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Numa
✓ verifiedPaid
AI operating system for car dealerships with voice AI, a smart inbox and agents that capture leads and lift service revenue.
207K visits/mo337 saves
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16x Prompt
✓ verifiedFreemium
Local desktop app that assembles code-context prompts for LLMs, with API integrations, token tracking, and prompt saving.
21K visits/mo
Pricing
No public pricing
No public pricing
No public pricing
No public pricing
Free: $0 (10 prompts/day)
Individual lifetime license: $48
Team lifetime license: $68
Core features
- ✦Unlimited chatbots deployment
- ✦Automatic retraining on webpages
- ✦Customizable appearance and personality
- ✦One-click deployment to multiple platforms
- ✦User Management
- ✦Custom APIs
- ✦Lifelike voice AI agents
- ✦24/7 availability
- ✦Customer-led conversational platform
- ✦Integration with enterprise systems
- ✦Voice AI with full customer context
- ✦AI-native Smart Inbox across channels
- ✦LiveCSI real-time satisfaction monitoring
- ✦Service Advisor Agent
- ✦Heat Case and Opportunity Agents
- ✦Mobile app
- ✦Coding education platform for beginners
- ✦Curriculum on Next.js, Vercel, and AI
- ✦AI-powered app development
- ✦Live events and hackathons
- ✦Coding community
- ✦AI-centric platform for software engineers
- ✦Nia AI for code understanding
- ✦Context management and codebase understanding tools
- ✦Source-code context and prompt management
- ✦Custom and formatting instructions
- ✦BYOK API integrations (OpenAI, Claude, Gemini, etc.)
- ✦Token-limit tracking
- ✦Code-edit feature with visual diffs and backups
- ✦Local, offline prompt generation
Use cases
- →Customer service chatbots
- →Help desk chatbots
- →HR chatbots
- →Training chatbots
- →Consulting chatbots
- →White labeled chatbot for web apps
- →Answering customer service calls
- →Providing information and support
- →Resolving customer issues
- →Automating call center operations
- →Answering dealership calls and messages
- →Rescuing and booking service leads
- →Resolving customer complaints (heat cases)
- →Boosting service-advisor productivity
- →Learning to code and build AI-powered applications
- →Developing AI agents that can work with code safely and effectively
- →Empowering developers to orchestrate AI agents across the software lifecycle
- →Improving context management and codebase understanding for AI agents
- →Building context-rich prompts for AI coding
- →Comparing model outputs on the same task
- →Reusing saved prompts across tech stacks
- →Keeping code private during prompt creation
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