Production-grade AI career matching platform using pgvector
The Asset: CareerOS CareerOS is a high-velocity, pre-revenue AI career intelligence platform. It bridges the gap between candidate profiles and industry requirements through a deterministic MatchEngine and a multi-strategy Resume Parser. Unlike standard GPT wrappers, CareerOS involves deep engineering to solve one of the hardest problems in HR software: extracting structured data from complex, multi-column PDF layouts. By acquiring CareerOS, you are purchasing a production-hardened codebase, proprietary algorithms, and bypassing 6+ months of specialized advanced R&D. Proprietary IP & Technical Moat Graph-Based Resume Parser Standard LLM parsers break when fed non-linear, multi-column pdf resumes. Our hybrid ingestion pipeline solves this through Entity Graphing (spatially mapping text nodes before semantic extraction) and Fallback Hardening (combining deterministic heuristics with LLMs for extreme accuracy on unconventional resume layouts). Confidence-Aware MatchEngine A modular scoring system that evaluates candidate Fit Score against job roles via a Plugin Registry Pattern. It features Heuristic Depth (weighing project complexity intuitively) and a Verification Layer (flagging low-confidence data extractions, maintaining 100% data integrity for recruiters). Semantic Discovery Engine CareerOS leverages native Postgres Vector Similarity to enable natural language role discovery. Candidates and recruiters can find jobs that feel right semantically, looking far beyond generic keyword matching. Specifications & Tech Stack This codebase is strictly typed, highly modern, and built to scale to 100k+ roles seamlessly via HNSW vector indexing. Runtime: Next.js 16 Language: TypeScript 5+ (Strict Mode) Identity: Clerk Database: Supabase (Postgres, pgvector) Intelligence Layer: Google Gemini 1.5 Flash Security: Zero-Trust API validation layer Why Buy CareerOS? Currently, the platform has 0 monthly active users and $0 in revenue. You are acquiring the technical infrastructure. If you are a solo founder wanting to launch a premium job board tomorrow, or an established Recruitment Agency looking to upgrade your legacy resume parser, building this tech from scratch will cost upwards of $40,000+ in engineering salaries. CareerOS provides the ultimate running start.
Project details
Built with:TypeScript,JavaScript,CSS,PLpgSQL,Batchfile
Next step: <ol><li data-list="ordered"><span class="ql-ui" contenteditable="false"></span><span style="background-color: rgb(31, 31, 31); color: rgb(204, 204, 204);">Launch CareerOS directly to candidates, charging a monthly subscription for advanced resume parsing, skill gap analysis, and AI-driven job applications.</span></li><li data-list="ordered"><span class="ql-ui" contenteditable="false"></span><strong style="background-color: rgb(31, 31, 31); color: rgb(204, 204, 204);">B2B Reciprocal Matching:</strong><span style="background-color: rgb(31, 31, 31); color: rgb(204, 204, 204);"> Pivot the platform to be recruiter-facing. Agencies can pay high-ticket licenses to use the pgvector similarity search to instantly find the "Top 1%" of candidates from a database.</span></li><li data-list="ordered"><span class="ql-ui" contenteditable="false"></span><strong style="background-color: rgb(31, 31, 31); color: rgb(204, 204, 204);">API Licensing (Enterprise):</strong><span style="background-color: rgb(31, 31, 31); color: rgb(204, 204, 204);"> The Resume Parser and MatchEngine are highly modular. They can be cleanly extracted and sold as a REST API to existing Applicant Tracking Systems (ATS) that desperately need to upgrade their legacy, regex-based PDF parsers.</span></li></ol><p><br></p>
For sale
Asking price: $10,000 USD
What's included
Source code, All related data, All related design work
The complete Next.js 16 source code repository. Full rights to the proprietary Graph-Aware Resume Parser and MatchEngine IP. The HANDOVER.md engineering document (explaining the architecture in detail). Transfer of the Vercel hosting project and Supabase (Postgres/pgvector) instance. 30 days of post-sale email/chat support to assist your engineering team with the codebase transition and environment setup.
Reason for selling
I am busy with other things and no longer have time to maintain this project.
Project metrics
Average Monthly Visitors: Pre-launch
Average Monthly Revenue: Pre-revenue
Average Monthly Downloads: Pre-launch / New project
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