Assessment / edtech
Exam proctoring vision service
A computer-vision microservice that watches exam video for face presence and anomalies, called synchronously by the Rails assessment platform that owns the exam session.
Pipeline
- video frame
- face detection
- anomaly scoring
- flag session
01The brief
What made this hard.
The problem
Vision inference does not belong inside a Rails request cycle, and the Python ML ecosystem does not belong inside a Rails deployment.
The approach
The detection workload was split into a separate containerised service with a narrow HTTP contract. The Rails app stays the system of record for sessions and results; the vision service stays independently deployable and independently scalable.
02In production
What it actually does, day to day.
- ML workload isolated from the application deployment
- Independently scalable inference
- Narrow, testable service boundary
Stack
03More systems
Others we have shipped.
04Start here
Tell us what you are building. We will tell you what it takes.
Four short steps, then a real conversation with the engineer who would build it. No sales call, no discovery deck.
Rather just talk?
Grab 45 minutes. You will be on with an engineer, not a salesperson.
- No NDA needed to have the first conversation
- You keep the architecture note either way
- We will tell you if we are the wrong fit