As online examinations grow globally, EdTech platforms need accurate, scalable, and cost-efficient AI proctoring systems. This case study shows how InteligenAI helped a US-based exam proctoring platform replace generic vision APIs with a fully custom AI-based remote proctoring solution—designed to improve detection accuracy, reduce operational cost, and eliminate manual review bottlenecks.
This work demonstrates InteligenAI’s expertise in AI for EdTech, AI-powered cheating detection, computer vision pipelines for exam monitoring, and end-to-end ML deployment.
Client challenge
A leading US remote proctoring provider relied on third-party cloud vision APIs to detect cheating activities, monitor candidate behavior, and identify prohibited objects.
However, they faced critical scalability and accuracy issues:
1. High cost of off-the-shelf vision APIs
Pay-per-detection pricing made large exam volumes expensive and unpredictable. The client needed a low-cost, high-accuracy AI proctoring alternative they could fully own.
2. Inconsistent detection accuracy
Generic models struggled in real exam conditions:
- Low-light rooms
- Poor webcams
- Moving candidates
- Small or partially visible devices like phones or smartwatches
This led to missed violations and false positives.
3. Heavy reliance on manual review
Human reviewers had to validate most sessions, creating:
- Slow turnaround
- Subjective judgement
- Operational fatigue
- Inability to scale during peak exam seasons
The client needed a domain-specific AI exam proctoring model that could consistently detect violations and eliminate dependency on cloud providers.
Our AI solution
InteligenAI built a custom AI-based proctoring system designed specifically for real-world remote exam environments.
You can explore more of our work here
1. Custom CV detection pipeline
We designed and trained an object detection model optimized for:
- Candidate behavior monitoring
- Cheating pattern recognition
- Detection of prohibited items (phones, watches, extra devices)
- Multiple participants in frame
The system achieved higher accuracy than generic cloud APIs and eliminated recurring per-call fees.
2. AI assisted data labeling & MLOps pipeline
To scale training data efficiently, we implemented a hybrid pipeline:
- AI auto-labeling to accelerate dataset creation
- Human verification loops for edge cases
- Automated model retraining when new exam scenarios appear
- Model versioning, drift monitoring, and A/B evaluations
This ensured the proctoring model continuously improved over time.
3. Scalable, real-time inference service
We deployed a containerized inference stack using:
- GPU/CPU auto-scaling
- Low-latency real-time detection API
- Full control over updates and rollouts
- Cost-predictable infrastructure
The platform can now process thousands of exam sessions concurrently with stable performance.
Impact & Results
Within weeks of deployment, the client saw measurable improvements:
- 60–75% reduction in AI operating costs
- Higher detection accuracy in low-light and multi-participant scenarios
- Significant reduction in manual reviews
- Consistent and auditable detection results
- Scalable infrastructure ready for peak exam seasons
- Full ownership of the AI proctoring technology
This positioned them as a more reliable, cost-efficient proctoring provider in a highly competitive EdTech market.
If you run an EdTech platform that needs custom AI proctoring, cheating detection, or computer vision monitoring, this case study highlights why custom-built AI outperforms generic APIs:
- Better accuracy in real exam settings
- Lower long-term costs
- Faster detection
- Less human review
- Full control over data, models, and updates
As the demand for secure, reliable, and scalable online examinations grows, EdTech companies can no longer rely on generic vision APIs or manual review teams. This project shows how a custom AI proctoring solution built with domain-specific computer vision, automated data pipelines, and real-time inference can dramatically improve accuracy while reducing operational costs.
For any platform looking to modernize its remote exam monitoring, this case study proves a simple truth:
Own your AI, tailor it to your environment, and your proctoring system becomes smarter, faster, and far more scalable.
InteligenAI helps EdTech businesses achieve exactly that.