AI Job Application Agent

A grounded job-application copilot. Search live listings across four ATS providers, paste a job description and see it parsed into hard / soft / must-have skills, and run a five-stage supervised pipeline that produces a tailored resume + cover letter — every claim anchored to evidence from the source resume.

Live: job-application-copilot.xyz · Workspace: app.job-application-copilot.xyz

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Visual tour

Step 01 — Resume builder (chat one into existence)	Step 03 — Job Detail (parsed JD with match score + skill chips)

Output — classic_ats theme	Output — Cover letter

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What's actually inside

System	What it does
Live job search	Cached index of ~12,000 open roles from Greenhouse, Lever, Ashby, and Workday — refreshed every 4 hours. Filter by company, work mode, role type, posted-within. Sort by relevance, recency, or alphabetical.
Resume intake	Upload PDF / DOCX / TXT, or chat one into existence with the conversational builder. Parsed into a normalized profile with skills, experience timeline, projects, publications, and certifications.
JD review	LLM-first JD parser with regex fallback. Surfaces hard skills, soft skills, and must-haves; shows match score against the loaded resume.
Supervised pipeline	Matchmaker → Forge (tailoring) → Gatekeeper (review) → Resume Generation → Cover Letter. Three-layer LLM retry stack with per-agent fallback isolation, deterministic floor on every stage.
Artifact export	Two themes (classic_ats for ATS parsers, professional_neutral for human readers) in DOCX or PDF. The same source data feeds either pathway.
Grounded assistant	Floating workspace chat with full context of the loaded resume, JD, analysis state, and saved jobs. Streams answers as they generate.
Command palette	⌘K / Ctrl+K from anywhere — jump between steps, load a saved job, re-ask a recent assistant question, or run the analysis.

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How job discovery works

The cached jobs layer lives in Postgres (cached_jobs table) and is refreshed by a scheduled worker that fans out across all four sources. Highlights:

• ~117 Greenhouse boards + 30 Lever sites + 36 Ashby boards + 11 Workday Fortune-500 tenants in the active source pool.
• 4-hour refresh cadence via pg_net cron triggering the /admin/refresh-cache endpoint (6 refreshes per day at 00:00 / 04:00 / 08:00 / 12:00 / 16:00 / 20:00 UTC).
• Relevance-ranked search through a Postgres RPC that combines query token coverage with recency.
• Saved-jobs drawer with a 24-hour TTL — bookmarks survive page reloads but expire if you don't act on them, with an EXPIRED badge so nothing silently disappears.

See ADR-013 and ADR-014 for the load-bearing decisions.

How the supervised pipeline works

ApplicationOrchestrator._run_pipeline runs five stages with progress callbacks, per-stage duration logging, JSON-contracted agent outputs, and per-agent fallback isolation:

1. Matchmaker (deterministic) — build_fit_analysis() compares the candidate profile against the JD and produces matched / missing skills.
2. Forge (TailoringAgent) — rewrites the deterministic baseline into role-specific resume guidance.
3. Gatekeeper (ReviewAgent) — checks grounding, reports unsupported claims, and returns corrected tailoring when repairs are possible.
4. Resume generation (ResumeGenerationAgent) — builds the final tailored resume artifact from the reviewed output.
5. Cover letter (CoverLetterAgent) — runs only after review approval and produces a role-specific cover letter.

Each agent follows the same operating shape: deterministic baseline first, LLM-assisted refinement second, structured JSON output, and a deterministic fallback when assisted execution is unavailable. Per-agent fallback isolation means a single failing agent falls back independently — the other three keep their LLM-quality output. See ADR-018 for the three-layer retry stack (SDK retry × 2 + app-level retry + per-agent retry).

How grounding works

• Deterministic services build the candidate profile, JD summary, fit analysis, and first-pass tailored draft before the agent layer runs.
• ReviewAgent returns grounding_issues, unresolved_issues, revision_requests, and an optional corrected_tailoring payload.
• The orchestrator uses corrected_tailoring as the downstream source of truth when review repairs the draft.
• Cover-letter generation is gated on review approval.
• The fallback review path checks whether the output references missing hard skills that aren't evidenced in the source profile.

Engineering notes

• 44 Python test files cover parsing, normalization, fitting, tailoring, orchestration, builders, exports, auth, quotas, persistence, error handling, and the four ATS adapters.
• Quality runners in tests/quality/ produce evidence for each LLM-driven stage (parser, tailoring, review, resume gen, cover letter, assistant, JD parser, latency baseline).
• 19 ADRs in docs/adr/ record the architectural decisions, including the Streamlit-first → Next.js + FastAPI transition (ADR-012), DOCX-first export (ADR-015), conversational builder (ADR-016), state-aware assistant (ADR-017), and three-layer retry stack (ADR-018).
• Architecture details live in docs/architecture.md.

Deployment

• app.job-application-copilot.xyz → Vercel-hosted Next.js workspace
• api.job-application-copilot.xyz → VPS-hosted FastAPI backend
• frontend/ → Next.js + React 19 + Turbopack
• backend/ → FastAPI + Uvicorn, async OpenAI client, Supabase Postgres
• backend/vps/ → Docker Compose + Caddy bundle for the backend stack
• src/ → shared Python core (orchestrator, agents, builders, schemas, services)