Estimate infrastructure or support load created by voice assistant flow

Problem Statement Description

You are evaluating a new voice assistant flow used by healthcare coordinators to complete coordination tasks hands-free while moving between systems, calls, patient records, and clinical staff requests. The interview asks you to estimate the infrastructure load and/or support burden this flow could create at scale.

Focus on defining the scope clearly: who the healthcare coordinators are, what “hands-free completion” means in their daily workflow, how often they use the assistant, how long interactions last, and what backend services are invoked. You may estimate voice-processing demand, concurrent sessions, API calls, storage/logging volume, escalation volume, or support tickets—choose a coherent load definition and make it measurable.

Because this is a hard guesstimate, the strength of your answer will come from structured assumptions, transparent math, and sensitivity analysis rather than a single exact number. Healthcare context matters: reliability, privacy, accessibility, auditability, and error recovery can materially affect both infrastructure and support needs.

The experience should consider:

- The target population: number of healthcare coordinators, facilities, shifts, and active users per day.

- The unit of demand: sessions, minutes of audio, utterances, completed workflows, backend calls, or support contacts.

- Adoption and usage frequency across coordinator roles, shift patterns, task urgency, and hands-free scenarios.

- Voice flow complexity, including speech recognition, natural language understanding, authentication, confirmations, retries, and human handoff.

- Peak-load assumptions, concurrency, latency expectations, and regional or facility-level traffic spikes.

- Support-load drivers such as failed recognition, incorrect task completion, privacy concerns, training gaps, and exception handling.

- Guardrail assumptions around protected health information, audit logs, consent, security, and responsible AI behavior.

- Sanity checks and sensitivity ranges for best case, expected case, and high-load scenarios.

Your goal is to produce a defensible estimate that a product or infrastructure team could use for capacity planning, support staffing, or launch readiness decisions, while clearly showing the assumptions and factors that would most change the result.