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Use Cases

With great power comes great responsibility

Customized product offering

By using face recognition algorithms, you can suggest the best fitting product at individual customer level.

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Inventory Management

Use simulation technologies powered by ML to analyse and estimate inventory data real time.

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Product Recommendations

Improve your recommendation engine and increase revenue & user engagement.

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Search Engine Improvement

With the help of AI, you can better tag and surface your inventory, and improve the user funnel and conversion rates.

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Product Image Categorization

Categorize your product inventory in a scalable way, based on pre-defined tags or categories.

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Review Categorization

Analyze your customer reviews in a scalable way and drive useful insights for your product offering, pricing etc.

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Vendor Quality Assurance

Ensure vendor quality with automated checks of inventory details provided.

‍

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Image transcription

Automate extraction of product details and characteristics from inventory images.

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Download our 3 pager to find out more about:

Challenges that retail companies have
How AI can help your retail business be automated
More use cases where AI can help you
The benefits of the Super.AI solution
Additional customer case studies
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Case Studies

You are in great company

Retail & e-Commerce

eCommerce Analytics platform builds neural network with super.AI

Our client is an eCommerce Analytics platform. With tens of thousands of data points, the company was looking for a scalable and fast way to tag its data sets. The company started building a dataset with 2 internal resources dedicated to labeling but wanted to improve the latency while ensuring the quality of the output tags. They realized the super.AI solution can help them achieve both objectives.

Using super.AI, we were able to significantly accelerate our labeling efforts. We were worried that increasing the automation would reduce the overall quality of the dataset, but super.AI was able to deliver human quality at AI speeds. We’ve gotten almost perfect accuracy.
Data Analyst
Retail & e-Commerce

E-commerce giant builds product search enhancement and recommendation engine with super.AI

The company, a leading retail & classifieds global group, reached out to super.AI to get help with cleaning up its user-generated product tags, with the ultimate goal to enhance the product search and the recommendation engine. We worked with them on a consistent product taxonomy and tagging categories, including for subjective tags such as style, and then labeled hundreds of thousands of images within a record timeframe. The client is now able to offer much more granular product results and recommendations to its user base and ultimately increase engagement and revenue.

We had a lot of user-generated content and were struggling to make sense of it given the lack of consistency in user-generated tags. Our product recommendation engine was limited in its functionality because of this. Using the super.AI platform has allowed us to define a consistent taxonomy, label hundreds of thousands of images in record time, and significantly enhance our recommendation engine.
Data Scientist
Retail & e-Commerce

Large US retailer builds product recommendation engine with the help of super.AI

One of the largest retailers in the US reached out to super.AI to help them create a product taxonomy to be used for the retailer's recommendation engine. The retailer had a large wealth of data, mainly provided by external vendors, and was struggling to ensure consistency between the various data inputs, as well as capturing relevant insights from the product details in order to develop its product recommender. Thanks to our work, they were able to launch their recommendation engine within a few weeks.

We got insights into our product content that we’ve never been able to get before. As soon as I saw the first report during the POC I realized I would make a very compelling presentation to leadership 3 levels above me
Senior Product Content Manager

How it works

Simple to use APIs, labeling tools, AI models with the click of a button

CONVERSION RATE OPTIMIZATION
Product Image Categorization
Product Image Categorization
Review Categorization
Review Categorization
Vendor Quality Assurance
Vendor Quality Assurance
Image Transcription
Image Transcription
Input
OUTPUT
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

dp = DataProgram("image-tagging")
instance = dp(name="my-image-tagging", instructions="Select the most appropriate tag(s) for the content of the image")
instance.label(inputs=["https://cdn.super.ai/Webflow+assets/lee-campbell-qNPESem_t4I-unsplash.jpg"])

curl -X POST \
"https://super.ai/v1/apps/my-image-tagging/job" \
-H "API-KEY: live_t9wHIzxjuGSuKTx2jWQTzgXehu1uvkK27QM3tzm6f1g" \
-H "Content-Type: application/json" \
-d '{"inputs":["https://cdn.super.ai/Webflow+assets/lee-campbell-qNPESem_t4I-unsplash.jpg"]}'

superai client create_jobs \
--app_id my-image-tagging \
--inputs ["https://cdn.super.ai/Webflow+assets/lee-campbell-qNPESem_t4I-unsplash.jpg"]

{
 "label": {
   "choices": [
     {
       "tag": "0",
       "value": "bag"
     },
     {
       "tag": "1",
       "value": "dress"
     },
     {
       "tag": "2",
       "value": "jeans"
     },
     {
       "tag": "3",
       "value": "lifestyle"
     },
     {
       "tag": "4",
       "value": "sports"
     },
     {
       "tag": "5",
       "value": "business"
     }
   ],
   "selections": [
     {
       "tag": "0",
       "value": "bag"
     },
     {
       "tag": "2",
       "value": "jeans"
     },
     {
       "tag": "3",
       "value": "lifestyle"
     }
   ]
 }

