Build Reliable Employee-Operations and Expense Intelligence Systems

Design four connected capabilities: a tagged event counter for dashboards and billing, a driver-pay ledger, an employee-termination orchestrator across external systems, and an LLM-backed expense assistant over employee and reimbursement records.

Constraints & Assumptions

• Mobile clients can upload a large offline backlog; events may be late or duplicated and cannot be silently lost.
• Dashboards tolerate tens of minutes of freshness delay, but monthly billing must reconcile exactly.
• Some termination actions are parallelizable, some have prerequisites, and some cannot be safely repeated.
• The assistant must enforce company and employee data boundaries before model access.

Clarifying Questions to Ask

• Which event fields form the billing identity and which are mutable tags?
• Does a pay-rate change affect only future work or also unsettled historical work?
• Which termination systems support idempotency, webhooks, or compensating actions?

Part 1 — Event counter

Design ingestion, deduplication, tagged aggregation, dashboard serving, and exact monthly billing for mobile and web SDK events.

What This Part Should Cover

• Durable acceptance and idempotency
• Event-time windows and late-data policy
• A reconciled billing ledger separate from approximate dashboards

Part 2 — Driver pay

Model drivers, rate history, completed jobs, overlapping time spans, total accrued cost, and paid versus unpaid amounts without rewriting history.

What This Part Should Cover

• Immutable work records and effective-dated rates
• Overlap and duplicate semantics
• Accrual, payment, and reconciliation APIs

Part 3 — Termination workflow

Orchestrate seven to ten dependent revocation and payout actions, minimize completion time, and resume automatically after interruption.

What This Part Should Cover

• A persisted DAG or saga state machine
• Idempotency and compensation per step
• Priority, retry, webhook, and operator escalation behavior

Part 4 — Expense assistant

Turn a natural-language expense question into safe structured operations over two large JSON-backed datasets, support aggregates and streaming, and explain when to use deterministic tools versus an agent.

What This Part Should Cover

• Tenant-scoped parsing and authorization
• Typed query plans and aggregate correctness
• Streaming UX without leaking unvalidated partial results