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Solutions Servicing Generative AI

Integrate, Develop, & Train Large Language Models

Gain Productivity and Advance Application Development

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Solutions Servicing Large Language Models and Generative AI

At Innodata, we believe that large language models and generative AI have the power to revolutionize how companies run and engage with their customers. Businesses can gain a competitive advantage and keep a leading edge by utilizing recent developments in natural language processing and generative AI technologies.

Innodata specializes in integrating and customizing generative AI, transformer models, and Open AI’s ChatGPT for your products and operations. To increase efficiency and improve application development, we assist enterprises in integrating large language models, or many large language models, into their business processes and end products in 40+ languages.

Increase Efficiency & Innovation

Streamline your business operations, improve customer engagement, and enhance the quality of your products. Because large language models can dramatically increase productivity and innovation, we collaborate closely with you to ensure that our solutions are in line with your corporate objectives.

Tailored Solutions For Any Use Case

Because we are aware that every company is different, our services are customized to fit your particular requirements. We have the knowledge to support your success whether you’re wanting to train, developed, or integrate a single model or a number of models in your organization.

Meet your Generative AI Partners

Our team of 4,000+ in-house experts can help you fully utilize large language models and benefit from the most recent developments in the field of natural language processing. We can help you with the integration, with developing training models, and with training to make sure that your team has the know-how to use these tools efficiently.

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Success Stories

We help some of the world’s leading brands gain productivity and advance application development.

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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Unlock the Full Potential of Large Language Models and Generative AI

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