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Automate text processing with AI

AI text processing that’s flexible, easy to use, and powerful right out of the box.

Read between the lines with AI-powered text processing.

Named Entity Recognition
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Choose from a library of AI apps or create your own
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Extract actionable data with guaranteed quality
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Combine with your preferred tool for end-to-end automation
HOW IT WORKS

Easily connect to your data, define human and AI workflow, and extract information

Intent Variation
Email Understanding
Named Entity Recognition
Entity Linking
input
OUTPUT

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?"}]}'

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?"}])""

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?"]}]

output
Intent VariationIntent VariationIntent Variation
Intent VariationIntent VariationIntent Variation
input
OUTPUT

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"}]}'

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"}])"

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"}]

output
Email UnderstandingEmail UnderstandingEmail Understanding
Email UnderstandingEmail UnderstandingEmail Understanding
input
OUTPUT

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."}]}'

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."}])"

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"
     }
   ]
}

output
Named Entity RecognitionNamed Entity RecognitionNamed Entity Recognition
Named Entity RecognitionNamed Entity RecognitionNamed Entity Recognition
input
OUTPUT

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."}]}'

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."}])"

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
     }
  ]
}

output
Entity LinkingEntity LinkingEntity Linking
Entity LinkingEntity LinkingEntity Linking
USE CASES

What can AI do for text processing?

Content Moderation
Content Moderation

Scale comment analysis, automate monitoring, define actions based on pre-determined triggers, and more with AI-powered content moderation.

Product Recommendations
Product Recommendations

Improve product recommendations, leading to increased revenue and more engaged users.

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CASE STUDIES

You are in great company

Chemicals
Improving Chatbot Conversationality for Linde
The customer was building a chatbot solution for an internal knowledge base for its employees. They were using an existing ML service to build natural language into bots. They were looking for a reliable partner to help them classify the available information and format it into training data for their ML service.
Software
Building a Smarter Chatbot for Facebook
The customer is a remote software provider offering a suite of various services for its customers, in a SaaS model. As part of its ongoing automation processes, the company had launched a customer service chatbot.
Retail
Profitero Speeds Up Processing and Improves Data Accuracy 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.
WHO WE ARE

This is Super.AI

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Our data programming and adaptable Meta AI make it possible to guarantee output quality. Simply select a performance threshold and let us do the rest.
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Built for non-technical users
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Leverage AI, bot, and human workers
Combine multiple sources of supervision to achieve your desired quality threshold. The system automatically improves accuracy and increases automation over time.
Leverage AI, bot, and human workers