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AutoResponder.ai
✓ verifiedfree
Mobile auto-reply bot sending customizable automatic responses across WhatsApp, Messenger, Instagram, Telegram, Signal and Viber.
271K visits/mo5.0K saves
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Glassix
✓ verifiedPaid
AI-native contact center that resolves customer inquiries across chat, voice and social channels for support teams.
271K visits/mo752 saves
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Afiniti
✓ verifiedPaid
Contact-center AI that optimizes agent-customer pairing and routing to lift measurable business outcomes for large enterprises.
38K visits/mo
Pricing
Free: $0 (500 daily replies, unlimited rules)
PRO: $3.33/mo billed annually (unlimited replies, AI connections)
Free trial available
Starter: $49/user/mo
Growth: $65/user/mo (annual)
Free trial available
No public pricing
Core features
- ✦Rule-based automatic replies for incoming chats
- ✦Unlimited custom message rules
- ✦Pattern and similarity matching for triggers
- ✦Connects to ChatGPT, Gemini, Dialogflow and web servers
- ✦Tasker/MacroDroid integration for custom actions
- ✦Supports six major messaging apps
- ✦Omnichannel inbox for chat, email, social and voice
- ✦AI agents and chatbots with human co-handling
- ✦CrystalVoice AI voice channel
- ✦Automation and workflow routing
- ✦Reporting and real-time dashboards
- ✦Mobile agent app and API
- ✦AI-driven customer-agent pairing
- ✦Automated voice and chat AI agents
- ✦Dynamic routing and SLA orchestration
- ✦Natural-language analytics and simulations
- ✦Transparent, responsible-AI decisioning
- ✦No rip-and-replace integration with existing platforms
Use cases
- →Auto-answer customer messages on WhatsApp
- →Send out-of-office replies
- →Build simple messaging chatbots
- →Route messages to AI models for smart responses
- →Automate first-line customer support
- →Unify messaging across channels in one inbox
- →Deploy chatbots for 24/7 self-service
- →Scale support for e-commerce and enterprise teams
- →Improving contact-center conversion and retention
- →Optimizing agent pairings and routing
- →Automating customer interactions across voice and chat
- →Analyzing contact-center performance in natural language
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