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Introducing Kyndi 6.0 - The latest release of Kyndi's Generative AI Answer Engine that optimizes knowledge management and user experiences

Your website visitors should be able to find answers to their questions easily and quickly through your site. A natural language-powered interfaces facilitate this effort and drive improved user engagement.

Making generative AI work work for enterprises with Kyndi Generative AI Answer Engine

Unpack the new capabilities in Kyndi's Generative AI-powered Answer Engine

Simplifying large language model implementation and making enterprise knowledge instantly available to everyone with Kyndi's end-to-end Answer Engine

A summary of new features and enhancements in the latest release Kyndi 4.2

Understand the difference between Kyndi and ChatGPT

Introducing Kyndi Clarity - A Self-service Solution Empowering Everyone to Find the Right Answers in One Click

There are different approaches to tackling AI projects. Question is: Do you want your team to spend time labeling data and training the model, or do you want to focus on time to production so your business users can benefit from AI immediately?

A quick guide for designing Kyndi Natural Language Search that users love to use

Use the right search tool for your data to find the correct answers for your users

Kyndi Natural Language Search - Finding the right answers, fast!

InsideBIGDATA interviewed Kyndi Founder & CEO Ryan Welsh on the current limitations of unsupervised learning and the benefits of combining connectionist and symbolic approaches.

We are seeing an exciting shift in the enterprise mindset related to NLP AI solutions. On an increasing basis, organizations have evolved from a ‘does it work?’ mentality to a focus on ‘how do we deploy?’ this valuable new technology enterprise-wide.

Enterprise AI requires a new AI approach. That is our belief here at Kyndi, and that was a central theme at NeurIPS 2020, which was held virtually from December 6th – 12th. Among the AI community, it is now an accepted fact that supervised machine learning is prohibitive for most business situations. That’s because supervised machine learning requires too much technical knowledge, requires too much data (both quantity and quality), and adds too much business risk in most cases because it is a black box.