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What's New in Kyndi 4.2
A summary of new features and enhancements in the latest release Kyndi 4.2
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What are the differences between Kyndi and ChatGPT?
Understand the difference between Kyndi and ChatGPT
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Introducing Kyndi Clarity – A Self-service Solution That Actually Works
Introducing Kyndi Clarity - A Self-service Solution Empowering Everyone to Find the Right Answers in One Click
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To train, or not to train? That is the business question.
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?
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Build a Kyndi Natural Language Search That Your Users Love
A quick guide for designing Kyndi Natural Language Search that users love to use
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Are you using the right search tool for your data and users?
Use the right search tool for your data to find the correct answers for your users
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Kyndi Natural Language Search-Finding the Right Answers, Fast!
Kyndi Natural Language Search - Finding the right answers, fast!
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The Future of AI: 2022 Trends
InsideBIGDATA interviewed Kyndi Founder & CEO Ryan Welsh on the current limitations of unsupervised learning and the benefits of combining connectionist and symbolic approaches.
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From Test Drive to Mainstream: Why Enterprises Should Consider a Platform Approach to Deploying NLP AI
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.
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Could Unsupervised Learning be THE Key to Enterprise AI Adoption?
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.
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