Microsoft Azure Cognitive Services
generalPre-built AI APIs from Microsoft for vision, speech, language, and decision tasks
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.
Pre-built AI APIs from Microsoft for vision, speech, language, and decision tasks
Open-source hub for ML models, datasets, and inference.
Real-time voice changer for gaming and streaming
AI assistant supporting multiple major models
AI contact center for omnichannel communication
Docs, spreadsheets, and apps in one platform
Payment and tax handling for digital products
Remove watermarks and unwanted objects from photos
AI voice changer for gaming, streaming, and chat
AI code assistant for faster development
Writing clarity tool that flags clunky sentences
Knowledge base software for teams
AI chatbot for customer support
Text-to-video with custom avatars and AI dubbing
Build and deploy custom apps with no code
AI-powered knowledge base for company information
All-in-one AI development platform from browser with zero setup
AI presentation maker for PowerPoint and Google Slides
AI content generation for businesses
Customer intelligence platform for feedback analysis
Real-time voice changer for Discord, Zoom, and OBS
Chrome extension combining multiple AI models
Enterprise platform for building voice AI agents
Practice management for healthcare
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.