Cald.ai

Initiates automated outbound phone calls using AI agents that handle call routing, number dialing, and connection establishment through integrated telecom APIs (likely Twilio, Bandwidth, or similar). The system manages call state transitions from initiation through connection, handling dial failures, busy signals, and voicemail detection before handing off to the conversational AI agent.

Receives inbound phone calls via a dedicated phone number and routes them to AI agents based on IVR logic, caller intent detection, or skill-based routing rules. The system handles call queuing, agent availability tracking, and fallback routing (e.g., to human agents or voicemail) when AI agents are unavailable or the call requires escalation.

Analyzes customer sentiment and emotional state during calls using speech prosody analysis (tone, pitch, pace) and transcription-based NLU. The system provides real-time sentiment feedback to agents and can trigger escalation or behavior changes if negative sentiment is detected.

Manages outbound call scheduling across time zones, handles callback requests from customers, and implements intelligent retry logic (exponential backoff, optimal retry windows). The system tracks callback status and integrates with calendar systems to avoid scheduling conflicts.

Manages real-time two-way voice conversations using a speech-to-text pipeline, LLM-based response generation, and text-to-speech synthesis. The agent maintains conversation context across multiple turns, handles interruptions and overlapping speech, and generates natural-sounding responses with appropriate prosody and pacing for phone interactions.

Records all inbound and outbound calls, automatically transcribes them using speech-to-text, and stores recordings with compliance metadata (consent flags, retention policies, encryption). The system enforces regulatory requirements like TCPA consent recording and GDPR data retention limits, with audit logs for access control.

Aggregates call data (duration, outcome, agent performance, customer sentiment) and generates dashboards and reports showing key metrics like call volume, resolution rate, average handle time, and customer satisfaction. The system provides real-time monitoring and historical trend analysis with drill-down capabilities.

Allows configuration of AI agent behavior through system prompts, conversation templates, and behavioral rules (e.g., escalation triggers, response tone, handling of specific objections). Customization is applied at the agent level and can be A/B tested across different call cohorts to optimize performance.

Enables seamless transfer of calls from AI agents to human agents when the AI agent detects a need for escalation (e.g., complex issue, customer frustration, explicit request). The system maintains call context during transfer, queues calls based on agent availability, and provides warm handoff with agent briefing.

Supports inbound and outbound calls in multiple languages using language detection (for inbound) or language selection (for outbound). The system automatically routes calls to language-specific agents or uses multilingual speech-to-text and TTS models to handle non-English speakers.

Integrates with CRM systems (Salesforce, HubSpot, etc.) and other business applications to retrieve customer context before calls and update customer records after calls. The system uses APIs or webhooks to sync call data, customer interactions, and outcomes back to the CRM.

Automatically detects when a call reaches a voicemail system (using audio fingerprinting or machine learning) and either leaves a pre-recorded message, hangs up, or triggers a callback. The system distinguishes between human voicemail greetings, robotic voicemail systems, and answering machines.