Evidence-Grounded AI Workflows
JobsAI brings opportunity research, résumé-grounded analysis, and follow-through into one private job-search workflow, with human review at each decision point.
Challenge
Managing a job search involves more than collecting listings. Role requirements, résumé evidence, company research, networking activity, interviews, and next steps need to stay connected, while AI recommendations must remain traceable to source material.
Workflow
- Collect and organize: Gather opportunities from configured public employer boards, job APIs, and selected search sources in a PostgreSQL-backed pipeline.
- Ground analysis: Retrieve relevant résumé and opportunity-document evidence for role matching and tailored materials, so recommendations can point back to their support.
- Review and plan: Use a Flask dashboard to manage workflow stages, company and contact research, networking interactions, interviews, calendar events, and daily planning.
- Assist through MCP: Expose selected search, CRM, scoring, scheduling, and application-preparation workflows to compatible assistants.
Design principles
Deterministic scoring compares the complete résumé with saved career goals. Optional AI enrichment structures posting requirements and fit notes. Qualification stays tied to résumé evidence, while preferences and workflow activity remain distinct, making recommendations easier to inspect and correct.
Messaging workflows prepare and track outreach; messages are sent personally. AI supports preparation and organization while the user retains control of external actions.
Deployment and privacy
JobsAI is a private project and is not publicly available. Its production environment runs on a NAS, with OCI and AWS standby environments. PostgreSQL data is replicated to the standbys; retrieval and other runtime data have separate recovery requirements. This summary describes the workflow at a high level and omits private implementation and deployment details.