Job Application Automation Platform

An end-to-end platform that builds a candidate profile from any input, discovers relevant jobs, scores fit with an evidence map, and generates a tailored resume, cover letter, and pitch through a quality-filter pipeline — then prepares a final application package for download.

Private repository. Source code is not public. Implementation details, a guided walkthrough, or a sanitized sample are available on request.

This proof is a full application platform: multi-modal profile intake, scheduled job discovery, explainable matching with an evidence map, and a generate-then-quality-check pipeline that ends in a downloadable package. It is private-repo proof — the architecture and release snapshot are verifiable in source, while integrated send and any placement outcome stay out of scope here.

Proof assets and links

private-repo-proof: Mavline/job_autosearch — frontend plus Python backend on Supabase, with LLM-based profile building, job discovery, explainable matching, and resume/cover-letter package generation and PDF export. Release snapshot in RELEASE_NOTES.md; integrated send deferred. No public deployment claimed.

Repository or technical proof boundary. Portfolio proof, not measured client metrics.

Problem

Applying to jobs well is repetitive and slow: build a profile, find relevant postings, judge fit, and tailor a resume and cover letter for each one. Each step is automatable, but only if the matching stays explainable and the generated text passes a quality bar.

Context

This is a full application platform with a frontend, a Python backend, and external services — not a prompt. It is presented as private-repo proof. Integrated sending to recruiters is deliberately out of the current release boundary: the platform prepares a downloadable package, and the candidate uses their own delivery channel.

Inputs

Candidate background from free text, uploaded documents, voice dictation, or an AI interview
Job search directions: roles, location, salary, constraints, target companies
Discovered job postings from web/job search providers

Technical approach

A profile is built from any input — free-form text, uploaded documents, voice dictation, or an AI interview. Job discovery runs on demand or on a schedule, with normalization and deduplication of postings. A matching stage scores fit and produces an evidence map that links the candidate's facts to each job's requirements, so a score is explainable rather than opaque. A generation stage produces a tailored resume, cover letter, and short pitch and runs them through a quality-filter pipeline; the candidate can review and edit the text and download a final package PDF. Statuses, history, logs, and cost/usage are tracked, with browser push notifications for pending actions. Supabase handles auth and storage; an LLM provider handles language, embeddings, and audio; a search provider handles discovery; and a PDF renderer produces the final package. Integrated send is a deferred, later-release seam.

Stack

Frontend application with a Python backend
Supabase for authentication and storage
LLM provider for language, embeddings, and audio (transcription)
Web/job search provider for discovery
Server-side PDF rendering for the final package

What was built

A working platform: profile building from text, documents, voice, or an AI interview; automatic and on-demand job discovery with deduplication; explainable match scoring with an evidence map; tailored resume, cover-letter, and pitch generation behind a quality filter; an application review/edit screen with final-package PDF export; status, history, and cost/usage tracking; and browser push notifications. Integrated send is scoped as a future capability.

Deployment

Private-repo proof with a documented release snapshot. The platform architecture is verifiable in source; no public live URL, send automation, or measured placement outcome is claimed for this page.

What can be reused

Multi-modal profile intake: text, documents, voice, and AI interview into one profile
Explainable matching with an evidence map from candidate facts to requirements
A generation-plus-quality-filter pipeline before any output is shown
Server-side PDF packaging with a deferred delivery seam