Identify Defects,
Improve Quality and Cut Costs
Trusted by leading AI and data science teams
SOLUTION
Computer vision for visionaries
Product & Asset Inspection
For manufacturers & equipment operators
- Catch non-obvious defects that manual inspections can miss
- Improve batch quality and ensure equipment integrity
- Avoid costly recalls and other reputation risks
Visual Data Modeling & Labeling
For any image processing application
- Fast-track model development while improving quality
- Analyze model performance and correct mispredictions
- Boost model accuracy with high-quality synthetic data
WHO WE SERVE
Manufacturers and Data Science
teams everywhere
Medical Device & Pharma
Medical Device & Pharma
Railway
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Food & Agriculture
Food & Agriculture
Critical Infrastructure
Automotive
Other
enhance your detection efforts.
Who You Are
In-Line Manager
Process Engineers
VP of Quality
Data Science Teams
Machine Vision Teams
OUR APPROACH
How Akridata Improves Your
Vision System
Traditional Approach
- Getting connected to a new visual data platform takes weeks and requires moving or copying your data outside of your environment.
- Visual data labeling is slow, labor-intensive, costly, and riddled with human error.
- Models report many false positives and negatives, resulting in lower yields and defective product shipments.
- System has a rigid configuration with limited support for your unique processes or use cases.
Akridata’s Approach
- Rapid Onboarding: Simply link to your data source and start building your model right away with our no-code interactive interface.
- Automated Labeling: Spend 80% less on labeling with our proprietary AI models that label visual data quickly and accurately.
- Deep Learning: Our intelligence layer continually refines model performance to improve inspection decisions and eliminate escapes.
- Fully Customizable: Configure our platform as needed to support virtually any computer vision workflow.
This Medical Device Company Decreased False Positives by 40%
The company’s in-line inspection system, efficient but inaccurate, fell short despite repeated model tuning. Real-world production conditions and a rigid, speed-focused approach caused incorrect part flagging, posing a challenge in balancing speed without sacrificing accuracy or risking defective product shipments.
This medical device company implemented Akridata to boost accuracy and efficiency in assembly line inspections.
The company’s in-line inspection system, efficient but inaccurate, fell short despite repeated model tuning. Real-world production conditions and a rigid, speed-focused approach caused incorrect part flagging, posing a challenge in balancing speed without sacrificing accuracy or risking defective product shipments.
This medical device company implemented Akridata to boost accuracy and efficiency in assembly line inspections.
This improvement significantly reduced inventory wastage and prevented the shipment of defective products.
The implementation of Akridata's solution elevated product quality standards, safeguarding the company's esteemed brand reputation.
Leveraging Akridata's Data Explorer, the pharmaceutical company achieved a notable 40% decrease in false positives and uncovered a 30% reduction in false negatives.
Improved operational accuracy efficiency and reduced recalls while cutting costs.
RESOURCES
Looking Ahead to 2024: AI’s Effect On The Industry
Read Akridata’s predictions for the computer vision industry in 2024.
AI for Visual Data: Computer Vision in Business
Learn how computer vision can help companies of all kinds, from security to manufacturing.
Automatic Image Quality Analysis
Discover how Akridata ensures models are built using clean, high-quality datasets.
RESOURCES
Looking Ahead to 2024: AI’s Effect On The Industry
Read Akridata’s predictions for the computer vision industry in 2024.
AI for Visual Data: Computer Vision in Business
Learn how computer vision can help companies of all kinds, from security to manufacturing.
Automatic Image Quality Analysis
Discover how Akridata ensures models are built using clean, high-quality datasets.