Sample use cases

AI is transforming industries from retail to healthcare. It all starts with labeled training data.

  • Use case

  • Gastroenterology

Gastrointestinal endoscopy

AI in gastrointestinal endoscopy is one of the most advanced in medicine. Medical device companies use Cord to detect malignant polyps and conditions such as inflammatory bowel disease.

  • Use case

  • Radiology

Radiology

AI in radiology is used in a myriad of applications in areas such as brain imaging, mammography, and thrombolysis. Researchers use Cord's advanced DICOM viewing and annotation features to build AI that improves patient outcomes and save lives.

  • Use case

  • Thermal imaging

Thermal imaging

Thermal imaging is critical in search and rescue operations where time is against you, and lives may be at stake. Rescue personnel can get fatigued observing thermal images for hours on end. AI boosts their effectiveness and relieves fatigue.

  • Use case

  • Retail

Retail

Retailers are using AI to automate checkouts, locate items, gather intelligence on purchasing behaviour, and much more. Cord's support for complex label structures makes it possible for retail companies to build complex AI solutions.

  • Use case

  • Drone

Drone surveillance

Drones are used across a broad set of domains. AI-powered drones can help automate time-consuming and expensive tasks such as cattle surveillance, building inspection, and crop management.

  • Use case

  • Input here

Your use case

AI can be used to solve hard problems across many different use cases in a multitude of different domains. Cord can help you accelerate your AI journey, achieve your goals, and increase your ROI.

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Speak to a member of our team to learn how you can iterate faster.

Sample case studies

We work with some of the world's leading companies, hospitals, and research institutions.

Stanford Medicine
Stanford Medicine

The Division of Nephrology reduced experiment duration by 80% while processing 3x more images.

Problem
Stanford was using three different pieces of software to identify, annotate, and count podocytes and glomeruli in microscopy images.
Solution
Stanford started using Cord’s training data platform & SDK to automate segmentations, count, and calculate sizes of segments.
Results
With Cord, Stanford researchers reduced experiment duration from an average of 21 to 4 days while processing 3x the number of images.
80%
Reduction in experiment duration
3X
Number of images
1 platform
... and not 3
KCL Logo
King's College London

KCL used Cord to achieve a 6.4x average increase in labelling efficiency for GI videos.

Problem
Using clinicians to annotate pre-cancerous polyp videos had prohibitively high costs to produce large datasets.
Solution
Deployed Cord’s micro-model module to increase clinician labelling efficiency and automate 97% of produced labels.
Results
Highest expense clinician saw 16x labelling efficiency improvement. Cut model development time from 1 year to 2 months.
6.4X
Faster than manual labelling
97%
Automated labels
6X
Faster to AI in production
Undraw surveillance
Human behaviour AI company

The company built custom label structures with Cord’s adaptive ontology & automated workflows with Cord’s SDK.

Problem
Complex annotation tasks rendered existing & open-source tools unusable. GDPR & privacy restrictions prohibited use of managed service.
Solution
Utilised Cord to build custom structure, integrate to their cloud for compliance, operate tracking modules to automate annotations.
Results
Built the most sophisticated human behaviour datasets on the planet. Automation features made in-house workforce practical.
GDPR & Privacy
Compliant
Custom label structures
Flexible ontology
97%
Automated labels
KCL Logo
Leading restaurant automation provider

The automation provider tracked objects across different views & replaced 37 annotation hours with 1.

Problem
Tracking objects across different views coupled with changing weather & lighting conditions led to annotation inconsistencies & high costs.
Solution
Deployed Cord’s micro-model & interpolation modules to track objects across different views, enforce consistency, & increase labelling efficiency.
Results
37x increase in labelling efficiency. Annotation accuracy increased from 94% to 99%.
37X
Faster than manual labelling
4pp
Accuracy boost
99%
Annotation accuracy