Executive Summary

Many professionals waste hours hunting across job boards, company sites, and recruiters, and roughly 100M active job-seekers face information overload and low-quality matches that cost time and missed opportunities. The pain is acute for mid-career and busy professionals who prefer discovery and automation over manual searching. You could build an automated personal job-search agent that ingests resumes, stated preferences, and workspace context (Notion/Slack/Calendar), uses LLMs to semantically score and rank openings, and pushes daily matches and actionables into users’ workflows. Paid features would include scheduled digests, one-click apply automation, and explainable relevance scores to build trust and reduce decision friction. The market is attractive: ~100M potential customers willing to pay about $60/year implies a $6.0B TAM, and the idea scores highly on market attractiveness (88/100) with solid revenue potential (78/100). Adoption tailwinds—LLM-driven personalization, workspace-native workflows, and consumer expectations for proactive automation—make discovery and retention easier today than five years ago. This product can stand out by deeply integrating into users’ workspaces and offering LLM-based, transparent relevance scoring plus automation-first UX that demonstrably saves time. Key challenges are sourcing comprehensive job feeds, protecting user privacy, and continuously validating match quality to convert users from free trials to paid subscriptions, but these are addressable with careful partnerships and metrics-driven iteration.

Market Opportunity

Automated personal job search agent that finds, scores, and pushes matches targets a $6.0B = 100M active job-seekers willing to pay $60/year for discovery and automation total addressable market with medium saturation and a year-over-year growth rate of 8% YoY estimated for career tools and HR tech consumer adjacent markets (based on HR tech and consumer subscription growth reports).

Key trends driving demand: LLM-driven personalization — LLMs enable natural-language understanding of job descriptions and candidate preferences, making relevance scoring more accurate and useful to job seekers.; Workspace-native workflows — Professionals increasingly store workflows in tools like Notion, so delivering job matches directly into those workspaces removes friction and increases retention.; Automation-first consumer apps — Users expect scheduled and proactive automation (daily digests, alerts) which reduces time-to-value and increases willingness to subscribe.; API availability and structured feeds — More boards and companies publish structured feeds, reducing the need for brittle scraping and enabling reliable aggregation..

Key competitors include Teal, Huntr, Jobscan.

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Automated personal job search agent that finds, scores, and pushes matches

Executive Summary

Market Validation

Market Opportunity

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