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generalBuild and deploy custom apps with no code
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.
Build and deploy custom apps with no code
AI presentation maker for PowerPoint and Google Slides
AI content generation for businesses
Real-time voice changer for Discord, Zoom, and OBS
Practice management for healthcare
Online course for AI prompt engineering
Single API for 500+ AI models
Automate deal analysis and CIM review
Press kit creation and media distribution
AI note-taking for students and researchers
Create summaries from any text automatically
Extract insights from reviews, surveys, and social media
Generate YouTube scripts and track competitor content
Affordable legal document templates and guides
Automated code review integrated with GitHub
Save and share prompts in one place
Transcribe and translate video content
Access advanced ChatGPT interface and prompts
Autonomous PR agent that pitches journalists and books podcasts
Screen recorder with AI transcription and meeting notes
AI chat bot for Twitch and Kick with custom personalities and games
AI essay, email, and script writing
ChatGPT-powered content generation for WordPress
Change your voice with AI effects and filters
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.