Problems / AI products, medium

Medical scribe: redaction bottleneck

A medical scribe transcribes visits, redacts PII and drafts notes. The redactor has 1 vCPU and 20 ms per note, at 100 notes a second.

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Your goals

Success ≥ 99%, p99 ≤ 5 s.

Test cases

Like a coding problem, a solution has to pass every case, not just the one it was built for. Submit in the lab runs all 4; 1 of them are hidden until then.

  1. As given. The problem’s own traffic, scheduled failures and seed.
  2. A slow dependency. The main data store or model answers twice as slowly for part of the run.
  3. A server dies. One replica of the service with the most replicas is killed for part of the run.

The traffic

100 requests a second for 30 seconds: 100% request (reads).

The system

What each component does in this design, and what calls it. The simulator mimics each one from these settings.

ComponentIts job hereSettings
Load balancerLoad balancerTraffic enters here.Calls AI app.Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header.–
AI appApp serviceLoad balancer forwards to it.Calls Speech to text, PII redactor, GPU serving pool.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s.3 replicas × 500 slots · timeout 30 s
Speech to textSpeech to textAI app calls it.Streaming speech recognition returns partial words while the user is still talking.400 slots · 6,000 calls/min
PII redactorPII redactorAI app calls it.Redacting personal data before it reaches a model or a log reduces privacy and compliance risk.100 slots
GPU serving poolSelf-hosted serving poolAI app calls it.GPU servers batch requests together; throughput rises with batch size until memory runs out.400 slots

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