validated by closed-loop feedback from ATS/job-board APIs to show which changes causally improve interview and hire rates. Built-in experiment tooling would let users A/B test rewrites and messaging, while enterprise integrations would offer licensing to outplacement providers and staffing firms. This is an attractive moment because large LLMs make personalized resume and outreach generation at scale feasible, APIs make signal extraction and outcome measurement possible, and tight labor markets increase willingness to pay for tools that demonstrably raise conversion. To stand out you’ll need a defensible data moat and partnerships that enable outcome tracking, a privacy-first design, and a focus on signal-level causal impact (ambitious early KPI: 2–5× interview conversion lift for tested cohorts); challenges include obtaining reliable ATS data access, proving causality in noisy hiring pipelines, and fending off established vendors who can add adjacent features.