Product Context

OpsPilot sells AI workflow automation to mid-market and enterprise clients in logistics, insurance, and back-office operations. The product ingests tasks from a client’s existing systems, recommends next actions to human operators, and can optionally auto-execute low-risk actions through the client’s operational tools.

Scale

Task

Design the lifecycle and production architecture for deploying an AI decisioning system into a client's existing operational workflow.

Your design should address:

1. How you would clarify requirements and define success for both OpsPilot and the client, including human-in-the-loop vs full automation boundaries.
2. A multi-stage ML architecture for generating candidate actions, ranking them, and re-ranking or filtering them using business rules, compliance constraints, and client-specific policies.
3. The offline and online system design: data ingestion from client systems, feature computation, training, model deployment, online serving, feedback logging, and rollback paths.
4. How you would handle client heterogeneity: different schemas, sparse labels for new clients, cold start, and varying workflow volumes.
5. Your evaluation plan, including offline metrics, online experimentation, operational KPIs, and how you would detect feature drift, training-serving skew, and unsafe automation.
6. The top failure modes you expect during rollout and how the system should degrade safely.

Constraints

• Many clients require PII minimization, audit logs, and region-specific data residency.
• Some workflows are high-risk, so the system must support confidence thresholds and mandatory human approval.
• Client source systems are inconsistent; some provide streaming events, others only daily batch exports.
• Inference cost must stay below $0.002 per task on average.
• New policies or workflow rules may need to take effect within 15 minutes without full model retraining.