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The New GitBook
✓ verified
Widely used technical-documentation platform with AI-powered search; very high traffic.
👁 653K/mo♥ 2.9K
Pricing
Essential: $29
Advanced: $85
Expert: $132
Fin AI Agent: $0.99
Copilot: $29
Proactive Support Plus: $99
No public pricing
Free Plan: $0 one-time
Hobby: $16/month
Standard: $83/month
Growth: $333/month
Auto Recharge Credits: $11/mo for 1000 credits
Credit Pack: $9/mo for 1000 credits
Enterprise Plan: Contact for Pricing
No public pricing
Core features
- ✦Help Desk
- ✦AI Chatbot
- ✦Proactive Support
- ✦Omnichannel Platform
- ✦Knowledge Base
- ✦Inbox
- ✦Tickets
- ✦AI Insights & Reporting
- ✦Workflows
- ✦Apps and Integrations
- ✦AI-powered search and insights
- ✦Git-based workflow
- ✦WYSIWYG editor
- ✦OpenAPI spec file support
- ✦SEO and AI data ingestion optimization
- ✦Adaptive content
- ✦Built-in analytics
- ✦Web scraping
- ✦Web crawling
- ✦Data extraction in Markdown, JSON, and screenshot formats
- ✦Dynamic content handling
- ✦Rotating proxies
- ✦Rate limits management
- ✦Open-source availability
- ✦Media Parsing
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
Use cases
- →Deliver help content to customers
- →Divert repetitive questions with a bot
- →Set up a central place for customers to find answers
- →Create multilingual articles for global customers
- →Resolve issues before they arise using outbound messaging
- →Build product documentation your users will love
- →Create interactive API docs
- →Localize your docs with AI
- →Centralize product docs, API reference, help center, and changelog
- →Powering AI assistants with real-time web content
- →Enhancing sales data with web information
- →Adding scraping capabilities to code editors
- →Enabling customers to build AI apps with web data
- →Extracting comprehensive information for in-depth research
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
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