Semantic Career Workflow Automation
A semantic application-ops workflow that extracts role pages, ranks fit with vector search, and prepares reviewable draft outreach without resorting to brute-force automation.
What it is: A semantic application workflow that turns noisy role pages into ranked opportunities and editable draft outreach.
What I built: Designed and implemented the semantic matching pipeline, orchestration flow, and draft-generation guardrails.
Current state: Pilot-stage work: real capability and working flows are in place, but stronger reliability or polish still matters.
Why it matters: Built a semantic role-ranking pipeline with vector search instead of keyword-only filtering.
Category: Product / System
Status: Pilot
Visibility: Public
What this project is
This project focuses on the front half of the application process: deciding which roles deserve attention and preparing a credible first draft once a role clears that bar. It extracts role pages into structured data, scores fit semantically, and prepares outreach that can be reviewed before anything is sent.
Why I built it
Manual role triage was too repetitive and too noisy. I wanted one workflow that could pull role requirements, compare them against real profile context, and prepare a usable draft without repeating the same research and drafting work every day.
Constraints
- Job pages have inconsistent structure and noisy text.
- Relevance scoring needed to prioritize semantic fit, not simple keyword overlap.
- Drafts needed enough role-specific structure to stay useful without collapsing into boilerplate.
Architecture
The pipeline has three layers:
1. Crawl4AI extraction of role pages into structured JSON.
2. Qdrant embeddings for semantic comparison against resume data.
3. Event-driven orchestration in n8n to trigger draft generation and review workflows.
OpenAI-powered drafting is gated so low-signal role matches do not trigger unnecessary output or waste review time.
Current state
This is best represented as a pilot workflow: the core architecture is real, but it still needs stronger evidence mapping and output discipline before it becomes a polished end-user tool.
Why it matters
The useful part is not just speed. It is creating a clearer application-ops layer where role discovery, fit scoring, and draft prep happen inside one reviewable system instead of six scattered manual steps.
Lessons
- Semantic pipelines need strong preprocessing to avoid garbage-in ranking.
- Automation should prioritize high-confidence opportunities over brute-force volume.
- Event logs and trace IDs make iterative tuning practical instead of anecdotal.
Key decisions
- Use semantic ranking before generation so low-signal roles do not trigger downstream drafting.
- Keep event-driven orchestration separate from matching logic so tuning does not require a full pipeline rewrite.
- Treat generated outreach as a reviewable draft, not an auto-send action.
What I'd improve next
The next improvement would be tighter evidence mapping between resume blocks and role requirements so the workflow can explain draft choices more clearly.