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Conversational AI

The Right Data to Train Your Virtual Assistants and Chatbots to Have the Right Conversations

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High Quality Data to Develop Effective Conversational Solutions

From chatbots to virtual assistants, conversational AI products are only as smart as the technology and data behind them. At Innodata, we make sure that you have the right data to accurately bridge conversations between humans and machines.

Combining our deep understanding of conversational AI data requirements with experienced AI resources and technologies, we deliver high-quality datasets to help train your virtual assistants or chatbots to seamlessly interact, engage and converse with humans.

Knowledgeable

We have teams of experts that support all processes required to build the data needed for conversational AI products, from data aggregation and taxonomy development to the creation of intents and utterances and labelling and annotation.

secure

We take data security and privacy seriously and we’re certified to handle the most sensitive and highly regulated data.

Efficient

With access to on-demand teams, we can process and build datasets within your set time frame at a large scale.

expertise

No crowdsourcing – we only utilize accredited and certified experts in the required domains across a variety of use cases such as travel, finance and human resources.

accurate

We utilize advanced processes and technologies coupled with process control techniques developed over 30 years building the world’s highest quality data products.

Global

24/7 global operations supporting data in 25+ languages, including Chinese, Korean, Japanese, and languages in Cyrillic and Roman script.

Conversational AI Services to Help Enhance Key Initiatives

Innodata offers a number of capabilities to meet a variety of needs for building conversational AI products:

Customer Support Chatbots

Train your chatbots or virtual assistants to better understand intents and utterances to enhance customer experience.

Sales & Marketing Chatbots

Train your chatbots to market and sell your products by providing the right product information to your potential customers

Travel Booking Assistants

Enable automated booking assistants to accurately understand booking requests and provide the correct responses and travel support.

Human Resource Service Delivery

Automate delivery of human resource services with chatbots including employee engagement, personalized messaging and communication.

REcommendation Engines

Provide intelligent recommendations on topics such as food and drinks, travel and tourism, education and personalized learning.

Deep Expertise Across Various Data Processes

Our teams support all processes required to build the data needed for any type of conversational AI product:

Large Data Aggregation

Automatically aggregate data from web sources

Taxonomy Development

Build taxonomies and ontologies for your platform

Labelling & Data Annotation

Label and annotate data to create structured datasets

Creating Intents & Utterances

Create intents and utterances for training your platform

Bring Intelligence to Your Digital Engagements

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Conversational AI Success Stories

We help some of the world’s leading brands build their conversational AI products.

Virtual Assistant

Travel Booking Company Deploys AI Booking Assistant Chatbot

Travel Booking Company Deploys AI Booking Assistant Chatbot

Objective

A leading travel aggregator and booking engine required data to train its AI platform that powers its booking assistant chatbot. The chatbot needed to accurately identify the intents of customer messages in order to provide the appropriate response.

Solution

  • Annotated messages from chatbot users for any mention of a specific hotel or hotel chain and all occurrences of locations, including cities, regions, districts, and addresses.
  • Labelled messages for the most applicable labels from a taxonomy to identify the intent of the messages. Labeling covered English and non-English messages including Chinese and French.

  • Implemented double pass process and quality audit to ensure high quality in the annotation and labelling.

Results

  • Highly accurate annotated and labelled datasets were created to train the AI platform to accurately respond to customer messages.
  • The datasets enabled the booking assistant to provide appropriate responses to customer inquires.
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Recommendation Engine

Leading Cruise Operator Builds Travel Recommendation Engine

Leading Cruise Operator Builds Travel Recommendation Engine

Objective

A leading cruise operator wanted to build a chatbot that can provide its passengers with recommendations on activities and to do in its ports of destination. To bring forth a guided conversation, the operator needed to acquire data about the destinations, organize the data, build a taxonomy and label the data to the taxonomy and ingest the data as part of the knowledge graph for recommendations.

Solution

  • Scraped thousands of websites for travel content and other data relevant to the identified ports of destination.
  • Built a taxonomy based on information from the acquired data.
  • Labelled the acquired data in accordance with the taxonomy.
  • Created intents and utterances for the recommendation engine.

Results

  • Innodata created the datasets for the recommendation engine within 3 months.
  • The cruise operator successfully deployed the recommendation engine, which provided passengers with recommendations based on the passenger profiles and other parameters specified by passengers.
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Chatbots

Global Organization Automates HR Service Delivery to Employees

Global Organization Automates HR Service Delivery to Employees

Objective

A global organization wanted to automate the delivery of human resource services to employees based in multiple countries. A key part of this strategy was the deployment of a chatbot that enables employees to ask questions and get answers directly on areas such as employee benefits and company policies.

Solution 

  • Developed a taxonomy for annotating specific data points in company policy documents.
  • Annotated thousands of company policy documents in accordance with the taxonomy.
  • Created intents and utterances to enable the chatbot to understand the intent of the questions from employees and return the appropriate answer.

Results

  • The organization was able to launch the chatbot and received positive feedback from its employees.
  • The platform also included a feedback loop to continuously improve the accuracy of the answers returned by the chatbot.
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