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Operationalizing AI.

DigitSquare powered by Digit7 is an automated annotation tool that uses computer vision applications like recognizing objects, analyzing behavior, and recognizing faces. AI implementations run into problems with data, which is a key part of AI initiatives. Especially because for Computer Vision to work, it needs to see digital images over and over again so that it can correctly identify real things in real time from a live feed. DigitSquare can take in a lot of data and annotate it quickly and accurately in less time.
It takes about 1M images of the same item in order for a computer vision product to reach 99% accuracy in identifying products in real time.

Customers are concerned about privacy and security.

Concerns about privacy and safety are addressed by our 3D modeling data. DigitSquare powered by Digit7's computer vision does not use facial recognition to figure out who your customers are. DigitSquare instead makes unique user profiles based on your customers' unique geometric and dimensional data, which is affected by things like their clothing and physical traits. Their personal information is not kept after they are done shopping.
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It is essential for you to understand your customers.

These one-of-a-kind identifiers are essential in assisting DigitSquare in not only identifying the things that your customers are picking, but also in tying those products back to the customer's personal basket in a fresh and innovative way. Customers are able to make better-informed purchases because to the 3D product data models provided by DigitSquare. This will create higher customer engagement and retention, in addition to building brand awareness, which will ultimately result in an increase in your income.

Core Features

Model generation & confidence score of 98%​
Model prediction speed 10ms - 1s​
4x – 6x Faster than manual labelling​
6x – 8x Cost saving​

Industries - Actionable Solutions
across Industries

Industries 1
01. Autonomous Vehicles
Industries 2
02. Agriculture
Industries 3
03. Security & Surveillance
Industries 3
04. Manufacturing
Industries 1
05. Insurance
Industries 2
06. Medical Imaging
Industries 3
07. Sports
Industries 3
08. Retail Automation

Why choose DigitSquare?

Why does DigitSquare matter?

DigitSquare is an end-to-end Saas based platform for annotating, training, and automating the computer vision pipeline.

Are we saving the customer – time? Money? People resources?

We are saving time, money and human effort in terms of annotating. 

What is Annotation/Labeling?

Annotation is the technique through which we label data so as to make objects recognizable by machines. 

Different types of labeling DigitSquare supports?

Rectangle, Polygon, Point and Line.

What all the industries you serve?

Any industry requirement specific to Image/Video processing use-cases.

Is DigitSquare a free tool?

Not a free tool, licensed tool. 

Installation required or we can open in browser?

Web Browser 

How quick/fast and accurate?

Depends on the Training data set and accurate annotations. 

DigitSquare subscription cost?

  • Basic -> $10,000 
  • Advanced -> $20,000 
  • Enterprise -> $30,000 
  • Additional cost for Auto Label/Model Generation Hours -> depending on the usage or requirement needs. 

Is coding required or anyone can access?

Tool can be accessed only if the license is purchased, Coding is not required, this tool is built for ML Engineers. 

How is my data handled on DigitSquare?

DigitSquare complies to GDPR.

How it is used to build AI models?

  • Using annotated data sets, if the data is not annotated as well, there is an option to auto label, set hyperparameters and generate models. 
  • Models generated using globally available standard formats. 

How machine learning and deep learning works for image annotation.

  • Refer the same Answers as above. 
  • Using annotated data sets, if the data is not annotated as well, there is an option to auto label, set hyper parameters and generate models. 
  • Models generated using globally available standard formats. (Pytorch, TensorFlow, ONNX etc). 

What is synthetic data? Adv/dis adv of synthetic data? Types

  • Synthetic Image generation is the creation of artificially generated images that look as realistic as real images.  
  • ML Engineers often require highly quantitative accurate, and diverse datasets to train and build accurate ML models.  
  • Synthetic data helps in reducing the costs of data collection and data labelling. In addition to lowering costs,  
  • synthetic raw data helps address privacy issues associated with sensitive real-world data. 
  • Types of Synthetic Data 
    • Text data: Synthetic data can be artificially generated text in natural language processing (NLP) applications. 
    • Tabular data: Tabular synthetic data refers to artificially generated data like real-life data logs or tables useful for classification or regression tasks. 
    • Media: Synthetic data can also be synthetic video, image, or sound to be used in computer vision applications. 
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