CASE STUDY 10

Agentic AI Workflow for Job Sourcing and Filtering

Agentic workflow that sources internship and job listings, filters by visa requirements, and prepares semi-automated applications.

Industry: Career services / EdTech Timeline: 5 weeks AI AgentsMulti-AgentWeb Scraping
Agentic AI Workflow for Job Sourcing and Filtering — architecture diagram
SYSTEM ARCHITECTURE

The pain

A student-focused career services product needed to surface internship listings matching very specific criteria (visa sponsorship, internship-only, posted within 7 days, specific role types).

The existing approach was manual filtering through job boards. Students spent 5+ hours per week sorting through irrelevant postings before they could even apply.

The team needed an agentic system that sources from multiple boards and company career pages, then aggressively filters using LLM reasoning about visa sponsorship language and role suitability.

What I built

Browser agents

Playwright crawlers for LinkedIn, Indeed, Glassdoor, Workday, Greenhouse, Lever.

LLM filter

GPT-4o evaluates each posting against strict criteria.

Visa rules engine

Structured logic with LLM fallback for ambiguous postings.

Notion shortlist

Populated daily with passing roles, ranked by match quality.

Daily digest email

New matches with one-line summaries.

Application-draft generator

Tailored cover letter per role.

Human approval flow

Students review every application before send.

Outcome

30min/wk
Student review time, down from 5h
11%
Application-to-response, up from 4%
1,200/wk
Postings processed
96%
Filter precision

Stack

n8nPythonPlaywrightMake.comLangChainCrewAIGPT-4oNotionAirtableGoogle Sheets
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