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Named Entity Recognition
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Natural
Language
Intent Variation
Intent Variation
Email Understanding
Email Understanding
Named Entity Recognition
Named Entity Recognition
Entity Linking
Entity Linking
Input
OUTPUT
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dp = DataProgram("text-text")
instance = dp(name="my-text-text", instructions="Please clarify the intent of the following question by providing alternative questions to it.")
instance.label(inputs=[{"intent": "How long is your store normally open?"}])""

curl -X POST \
"https://super.ai/v1/apps/my-text-text/job" \
-H "API-KEY: live_t9wHIzxjuGSuKTx2jWQTzgXehu1uvkK27QM3tzm6f1g" \
-H "Content-Type: application/json" \
-d '{"inputs":[{"intent": "How long is your store normally open?"}]}'

superai client create_jobs \
--app_id my-text-text \
--inputs [{"intent": "How long is your store normally open?"}]

[{'variations': ["What are your business hours?",
"What are your hours of operation?",
"What time does your store open and close?",
"What are typical business hours?",
"What are your store's hours?",
"What are your store's opening times?"]}]

[{'variations': ["What are your business hours?",
"What are your hours of operation?",
"What time does your store open and close?",
"What are typical business hours?",
"What are your store's hours?",
"What are your store's opening times?"]}]

[{'variations': ["What are your business hours?",
"What are your hours of operation?",
"What time does your store open and close?",
"What are typical business hours?",
"What are your store's hours?",
"What are your store's opening times?"]}]

Run Data program
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Input
OUTPUT
Intent Variation
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="Extract all necessary information for customer service requests..")
instance.label(inputs=[{"email_url": "https://cdn.super.ai/Webflow+assets/email-understanding-input.png"}])"

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

superai client create_jobs \
--app_id my-test-tagging \
--inputs [{"email_url": "https://cdn.super.ai/Webflow+assets/email-understanding-input.png"}]

[{"intent":"missing suitcase inquiry", "flightNumber": "AA5432", "start":"Berlin, Berlin, DE", "end":"Seattle, Washington, USA", "date":"14.01.2020", "priority":"high"}]

‍

[{"intent":"missing suitcase inquiry", "flightNumber": "AA5432", "start":"Berlin, Berlin, DE", "end":"Seattle, Washington, USA", "date":"14.01.2020", "priority":"high"}]

[{"intent":"missing suitcase inquiry", "flightNumber": "AA5432", "start":"Berlin, Berlin, DE", "end":"Seattle, Washington, USA", "date":"14.01.2020", "priority":"high"}]

Run Data program
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Input
OUTPUT
Email Understanding
Input
OUTPUT
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dp = DataProgram("text-span")
instance = dp(name="my-text-span", instructions="Find all entities in text.")
instance.label(inputs=[{"text":"I remember I was driving Steve Jobs back from the airport along Highway 85. Steve was coming back from a visit to Oregon to a place he called an “apple orchard.” It was actually some kind of commune. Steve suggested a name – Apple Computer."}])"

curl -X POST \
"https://super.ai/v1/apps/my-text-span/job" \
-H "API-KEY: live_t9wHIzxjuGSuKTx2jWQTzgXehu1uvkK27QM3tzm6f1g" \
-H "Content-Type: application/json" \
-d '{"inputs":[{"text":"I remember I was driving Steve Jobs back from the airport along Highway 85. Steve was coming back from a visit to Oregon to a place he called an “apple orchard.” It was actually some kind of commune. Steve suggested a name – Apple Computer."}]}'

superai client create_jobs \
--app_id my-text-span \
--inputs [{"text":"I remember I was driving Steve Jobs back from the airport along Highway 85. Steve was coming back from a visit to Oregon to a place he called an “apple orchard.” It was actually some kind of commune. Steve suggested a name – Apple Computer."}]

{
   "spans": [
     {
       "end": 34,
       "tag": "0",
       "start": 25
     },
     {
       "end": 80,
       "tag": "0",
       "start": 76
     },
     {
       "end": 119,
       "tag": "1",
       "start": 114
     },
     {
       "end": 150,
       "tag": "2",
       "start": 146
     },
     {
       "end": 205,
       "tag": "0",
       "start": 210
     },
     {
       "end": 238,
       "tag": "3",
       "start": 226
     }
   ],
   "entities": [
     {
       "tag": "0",
       "value": "Person"
     },
     {
       "tag": "1",
       "value": "State"
     },
     {
       "tag": "2",
       "value": "Fruit"
     },
     {
       "tag": "3",
       "value": "Organization"
     }
   ]
}

