Yellow Messenger in Gartner!

YM Gartner

Yellow Messenger got recognized over 11 Gartner Reports for our star platform features. 

Let’s find out more about what made this possible?

Most companies are working with some form of NLP, AI solutions, or are using them for different processes in the organization. The very form and mode of conversations have evolved in recent years, and with it has proliferated conversational AI solutions in the market. More than 35% of CIOs in the Gartner 2020 CIO survey have indicated that they have already adopted or are planning to adopt conversational platforms in the next 12 months.

An Intelligent virtual assistant platform is acclimated for its unique technological features. And the homage provided by Gartner to Yellow Messenger is because of our technological differentiator and what we intend for our conversational AI platform.

Intelligent virtual assistants are conversational interfaces that imply semantics, machine learning, natural language processing, prediction models, recommendations, and personalization to assist people or automate an entire conversation.

Here are 5-star technological features of the Yellow Messenger platform that made us the leading conversational AI solution provider:

  • Data-efficient NLP
  • Deep Learning-based Insights Engine
  • Translation-independent Multilingual Model
  • Marketplace and Community
  • Omni-channel Automation-first Service Desk

Our Differentiator

#1. Data-Efficient NLP

Yellow Messenger NLP engine utilizes transfer learning along with few-shot models to provide high data efficiency. These models are fine-tuned on the customer’s dataset, allowing custom intents to be created and trained. Along with this, the NLP engine uses embeddings that consider the entire sentence instead of individual words. This enables the model to accurately determine the user’s intent.

#2. Deep Learning-based Insights Engine

The abundance of unstructured data in the form of documents, stored in silos created over several years causes a lot of chaos and wastage of time. Yellow Messenger’s proprietary “Document Cognition” engine based on a Transfer Model, ingests the unstructured data and automatically creates intent from it, allowing the enterprise to offer cognitive search across any platform without any manual training. It can connect with knowledge management systems like Microsoft Sharepoint, ServiceNow, Confluence, etc. This allows enterprises to use their existing knowledge base to bootstrap faster, and the ranking accuracy automatically improves.

#3. Translation-independent Multilingual Model

Globalization and Digitization opened the floodgate of global consumerism to all enterprises. With this came the challenge of serving consumers in their native tongue. And hence we developed a multilingual sentence embedding model, supported by our language-specific models for certain languages. This enabled our customers to train intent classification models only in English and use the same data for NLU across languages.

#4. Marketplace and Community

Conversational AI is still in its nascent stage and we still need to establish guidelines for the best practices, conversational experiences, flows, journey, etc. Yellow Messenger is working relentlessly to establish a community of SI partners, and developers to support the development of enterprise-ready conversational ai solutions. 

#5. Omni-channel Automation-first Service Desk

The service Desk is a key component of any customer/employee conversation automation tool. Our entire approach to the service desk is automation-first, where not only do the Virtual Assistants deflect a huge majority of the incoming queries, but are also equipped to make human agents more productive, should a query be not completely deflected by the Virtual Assistant. One such feature is agent recommendations. We have also introduced agent actions to automate agent workflows, which can then be used to retrain the bot.

Working with global enterprises helped us realize early on that implementing an enterprise-grade virtual assistant is ineffective without the tools to support the large scale of these implementations. Our team relentlessly works on improving the conversational AI components to provide our customers the best of the best, and this also helps us get an edge in the market.

Gartner’s Report 

Within a span of 1 year, we got ourselves mentioned in 11 Gartner reports pertaining to the exceptional enterprise-consumer conversation experience we have provided to our global customers with our conversational AI solutions. 

#1. 2021 Planning Guide for Customer Engagement

Report Highlight:

Today’s customer engagement architecture must evolve to support shifts in customer and organizational requirements. Application technical professionals responsible for customer engagement must focus on increasing the adoption of cloud-based technology, AI, and the effectiveness of self-service.

Read the report here

#2. 2021 Strategic Roadmap for Enterprise AI: Natural Language Architecture

Report Highlight:

Enterprises must shift from tactical to the strategic use of Natural language to ensure greater portability of language assets and models. To solve key business challenges and deliver natural-language-enabled enterprises, application leaders must see NL solutions as a collective, fundamental whole.

Read the report here

#3. Emerging Technologies: Tech Innovators in Conversational AI and Virtual Assistants

Report Highlight:

Innovations in NLT, the proliferation of virtual assistants, and increasing usage of neural machine translation will transform business, social and human-machine interactions over the next two years. Product leaders can learn from the ways innovators are disrupting the conversational AI space.

Read the report here

#4. Emerging Technologies and Trends Impact Radar: Devices

Report Highlights:

The rate of technology emergence and the pace of innovation is accelerating device diversity while shortening device lifetime. Product leaders in the device market must exploit emerging technologies to remain competitive, capitalize on market opportunities, and differentiate their offerings.

Read the report here

#5. Emerging Technologies and Trends Impact Radar: 2021

Report Highlights:

The most impactful technologies and trends are quickly evolving and have the potential to disrupt and transform the market. Product leaders must explore critically emerging technologies now to remain competitive, capitalize on market opportunities, and differentiate their offerings.

Read the report here

#6. Emerging Technologies and Trends Impact Radar: Hyper Automation

Report Highlight:

The idea of automating everything intrigues many organizations, but technologies are at differing maturity levels. Product leaders must decide which newer hyper-automation enabling technologies to offer, at what point in time they will be sufficiently mature and able to support clients.

Read the report here.

#7. Emerging Technologies and Trends Impact Radar: Artificial Intelligence

Report Highlight:

AI innovation — clustering around AI democratization, intelligent business insights, edge AI, and transforming human-machine interactions — flourished in 2020. Product leaders must understand the timing and potential impact of AI technologies to achieve competitive advantage and differentiation.

Read the report here.

#8. Emerging Technology Horizon for Devices

Report Highlight:

The rate of technology emergence and the pace of innovation are accelerating device diversity while shortening device lifetime. Product leaders in the device market must exploit emerging technologies to remain competitive, capitalize on market opportunities and differentiate their offerings.

Read the report here.

#9. Improve Customer Self-Service and Self-Solve With Knowledge-Centered Service

Report Highlight:

Customer self-service is valuable, but difficult to support well. This is a knowledge management problem that must be informed by analytics and enabled by the search. Application technical professionals can leverage Knowledge-Centered Service to improve self-service and support.

Read the report here.

#10. 2021 Strategic Roadmap for Enterprise AI: Natural Language Architecture

Report Highlight:

Enterprises must shift from tactical to the strategic use of Natural language to ensure greater portability of language assets and models. To solve key business challenges and deliver natural-language-enabled enterprises, application leaders must see NL solutions as a collective, fundamental whole.

Read the report here.

#11. 2021 CIO Agenda: A Banking and Investment Services Technology and Service Provider Perspective

Report Highlight:

The 2021 Gartner CIO Survey results suggest that financial services organizations remain optimistic for 2021 as they refocus on digitalization and new operating realities after COVID-19 impacts. Product leaders must align their products and services to support these industry imperatives.

Leave a comment