Why Invigilo chose SUPA as their data labeling partner


Scaling issues with internal labeling team resulting in tens of hours spent weekly manually reviewing and correcting annotated data.


SUPA BOLT’s built-in feedback loop mechanism was used to share instant feedback so errors could be corrected by annotators.


Annotation data accuracy improved by an estimated 15% through SUPA's quality assurance systems, review workflows and real-time collaboration.

Invigilo is Singapore’s leading AI video analytics solution provider for workplace safety. The company uses computer vision models in their video analytics software to enable real-time and continuous monitoring of critical events across different industry verticals such as construction sites, shipyards and manufacturing plants. Examples of this work include detecting people in close proximity to heavy machinery.

“Before using SUPA, we struggled with managing our internal data annotation processes. We’re now seeing better team morale as we are able to move faster and concentrate on model building and deployment.” — Vishnu Saran, CEO at Invigilo

Before SUPA, Invigilo relied on in-house data labelers to create and manage their labeled data. As a result, they were not able to reliably improve their label quality, efficiency and identify edge cases where their models were underperforming. Their team was managing all of their unstructured data through heavily manual workflows, spreadsheets and open-source labeling tools, which had scaling issues with quality and speed. This caused fatigue and took time away from mission-critical model development tasks.

To address these concerns, Invigilo sought a data labeling partner that could deliver quality annotation data with a fast turnaround time and real-time visibility on annotation data quality. SUPA was selected after a thorough evaluation of more than 10 options. The SUPA BOLT platform delivered high-quality annotations within a 24-hour turnaround time, and allowed Invigilo's ML team to review the annotation data as it was being completed by annotators. This real-time feedback mechanism ensured that quality benchmarks were met, and any issues were addressed immediately.

As a result, Invigilo’s team was able to immediately see a 80% gain in annotation delivery speed and a 15% increase in annotation quality. In the future, Invigilo will be looking to enhance its ML-powered video analytics offerings with more capabilities such as automated incident reporting. 

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