general 12

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.

Web Paid

Voicemod

general

Real-time voice changer for gaming and streaming

Paid 53 · 43,482 votes

Dialpad

general

AI contact center for omnichannel communication

Paid 52 · 29,208 votes

Respeecher

general

Professional AI voice generation for production

Paid 42 · 38,821 votes

Research Studio

general

AI-enhanced analysis for user research data

Paid 37 · 53,318 votes

Metaforms

general

Process survey data and generate insights with AI

Paid 37 · 47,628 votes

Maibrain

general

Business intelligence from multiple data sources

Paid 37 · 45,196 votes

Imgproof

general

Text checking in images and ads

Paid 36 · 29,783 votes

Nativish

general

AI writing tool that helps you write confidently in a second language

Paid 36 · 25,516 votes

Split Prompt

general

Break down complex prompts for better LLM responses

Paid 36 · 23,727 votes

Mixpeek

general

Search text, images, and video together

Paid 36 · 22,392 votes

Listen411

general

Transcribe podcasts in 1 minute for $1 per hour

Paid 35 · 13,099 votes

Thegist

general

Summarize Slack threads and channels

Paid 27 · 210 votes

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.