Notta Showcase
generalTranscribe and summarize meetings
General AI and machine learning tools include platforms for building, deploying, and managing ML models, along with infrastructure, evaluation, and workflow tools that support AI development broadly. With 674 tools, this category covers a wide spectrum from no-code ML builders to developer-facing MLOps infrastructure.
Transcribe and summarize meetings
Skills validation and hiring platform
Humanize AI-generated text
Knowledge base with AI search and chat
Get summaries and answers while watching YouTube
Online course for AI prompt engineering
No-code platform for building AI apps
Create AI voice clones from audio samples
Web scraping and data extraction
Professional AI voice generation for production
Summarize and translate documents in 150+ languages
Rewrite text in your own words instantly
Identify the location where a photo was taken
Captions and subtitles for video and audio in 120+ languages
Marketplace for AI agents and agencies
Single API for 500+ AI models
Convert images to AI art prompts
AI shopping comparison app for finding products
Detect UX changes and understand their impact
Opera browser with integrated AI assistant
AI automation for sales and revenue operations
Automate deal analysis and CIM review
Frontend for ChatGPT, Claude, Gemini, and other LLMs with low costs
ChatGPT interface supporting GPT-3.5, GPT-4, and Claude with free credits
This category includes tools aimed at very different audiences. Platforms like Ultracode and Workverse lean toward automation and productivity applications built on AI, while infrastructure tools like EdgeTrace serve engineers managing model pipelines and monitoring production systems. Tools like Userpersona and Hippo Scribe apply ML techniques to specific tasks like persona generation or medical transcription. The unifying thread is that they are powered by machine learning but do not fit neatly into a narrow vertical like image generation or speech-to-text. When navigating this category, the most useful filters are technical depth (no-code vs. API-first), deployment environment (cloud vs. self-hosted), and target use case. Many enterprise-grade tools here require custom pricing quotes, while developer tools often offer usage-based billing. Evaluating model accuracy and latency on your specific data is almost always necessary before committing to production use.