Manufacturing

Improve, automate and control manufacturing processes with computer vision applications

Computer Vision in Manufacturing

Computer Vision is advancing the manufacturing industry with growing adoption of applications like defect detection, process monitoring and accident prevention. SUPA enables ML teams to get labeled data fast, accelerating model building and driving business value.

What our clients say

“What surprised me is the amount of insight I could gather with smaller batches of data - not only did I discover more edge cases, I could also quickly change up my instructions and launch a revised batch within hours. I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch.”

Sravan Bhagavatula
Director of Computer Vision

“Our team members were able to smash our goals quicker than we did before”

Annabelle Porter,
Customer Service Officer

“Our team members were able to smash our goals quicker than we did before”

Annabelle Porter,
Customer Service Officer

Manufacturing use cases

Supply chain optimization

  • Detect and act on inconsistent supplier quality levels
  • Forecast the demand of parts to prevent stock outs
  • Ensure consistent lead times for orders

Anomaly detection

  • Filter and find rare occurrences in a data pipeline
  • Common domains include financial fraud, irregularities in time series analysis, fault detection and system health monitoring in sensor networks

Safety detection

  • Track the progress of the assembly line
  • Analyse and prevent workplace accidents in real time with vision-guided robots
  • Detect safety violations

Supply chain optimization

  • Detect and act on inconsistent supplier quality levels
  • Forecast the demand of parts to prevent stock outs
  • Ensure consistent lead times for orders

Anomaly detection

  • Filter and find rare occurrences in a data pipeline
  • Common domains include financial fraud, irregularities in time series analysis, fault detection and system health monitoring in sensor networks

Other manufacturing use cases

Product quality inspection

Automate the detection of defects on the assembly line. Machines help to assess complex surfaces and detecting cosmetic defects, reducing the chances of human error in QC.

Predictive maintenance

Using computer vision to notify your maintenance team of the stress on specific machinery. Predictive analytics will flag issues so you can avoid production downtime.

Thermal imaging

Recognize material types through temperature patterns, enhanced security surveillance and automatic electrical inspection of manufacturing lines.  Detect overheating and overloading.

Bryce Wilson
Data Engineer at Black.ai

Consistent support

If there's one thing that makes SUPA stand out, it's their commitment to providing consistent support throughout the data labeling process. The team actively and efficiently engaged with us to ensure any ambiguity in the dataset was cleared up.

Jonas Olausson
Data Engineer at Black AI
The best interface for self-service labeling.

Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.

Sravan Bhagavatula
Director of Computer Vision at Greyscale AI
Launch a revised batch within hours

I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.

Sparsh Shankar
Associate ML Engineer at Sprinklr
Really quick

The annotators were really quick. I would upload and 5 minutes later - 10 images done. I checked 5 minutes later - 100 images done.

Puneet Garg
Head of Data Science at Carousell
Good quality judgments

The team at [SUPA] has been very professional & easy to work with since we started our collaboration in 2019. They've provided us with good quality judgments to train, tune, and validate our Search & Recommendations models.

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