Annotation Solutions
Conversational AI
The Right Data to Train Your Virtual Assistants and Chatbots to Have the Right Conversations
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
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
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.
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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.
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.
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.