Can AI replace Rippling?

Build a closest honest personal substitute for Rippling in an empty repository.
Use Next.js 15, TypeScript, PostgreSQL, Drizzle ORM, and Docker Compose; do not offer alternative stacks.
The core loop is: manage a limited employee directory and onboarding checklist, maintain approvals and records, and export data without calculating payroll, controlling devices, or making employment decisions.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Implement employees, teams, roles, documents, leave requests, equipment, onboarding tasks, and acknowledgements.
Use least-privilege roles and log every read or change to sensitive records.
Add approval workflows, expiry reminders, export, retention, deletion holds, and encrypted backups.
Keep compensation fields optional and never calculate payroll, taxes, benefits, or statutory filings.
Provide a data dictionary and explain which fields should not be stored without legal review.
Ship Docker Compose, health checks, disaster recovery, and an emergency-access procedure.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out payroll calculation or money movement.
Deliberately leave out automated hiring decisions or applicant scoring.
Deliberately leave out tax, benefits, immigration, identity, and global-employment compliance.
Finish by running the tests and listing the exact commands used.