Translate founder experience into recruiter-ready CVs using AI-driven framing

Many founders and operator-level entrepreneurs—part of a global addressable pool of roughly 5 million mid-to-senior professionals—struggle to convert ambiguous, product- or equity-focused accomplishments into recruiter-friendly, metric-driven CVs. Recruiters and hiring managers increasingly screen by measurable outcomes, so those with founder backgrounds often see lower callbacks or lengthy transitions because their impact is described in narratives rather than hiring signals. You could build an AI-driven platform that ingests founder inputs (decks, KPIs, product roadmaps) and outputs recruiter-ready CVs, tailored role summaries, and ATS-optimized application packages using LLM reframing, structured metric extraction, and role-specific templates; a human-in-the-loop editing and coaching tier would capture edge cases and ensure factual integrity. Commercially this fits a $1,200 annual value-per-customer services model with self-serve subscriptions, enterprise packages for outplacement firms, and high-touch coaching for executive placements. The timing is attractive: a $6.0B addressable market, elevated founder exits and layoffs, and rapid improvements in LLM quality make high-fidelity reframing economically viable now, while recruiters’ shift toward data-driven signals increases willingness to adopt translated narratives. To stand out you must combine recruiter-validated conversion templates and a growing dataset that maps founder activities to hiring signals with partnership pipelines into recruiting firms and ATS systems, plus transparent provenance and optional verifications to build trust; the key challenges will be acquiring recruiter feedback loops, preventing factual overclaiming, and differentiating in a medium-competition field.