{
 "label": {
   "choices": [
     {
       "tag": "0",
       "value": "bag"
     },
     {
       "tag": "1",
       "value": "dress"
     },
     {
       "tag": "2",
       "value": "jeans"
     },
     {
       "tag": "3",
       "value": "lifestyle"
     },
     {
       "tag": "4",
       "value": "sports"
     },
     {
       "tag": "5",
       "value": "business"
     }
   ],
   "selections": [
     {
       "tag": "0",
       "value": "bag"
     },
     {
       "tag": "2",
       "value": "jeans"
     },
     {
       "tag": "3",
       "value": "lifestyle"
     }
   ]
 }

{
 "label": {
   "choices": [
     {
       "tag": "0",
       "value": "bag"
     },
     {
       "tag": "1",
       "value": "dress"
     },
     {
       "tag": "2",
       "value": "jeans"
     },
     {
       "tag": "3",
       "value": "lifestyle"
     },
     {
       "tag": "4",
       "value": "sports"
     },
     {
       "tag": "5",
       "value": "business"
     }
   ],
   "selections": [
     {
       "tag": "0",
       "value": "bag"
     },
     {
       "tag": "2",
       "value": "jeans"
     },
     {
       "tag": "3",
       "value": "lifestyle"
     }
   ]
 }

Run Data program
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Input
OUTPUT
Product Image Categorization
Input
OUTPUT
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

dp = DataProgram("text-tagging")
instance = dp(name="my-text-tagging", instructions="Select the most appropriate tag(s) for the content of the text",
instance.label(inputs=["I loved the color and I think it looks fabulous, but the fit was totally wrong so I couldn't really wear it"])

curl -X POST \
"https://super.ai/v1/apps/my-text-tagging/job" \
-H "API-KEY: live_t9wHIzxjuGSuKTx2jWQTzgXehu1uvkK27QM3tzm6f1g" \
-H "Content-Type: application/json" \
-d '{"inputs":["I loved the color and I think it looks fabulous, but the fit was totally wrong so I couldn't really wear it"]}'

superai client create_jobs \
--app_id my-text-tagging \
--inputs ["I loved the color and I think it looks fabulous, but the fit was totally wrong so I couldn't really wear it"]

{
 "label": {
   "choices": [
     {
       "tag": "0",
       "value": "highly rated aesthetic"
     },
     {
       "tag": "1",
       "value": "highly rated fit"
     },
     {
       "tag": "2",
       "value": "highly rated quality"
     },
     {
       "tag": "3",
       "value": "average rated aesthetic"
     },
     {
       "tag": "4",
       "value": "average rated fit"
     },
     {
       "tag": "5",
       "value": "average rated quality"
     },
     {
       "tag": "6",
       "value": "poorly rated aesthetic"
     },
     {
       "tag": "7",
       "value": "poorly rated fit"
     },
     {
       "tag": "8",
       "value": "poorly rated quality"
     }
   ],
   "selections": [
     {
       "tag": "0",
       "value": "highly rated aesthetic"
     },
     {
       "tag": "7",
       "value": "poorly rated fit"
     }
   ]
 }

{
 "label": {
   "choices": [
     {
       "tag": "0",
       "value": "highly rated aesthetic"
     },
     {
       "tag": "1",
       "value": "highly rated fit"
     },
     {
       "tag": "2",
       "value": "highly rated quality"
     },
     {
       "tag": "3",
       "value": "average rated aesthetic"
     },
     {
       "tag": "4",
       "value": "average rated fit"
     },
     {
       "tag": "5",
       "value": "average rated quality"
     },
     {
       "tag": "6",
       "value": "poorly rated aesthetic"
     },
     {
       "tag": "7",
       "value": "poorly rated fit"
     },
     {
       "tag": "8",
       "value": "poorly rated quality"
     }
   ],
   "selections": [
     {
       "tag": "0",
       "value": "highly rated aesthetic"
     },
     {
       "tag": "7",
       "value": "poorly rated fit"
     }
   ]
 }

{
 "label": {
   "choices": [
     {
       "tag": "0",
       "value": "highly rated aesthetic"
     },
     {
       "tag": "1",
       "value": "highly rated fit"
     },
     {
       "tag": "2",
       "value": "highly rated quality"
     },
     {
       "tag": "3",
       "value": "average rated aesthetic"
     },
     {
       "tag": "4",
       "value": "average rated fit"
     },
     {
       "tag": "5",
       "value": "average rated quality"
     },
     {
       "tag": "6",
       "value": "poorly rated aesthetic"
     },
     {
       "tag": "7",
       "value": "poorly rated fit"
     },
     {
       "tag": "8",
       "value": "poorly rated quality"
     }
   ],
   "selections": [
     {
       "tag": "0",
       "value": "highly rated aesthetic"
     },
     {
       "tag": "7",
       "value": "poorly rated fit"
     }
   ]
 }

