Twig

Twig analyzes incoming customer support tickets or chat messages using natural language understanding to identify issue categories, severity levels, and resolution pathways. It routes issues to appropriate resolution handlers (automated responses, knowledge base articles, or human agents) based on confidence scores and issue complexity, operating as a middleware layer between customer communication channels and support infrastructure.

Twig operates as a standalone support agent that handles customer inquiries outside business hours without human intervention, maintaining conversation context and escalation paths. It likely uses a state machine or conversation manager to track issue resolution progress, detect when human escalation is needed, and hand off to live agents with full context preservation when automated resolution fails.

Twig provides real-time assistance to human support agents by analyzing customer messages and suggesting relevant responses, knowledge base articles, or next steps. It operates as a co-pilot layer that enriches agent context with relevant information, previous interactions, and recommended actions, reducing cognitive load and improving resolution quality without replacing human judgment.

Twig integrates with multiple customer communication channels (email, chat, social media, ticketing systems) and presents them in a unified interface for both AI and human agents. It likely normalizes message formats, preserves conversation threading across channels, and maintains a single source of truth for customer interactions, enabling seamless handoffs between automated and human support.

Twig maintains persistent customer profiles and interaction history, enabling both AI and human agents to access relevant context about past issues, preferences, and resolution outcomes. It likely uses a vector database or semantic search to surface relevant historical interactions when new issues arise, reducing repetitive explanations and enabling more personalized support.

Twig detects when an issue exceeds its resolution capability and automatically escalates to human agents while preserving full conversation context, customer history, and AI-generated analysis. It likely uses confidence scoring, issue complexity detection, and predefined escalation rules to determine when human intervention is needed, then packages relevant information for seamless agent takeover.

Twig integrates with customer knowledge bases, documentation, or FAQ repositories and uses semantic search to retrieve relevant articles or solutions for customer issues. It likely embeds knowledge base content into a vector database and performs similarity matching against customer queries, enabling both AI and human agents to quickly surface relevant information without manual searching.

Twig generates customer-facing responses that match brand voice, tone, and communication style guidelines. It likely uses fine-tuning or prompt engineering to ensure generated responses align with company standards, avoiding generic or off-brand language. Responses are generated in real-time for automated resolution or as suggestions for human agents to review and send.