{
   "spans": [
     {
       "end": 34,
       "tag": "0",
       "start": 25
     },
     {
       "end": 80,
       "tag": "0",
       "start": 76
     },
     {
       "end": 119,
       "tag": "1",
       "start": 114
     },
     {
       "end": 150,
       "tag": "2",
       "start": 146
     },
     {
       "end": 205,
       "tag": "0",
       "start": 210
     },
     {
       "end": 238,
       "tag": "3",
       "start": 226
     }
   ],
   "entities": [
     {
       "tag": "0",
       "value": "Person"
     },
     {
       "tag": "1",
       "value": "State"
     },
     {
       "tag": "2",
       "value": "Fruit"
     },
     {
       "tag": "3",
       "value": "Organization"
     }
   ]
}

{
   "spans": [
     {
       "end": 34,
       "tag": "0",
       "start": 25
     },
     {
       "end": 80,
       "tag": "0",
       "start": 76
     },
     {
       "end": 119,
       "tag": "1",
       "start": 114
     },
     {
       "end": 150,
       "tag": "2",
       "start": 146
     },
     {
       "end": 205,
       "tag": "0",
       "start": 210
     },
     {
       "end": 238,
       "tag": "3",
       "start": 226
     }
   ],
   "entities": [
     {
       "tag": "0",
       "value": "Person"
     },
     {
       "tag": "1",
       "value": "State"
     },
     {
       "tag": "2",
       "value": "Fruit"
     },
     {
       "tag": "3",
       "value": "Organization"
     }
   ]
}

Run Data program
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Input
OUTPUT
Named Entity Recognition
Input
OUTPUT
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

dp = DataProgram("text-relationship")
instance = dp(name="my-text-relationship", instructions="Find all relationships in text.")
instance.label(inputs=[{"text":"Citibank, which was involved in moving about $100 million for Raul Salinas de Gortari, brother of a former Mexican President, to banks in Switzerland, are also expected to sign on."}])"

curl -X POST \
"https://super.ai/v1/apps/my-text-relationship/job" \
-H "API-KEY: live_t9wHIzxjuGSuKTx2jWQTzgXehu1uvkK27QM3tzm6f1g" \
-H "Content-Type: application/json" \
-d '{"inputs":[{"text":"Citibank, which was involved in moving about $100 million for Raul Salinas de Gortari, brother of a former Mexican President, to banks in Switzerland, are also expected to sign on."}]}'

superai client create_jobs \
--app_id my-text-relationship \
--inputs [{"text":"Citibank, which was involved in moving about $100 million for Raul Salinas de Gortari, brother of a former Mexican President, to banks in Switzerland, are also expected to sign on."}]

{
  "relationships":[
     {
        "end":"0",
        "relationship":"GIVER",
        "start":"1"
     },
     {
        "end":"2",
        "relationship":"MONEY",
        "start":"1"
     },
     {
        "end":"3",
        "relationship":"BENEFICIARY",
        "start":"1"
     },
     {
        "end":"4",
        "relationship":"RECEPIENT",
        "start":"1"
     },
     {
        "end":"4",
        "relationship":"FAMILY",
        "start":"3"
     },
     {
        "end":"6",
        "relationship":"ORIGIN",
        "start":"5"
     }
  ],
  "entities":[
     {
        "tag":"0",
        "value":"Organization",
        "startIndex":0,
        "endIndex":7
     },
     {
        "tag":"1",
        "value":"Transfer",
        "startIndex":32,
        "endIndex":37
     },
     {
        "tag":"2",
        "value":"Money",
        "startIndex":45,
        "endIndex":56
     },
     {
        "tag":"3",
        "value":"Person",
        "startIndex":62,
        "endIndex":84
     },
     {
        "tag":"4",
        "value":"Person",
        "startIndex":100,
        "endIndex":123
     },
     {
        "tag":"5",
        "value":"Organization",
        "startIndex":129,
        "endIndex":133
     },
     {
        "tag":"6",
        "value":"GPE",
        "startIndex":138,
        "endIndex":148
     }
  ]
}