Run Data program
Go Back
Learn More
Input
OUTPUT
Review Categorization
Input
OUTPUT
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

dp = DataProgram("image-tagging")
instance = dp(name="my-image-tagging", instructions="Select the most appropriate tag(s) for the content of the images")
instance.label(inputs=["https://cdn.super.ai/Webflow+assets/Vendor+Quality+Input.jpg"])

curl -X POST \
"https://super.ai/v1/apps/my-image-tagging/job" \
-H "API-KEY: live_t9wHIzxjuGSuKTx2jWQTzgXehu1uvkK27QM3tzm6f1g" \
-H "Content-Type: application/json" \
-d '{"inputs":["https://cdn.super.ai/Webflow+assets/Vendor+Quality+Input.jpg"]}'

superai client create_jobs \
--app_id my-image-tagging \
--inputs ["https://cdn.super.ai/Webflow+assets/Vendor+Quality+Input.jpg"]

{
 "label": {
   "choices": [
     {
       "tag": "0",
       "value": "images are varied"
     },
     {
       "tag": "1",
       "value": "images show front and back"
     },
     {
       "tag": "2",
       "value": "image is accurate to color"
     }
   ],
   "selections": [
     {
       "tag": "0",
       "value": "images are varied"
     },
     {
       "tag": "1",
       "value": "images show front and back"
     },
     {
       "tag": "2",
       "value": "image is accurate to color"
     }
   ]
 }

{
 "label": {
   "choices": [
     {
       "tag": "0",
       "value": "images are varied"
     },
     {
       "tag": "1",
       "value": "images show front and back"
     },
     {
       "tag": "2",
       "value": "image is accurate to color"
     }
   ],
   "selections": [
     {
       "tag": "0",
       "value": "images are varied"
     },
     {
       "tag": "1",
       "value": "images show front and back"
     },
     {
       "tag": "2",
       "value": "image is accurate to color"
     }
   ]
 }

{
 "label": {
   "choices": [
     {
       "tag": "0",
       "value": "images are varied"
     },
     {
       "tag": "1",
       "value": "images show front and back"
     },
     {
       "tag": "2",
       "value": "image is accurate to color"
     }
   ],
   "selections": [
     {
       "tag": "0",
       "value": "images are varied"
     },
     {
       "tag": "1",
       "value": "images show front and back"
     },
     {
       "tag": "2",
       "value": "image is accurate to color"
     }
   ]
 }

Run Data program
Go Back
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Input
OUTPUT
Vendor Quality Assurance
Input
OUTPUT
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

dp = DataProgram("image-structured")
instance = dp(name="my-image-structured", instructions="Transcribe the dimensions of the product in the image and choose the appropriate tags.")
instance.label(inputs=["https://cdn.super.ai/Webflow+assets/image+transcription+input.png"])

curl -X POST \
"https://super.ai/v1/apps/my-image-structured/job" \
-H "API-KEY: live_t9wHIzxjuGSuKTx2jWQTzgXehu1uvkK27QM3tzm6f1g" \
-H "Content-Type: application/json" \
-d '{"inputs":["https://cdn.super.ai/Webflow+assets/image+transcription+input.png"]}'

superai client create_jobs \
--app_id my-image-structured \
--inputs ["https://cdn.super.ai/Webflow+assets/image+transcription+input.png"]

{
 "label": [
   {
     "field_id": "Width",
     "field_type": "Float",
     "field_value": "3.9"
   },
   {
     "field_id": "Height",
     "field_type": "Float",
     "field_value": "5.5"
   },
   {
     "field_id": "Depth",
     "field_type": "Float",
     "field_value": "na"
   }
   ],
   "choices": [
     {
       "tag": "0",
       "value": "missing width"
     },
     {
       "tag": "1",
       "value": "missing height"
     },
     {
       "tag": "2",
       "value": "missing depth"
     }
     ]
    "selections": [
     {
       "tag": "2",
       "value": "missing depth"
     }
   ]
 }
}

{
 "label": [
   {
     "field_id": "Width",
     "field_type": "Float",
     "field_value": "3.9"
   },
   {
     "field_id": "Height",
     "field_type": "Float",
     "field_value": "5.5"
   },
   {
     "field_id": "Depth",
     "field_type": "Float",
     "field_value": "na"
   }
   ],
   "choices": [
     {
       "tag": "0",
       "value": "missing width"
     },
     {
       "tag": "1",
       "value": "missing height"
     },
     {
       "tag": "2",
       "value": "missing depth"
     }
     ]
    "selections": [
     {
       "tag": "2",
       "value": "missing depth"
     }
   ]
 }
}

{
 "label": [
   {
     "field_id": "Width",
     "field_type": "Float",
     "field_value": "3.9"
   },
   {
     "field_id": "Height",
     "field_type": "Float",
     "field_value": "5.5"
   },
   {
     "field_id": "Depth",
     "field_type": "Float",
     "field_value": "na"
   }
   ],
   "choices": [
     {
       "tag": "0",
       "value": "missing width"
     },
     {
       "tag": "1",
       "value": "missing height"
     },
     {
       "tag": "2",
       "value": "missing depth"
     }
     ]
    "selections": [
     {
       "tag": "2",
       "value": "missing depth"
     }
   ]
 }
}

Run Data program
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Input
OUTPUT
Image Transcription
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