Rosie

Intercepts incoming phone calls at the carrier/VoIP level using SIP protocol integration or carrier API hooks, routes calls to AI processing pipeline in real-time, and maintains bidirectional audio streaming with sub-100ms latency. Implements call state management (ringing, connected, hold, transfer) and integrates with existing phone systems via direct number assignment or call forwarding rules.

Converts incoming audio to text in real-time using streaming speech-to-text (likely Deepgram, Google Cloud Speech, or proprietary model), applies NLP to extract caller intent, sentiment, and key entities (name, phone, issue type) during the call. Uses context windows and conversation history to maintain coherence across multi-turn dialogues and identify when human escalation is needed.

Offers callers the option to schedule a callback at a preferred time instead of waiting on hold, stores callback request with caller context (issue, phone, preferred time), and automatically initiates callback call at scheduled time with full conversation history available. Integrates with team calendars to find available time slots and can prioritize callbacks based on customer value or issue urgency.

Generates natural, contextually appropriate responses using an LLM (likely GPT-4, Claude, or fine-tuned model) with access to business context (company info, policies, FAQs, customer history). Maintains conversation state across turns, applies business rules (e.g., 'never quote prices without manager approval'), and generates responses optimized for speech synthesis (shorter sentences, natural pauses, no special characters).

Converts AI-generated text responses to natural-sounding speech using neural TTS (likely Google Cloud TTS, Amazon Polly, or ElevenLabs) with prosody modeling to add emphasis, pauses, and intonation. Handles real-time streaming of audio chunks to the caller with minimal latency, supports multiple voices/languages, and optimizes for phone-quality audio (8kHz or 16kHz).

Analyzes conversation context and intent to determine if human escalation is needed, routes calls to appropriate team members (sales, support, billing) based on caller issue, and manages warm transfers with context handoff (transcript, customer history, unresolved questions). Uses decision trees or ML models to classify escalation triggers (e.g., 'customer angry', 'request outside AI scope', 'high-value opportunity').

Records all call audio and metadata (timestamp, duration, caller ID, transcript, intent, resolution) to secure storage with encryption at rest and in transit. Implements compliance features (TCPA, GDPR, HIPAA-ready) including consent tracking, automatic redaction of sensitive data (SSN, credit card numbers), and audit logs showing who accessed what data and when. Supports retention policies (auto-delete after N days) and legal hold for litigation.

Looks up caller information in CRM or customer database using phone number, retrieves customer history (previous calls, purchases, support tickets), and enriches conversation context with this data. Writes call outcomes (resolution, next steps, follow-up date) back to CRM automatically, updating customer records without manual data entry. Supports bidirectional sync with Salesforce, HubSpot, Pipedrive, and other CRM platforms.

Detects caller's language from speech (using speech-to-text language detection) and automatically switches AI responses, TTS voice, and knowledge base to match. Supports 20+ languages with language-specific NLP models for intent extraction and entity recognition. Handles code-switching (mixing languages mid-sentence) gracefully without requiring manual language selection.

Aggregates call data (volume, duration, resolution rate, escalation rate, sentiment) and generates dashboards/reports showing AI performance metrics. Tracks trends over time (daily/weekly/monthly), identifies common issues and failure modes, and provides insights for optimization (e.g., 'top 5 unresolved intents', 'calls with negative sentiment'). Supports custom metrics and alerts (e.g., 'alert if escalation rate > 20%').

Allows non-technical users to define business rules (e.g., 'never quote prices without manager approval', 'always ask for email before ending call', 'escalate if customer mentions competitor') using a visual rule builder or simple DSL. Enforces rules in real-time during conversation, blocking AI from violating policies and triggering escalation/alerts when rules are triggered. Rules can reference conversation context (intent, sentiment, entities) and customer data (account status, purchase history).