Coda
generalDocs, spreadsheets, and apps in one platform
ml 25
The ML category is a broad collection of 674 tools that apply machine learning across industries and functions, from healthcare documentation and legal research to user research, code generation, and content creation. It captures AI applications that do not fit cleanly into a single vertical.
Chatbase
generalAI chatbot for customer support
Brancher.ai
generalNo-code platform for building AI apps
Fineshare
generalCreate AI voice clones from audio samples
Essay Grader AI
generalAutomated essay grading and feedback
Prem AI
generalApplied AI research lab building sovereign and private AI
Docsie
generalConvert videos and files into structured documentation
Spellmint
generalStructured planning tools for team brainstorming
Gluecharm
generalAI impact analysis for faster feature development
Verbee
generalTeam collaboration for ChatGPT conversations
Pressmaster AI
generalAI content creation that preserves your voice
Fixt
generalWriting correction across multiple languages
Dinibo
generalAI travel planning and booking
Kerplunk
generalGenerate images, text, and audio with an easy interface
clonemyvoice.io
generalGenerate voiceovers for long-form content at lower cost
Hello8
generalTranslate video to 90+ languages
MakeTheDocs
generalAuto-generate docs from video walkthroughs
TimelyGrader
generalAI grading for essays and assignments
Cleft Notes
generalTranscribe voice notes into text
AI-Powered arXiv Paper Summarization
generalGet summaries of arXiv research papers in seconds
Buildship
generalVisually build AI workflows and APIs with no code
Olvy
generalUser feedback and changelog management
Telborg
generalDaily data on US energy, power grids, and permits
Glitter AI
generalAuto-generate step-by-step guides
Because this category covers so many domains, browsing by sub-use case is more efficient than scrolling the full list. Tools like Hippo Scribe and SopCreator serve very specific professional workflows, while others like User Evaluation or Userpersona target product and UX teams. The quality bar across the category is uneven: some tools are mature products with enterprise customers, while others are early-stage experiments. When evaluating any tool in this space, look for evidence of actual accuracy and reliability in your specific domain, since ML performance varies dramatically across tasks. Integration depth and data handling are often the deciding factors for business use. Pricing models are diverse, from usage-based API billing to flat-rate SaaS subscriptions. Open-source alternatives exist for many of the underlying tasks, so for teams with technical resources, comparing commercial tools against self-hosted options is worth the effort.