GenAI Applications
GenAI applications use AI models to create content, automate tasks, and boost productivity across business areas, helping teams choose and apply tools effectively.
Generative AI in Fashion: Top 13 Use Cases & Examples
89% of all companies across different sectors are switching to digital technologies, and the generative AI in the fashion industry is not an exception. McKinsey reports that fashion brands and companies invested approximately 2% of their income in emerging technologies. Moreover, they estimate the figure will rise to 3.5% by 2030.36 Blockchain technology, non-fungible tokens…
Top 40 Chatbot Applications with Examples in 2026
The global chatbot market is valued at $10.32–$11.45 billion in 2026, up from $8.7 billion in 2024, and projected to reach $32.45 billion by 2031 at a 23.15% CAGR. The generative AI chatbot segment alone is valued at $12.98 billion and growing faster, at a 31.11% CAGR. 51 That growth is real, but the more…
Generative AI Copyright: Law, Litigation & Best Practices in 2026
We analyzed tens of court cases and licensing deals to answer the key questions about copyright and generative AI. This is not legal advice. Copyright law varies by jurisdiction and is evolving fast. In most jurisdictions, the legality of using copyrighted works to train AI models has been actively litigated and courts are beginning to…
Top 12 AI Avatar Generation Tools
When choosing the right AI avatar generation tool, businesses can take into account the following components: We tested 7 AI avatar generation tools and compared their visual (resolution and export capabilities) and voice (number of languages supported and voice cloning availability) features, as well as their pricing plans. We signed up for the free trial…
17 Generative AI Healthcare Use Cases
Healthcare systems are facing increased data volumes, staff shortages, and rising expectations for personalized care. Generative AI is emerging as a key solution by synthesizing unstructured medical data, such as clinical notes, imaging reports, and patient histories, into insights for clinicians and administrators. Explore how generative AI is applied across healthcare delivery, administration, and population…
Top 10 Voice Recognition Tool & Applications
If you’ve used virtual assistants like Alexa, Cortana, or Siri, you’re likely familiar with speech recognition and conversational AI. This technology enables users to interact with devices through verbal commands by converting spoken queries into machine-readable text. Explore the top 10 uses of voice recognition technology in voice search, customer service, healthcare, and other areas.…
Speech-to-Text Benchmark: Deepgram vs. Whisper
We benchmarked the leading speech-to-text (STT) providers, focusing specifically on healthcare applications. Our benchmark used real-world examples to assess transcription accuracy in medical contexts, where precision is crucial. Based on both word error rate (WER) and character error rate (CER) results, GPT-4o-transcribe demonstrates the highest transcription accuracy among all evaluated speech-to-text systems. Deepgram Nova-v3 and…
Compare Google Dialogflow and Its Competitors
Tech giants such as Google, IBM, Microsoft, Amazon, and Facebook are investing in conversational AI to enable developers to build chatbots easily. These AI-powered chatbots can automate various routine tasks such as sending emails, searching for information on search engines, etc. We have collected essential information about Google Dialogflow and compared it to its main…
Answer Engine Optimization (AEO): Tips & Best Practices
With ~60% of Google searches resulting in zero clicks, users are becoming accustomed to receiving answers without visiting sources. 116 Answers engines like Perplexity.ai that provide answers rather than links, are growing in popularity. Explore the top answer engine optimization best practices, 6-key-components of AEO strategies, and AEO performance metrics: Real-life example: Rand Fishkin, the…
Sentiment Analysis Benchmark Testing: ChatGPT, Claude & Qwen
Achieving precise labeling of emotions and sentiments, as well as detecting irony, hatefulness, and offensiveness, remains a challenge, requiring further testing and refinement. We tested 10 large language models across five sentiment tasks: emotion, hatefulness, irony, offensiveness, and sentiment. We ranked them by average accuracy across all five. The results highlight clear distinctions between the…