Project Type: AI Product Prototype / Career Intelligence Agent / AI Evidence Builder
Target Users / Buyers: HR tech founders, career coaches, job platforms, professionals, students, recruiters, AI product prototype clients, and founders building career-tech products.
Core Problem: Most professionals describe their experience through claims rather than evidence. Resumes, LinkedIn profiles, and interviews often contain statements like “built AI products,” “improved workflows,” or “worked cross-functionally,” but the proof behind those claims is scattered across projects, achievements, metrics, GitHub links, case studies, interview notes, product decisions, and personal reflections.
Core Workflow:
Career data → evidence extraction → achievement structuring → project-to-skill mapping → role-fit analysis → evidence scoring → resume/interview/story generation → portfolio positioning recommendations.
Main Outputs: Career evidence library, role-fit proof points, stronger resume bullets, interview stories, STAR-style examples, project evidence map, positioning summary, portfolio content suggestions, LinkedIn profile improvements, and target-role fit narratives.
Tools / Stack: OpenAI, structured prompting, evidence extraction, scoring logic, embeddings, semantic search, Next.js, Supabase, user profile database, portfolio data model, prompt evaluation, and product workflow design.
Services Demonstrated: AI product prototyping, AI product building, AI evidence systems, structured text analysis, career-tech product development, AI-powered personal knowledge systems, and workflow-based AI product design.
Hiring, freelancing, and career positioning are increasingly evidence-driven.
A professional may have strong experience, but that experience often becomes difficult to evaluate because it is not organized as proof. Resumes compress years of work into short bullets. LinkedIn profiles reward polished claims. Interviews depend on recall under pressure. Portfolios often contain useful work, but the connection between projects, skills, outcomes, and target roles is not always clear.
The problem is not just resume writing.
The deeper problem is that career history is messy data.
It lives across many places: