AI career infrastructure for job seekers and institutions. 30+ tools, three engines, one founder. Seeking NVIDIA NIM integration for document intelligence at the parsing layer.
75% of resumes are rejected by ATS before a human sees them. Job seekers don't know why. Career centers can't scale 1-on-1 coaching. The gap between what applicants write and what systems read is a document intelligence problem.
ATS-optimized resume tailoring with semantic keyword matching, recruiter heatmap simulation, and multi-format export. Supports Workday, Greenhouse, Lever, iCIMS, Taleo, and 40+ ATS platforms.
Adaptive skill development using spaced retrieval practice (Ebbinghaus, Leitner, Pimsleur). Skill gap analysis against O*NET/ESCO taxonomies. Mastery-based progression, not time-based.
AI-curated industry briefings with multi-voice podcast generation. Source credibility scoring, recency weighting, and transparent curation logic. No black-box algorithms.
Resumes parse locally in the browser before any data reaches an AI model. Generation runs through Supabase Edge Functions proxying Gemini. Deployed on Vercel with GitHub Actions CI/CD.
Our generative pipeline (Gemini) handles text generation. NIM handles the structured understanding that generative models aren't optimized for.
Use NIM vision models to analyze resume visual structure — section boundaries, column layouts, header hierarchies. Powers our recruiter heatmap simulation with actual document layout data instead of heuristic guessing.
Use NIM NER models to extract structured skill, role, and experience entities from unstructured resume text. Enables semantic matching against job descriptions at the entity level, not string level.
Generative LLMs hallucinate structure. When we ask Gemini to extract skills, it invents ones that aren't there. NIM inference-optimized models give us deterministic, structured extraction — the right tool for the parsing layer, leaving generation to the generative model.
Direct consumer SaaS at $9.99/mo. 30+ tools accessible immediately. Free tier with premium upgrade. Chrome extension for in-browser tailoring.
Annual campus licenses for university career centers, community colleges, workforce development programs. FERPA-ready, WIOA-aligned, LTI 1.3 integration for Canvas/Blackboard/Moodle/D2L. Institutional admin dashboard with cohort management and outcomes reporting.
Eligible for DOL Strengthening Community Colleges grants, Perkins V, Title III/V. Career centers can include TMS in federal proposals as a technology partner.
Deploy NIM NER endpoint via build.nvidia.com. Build extraction pipeline in Supabase Edge Function. Replace heuristic keyword matching with entity-level skill extraction. Validate against O*NET taxonomy.
Integrate NIM vision/OCR models for resume layout analysis. Feed structured layout data into recruiter heatmap simulation. Support multi-column, non-standard, and designed resumes that PDF.js text extraction misses.
Deploy NIM-powered pipeline to institutional partners. Benchmark extraction accuracy vs. baseline. Publish results for NSF SBIR preliminary data. Scale inference for campus-wide deployments.
Access to NIM microservices for document understanding and NER models via build.nvidia.com. Prototyping and production inference for resume parsing pipeline.
GPU-backed compute for NIM container deployment. Scale inference for institutional campus deployments serving thousands of students per semester.
Access to NVIDIA engineering support for optimizing NIM model selection and deployment architecture for our document intelligence use case.