‍

{
  "relationships":[
     {
        "end":"0",
        "relationship":"GIVER",
        "start":"1"
     },
     {
        "end":"2",
        "relationship":"MONEY",
        "start":"1"
     },
     {
        "end":"3",
        "relationship":"BENEFICIARY",
        "start":"1"
     },
     {
        "end":"4",
        "relationship":"RECEPIENT",
        "start":"1"
     },
     {
        "end":"4",
        "relationship":"FAMILY",
        "start":"3"
     },
     {
        "end":"6",
        "relationship":"ORIGIN",
        "start":"5"
     }
  ],
  "entities":[
     {
        "tag":"0",
        "value":"Organization",
        "startIndex":0,
        "endIndex":7
     },
     {
        "tag":"1",
        "value":"Transfer",
        "startIndex":32,
        "endIndex":37
     },
     {
        "tag":"2",
        "value":"Money",
        "startIndex":45,
        "endIndex":56
     },
     {
        "tag":"3",
        "value":"Person",
        "startIndex":62,
        "endIndex":84
     },
     {
        "tag":"4",
        "value":"Person",
        "startIndex":100,
        "endIndex":123
     },
     {
        "tag":"5",
        "value":"Organization",
        "startIndex":129,
        "endIndex":133
     },
     {
        "tag":"6",
        "value":"GPE",
        "startIndex":138,
        "endIndex":148
     }
  ]
}

‍

{
  "relationships":[
     {
        "end":"0",
        "relationship":"GIVER",
        "start":"1"
     },
     {
        "end":"2",
        "relationship":"MONEY",
        "start":"1"
     },
     {
        "end":"3",
        "relationship":"BENEFICIARY",
        "start":"1"
     },
     {
        "end":"4",
        "relationship":"RECEPIENT",
        "start":"1"
     },
     {
        "end":"4",
        "relationship":"FAMILY",
        "start":"3"
     },
     {
        "end":"6",
        "relationship":"ORIGIN",
        "start":"5"
     }
  ],
  "entities":[
     {
        "tag":"0",
        "value":"Organization",
        "startIndex":0,
        "endIndex":7
     },
     {
        "tag":"1",
        "value":"Transfer",
        "startIndex":32,
        "endIndex":37
     },
     {
        "tag":"2",
        "value":"Money",
        "startIndex":45,
        "endIndex":56
     },
     {
        "tag":"3",
        "value":"Person",
        "startIndex":62,
        "endIndex":84
     },
     {
        "tag":"4",
        "value":"Person",
        "startIndex":100,
        "endIndex":123
     },
     {
        "tag":"5",
        "value":"Organization",
        "startIndex":129,
        "endIndex":133
     },
     {
        "tag":"6",
        "value":"GPE",
        "startIndex":138,
        "endIndex":148
     }
  ]
}

‍

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Entity Linking
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Use Cases

With great power comes great responsibility

Sentiment analysis

Determine and classify emotions or attitudes towards products, brands, or services within text data using NLP algorithms.

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Customer Service - General Enquiries

Use chatbots to scale your customer service team and provide high support and guidance.

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Inventory assistance

Use chatbots to assist your customers in finding products, checking for Inventory and recommending items.

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Lead generation

Use chatbots to generate new sales leads by qualifying potential customers.

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Orders tracking

Use chatbots to provide an easy way for customers to check order confirmation, order status and shipping status.

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Purchase assistance

Use chatbots to assist your customers in the purchase of various items such as concert tickets, hotel & restaurant reservations.

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Returns Processing

Use chatbots to assist and advise customers on returns and exchange of items.

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Chatbots

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Content Moderation

Analyze a large volume of user comments and automatically monitor and apply a pre-determined set of rules and guidelines to user-generated submissions.

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Knowledge Graph

Build a collection of interlinked descriptions of entities – objects, events or concept with the help of AI.

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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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Review Categorization

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

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Case Studies

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Oil & Gas

World leader in hydrogen production builds chatbot solution with super.AI

The customer was building a chatbot solution for an internal knowledge base for its employees. They were looking for a reliable partner to help them classify the available information and format it into training data for their ML service. Based on the original list of entities, we generated 88,000 augmentations and by combining them automatically, 6M automation, sufficient training data for its ML solution to build its first chatbot iteration for internal testers

We were looking for a supplier with NER expertise that could provide us with high-quality training data to feed into our existing ML service we were using to build a chatbot. We were very happy with the throughput and speed we received from super.AI.
Head of AI Solutions
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
Software

Software company improves Chatbot solution with super.AI

The customer reached out to us to help improve the relevance of the answers provided by their customer service chatbot. super.AI used its intent clustering data program to automate the clustering of a large input of help articles provided by the customer. Based on the output we provided to them, they were able to significantly improve the relevance of their chatbot.

We regularly benchmark super.AI’s Query Intent Data Program against the competition and the performance is very convincing. The CSV upload made my day so much easier, I was able to process thousands of data points in a matter of hours.
Data Scientist

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Customize instructions and labeling interface

Describe what you want done, output classes, edge cases, and things that are important for you. Humans will use this to train the AI.

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Choose your desired quality, cost, and speed. super.AI will gaurantee this level of quality using humans and AI.

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