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Experimental AI tool that suggests regex patterns from highlighted example text, for developers who dislike writing regex by hand.
AI creation studio that gathers scattered notes and ideas, then helps shape them into articles, slides, videos, or webpages.
Real-time web search and content-extraction API that grounds AI agents with fresh, structured data for research and RAG.
Spreadsheet-style Python data tool inside Jupyter that generates code via AI so analysts and data scientists can skip hand-coding.
AI coding platform and IDE that orchestrates multiple agent sessions and lets teams plug in their own AI subscriptions.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Text highlighting to define target matches
- ✦Multiple candidate regex outputs shown side by side
- ✦Live preview of what each pattern matches
- ✦Central capture space for saved articles, videos, and notes
- ✦AI prompts that surface connections and gaps in collected ideas
- ✦Generates articles, slide decks, videos, and webpages from source material
- ✦Prebuilt style templates for slides and pages
- ✦Available as desktop app, browser extension, and mobile app
- ✦Skill and prompt library for specific creation tasks
- ✦Real-time web search API
- ✦Page content extraction and crawling
- ✦LLM-optimized structured/chunked output
- ✦Built-in PII and prompt-injection filtering
- ✦High-throughput, low-latency infrastructure
- ✦Drop-in integrations with major LLM providers
- ✦Spreadsheet interface inside Jupyter notebooks
- ✦AI-generated Python code from spreadsheet actions
- ✦Runs on customer infrastructure (no data sent to Mito)
- ✦Bring-your-own LLM API keys
- ✦Excel-to-Python conversion
- ✦Compatible with existing Jupyter extensions
- ✦AI coding IDE with agent orchestration
- ✦Run and manage multiple agent sessions
- ✦Task, artifact and collaboration tools
- ✦Bring-your-own AI subscription or API keys
- ✦Cloud-scale agent execution
- →Quickly building a regex for extracting emails or similar patterns
- →Comparing several regex approaches against sample data
- →Creators consolidating research into finished blog posts or scripts
- →Professionals turning meeting notes into structured reports
- →Teachers converting lesson ideas into slides and video lessons
- →Product managers organizing scattered feedback into insights
- →Grounding AI agents with live web data to reduce hallucination
- →Building research or retrieval-augmented generation (RAG) applications
- →Powering AI-driven search assistants
- →Enterprise-scale agents needing reliable web access
- →Analysts automating Excel-style reports without hand-writing code
- →Data scientists speeding up exploratory data analysis
- →ML engineers iterating on feature engineering in notebooks
- →Enterprises needing private, self-hosted AI coding assistance
- →Shipping code faster with AI agents
- →Coordinating agent work across a team
- →Managing tasks and artifacts in one place
- →Running many parallel agent sessions