Aidbase

Automatically categorizes, prioritizes, and routes incoming support tickets using LLM-based intent classification and semantic understanding. The system analyzes ticket content to determine urgency, category, and optimal assignment path, reducing manual triage overhead and ensuring tickets reach the right team member or automated workflow. Routes can be configured based on custom business rules, SLA requirements, and team capacity.

Generates contextually appropriate initial responses to support tickets by analyzing ticket content, customer history, and knowledge base articles. Uses retrieval-augmented generation (RAG) to ground responses in company-specific documentation, reducing response time from minutes to seconds while maintaining brand voice and accuracy. Responses can be auto-sent or presented to agents for review/editing before sending.

Analyzes incoming support communications to automatically detect customer intent (bug report, feature request, billing issue, general question, etc.) and categorize issues using multi-label classification. Uses semantic embeddings and fine-tuned language models to understand nuanced customer language, handling implicit intents and mixed-intent messages. Results feed downstream automation, analytics, and team workflows.

Enables semantic search across company documentation and knowledge bases using vector embeddings and dense retrieval, returning ranked results based on semantic relevance rather than keyword matching. Integrates with support workflows to surface relevant articles during ticket handling, and powers RAG for response generation. Supports full-text search fallback for exact phrase matching and handles multi-language queries.

Automatically summarizes multi-turn support conversations into concise, actionable summaries capturing key issues, resolutions, and customer sentiment. Extracts structured insights including problem root cause, solution applied, time-to-resolution, and customer satisfaction indicators. Summaries are stored with tickets for future reference and feed analytics dashboards. Uses abstractive summarization rather than extractive to produce human-readable summaries.

Consolidates support inquiries from multiple channels (email, chat, social media, in-app messaging, etc.) into a unified ticket format with normalized metadata. Deduplicates messages from the same customer/conversation thread across channels and maintains channel-specific context (e.g., Twitter handle, email thread ID) for response routing. Provides single pane of glass for support teams while preserving channel-specific response requirements.

Monitors incoming support tickets and customer interactions to identify emerging issues, patterns, or critical problems that require immediate escalation or intervention. Uses anomaly detection on support metrics (spike in similar issues, unusual error patterns) combined with keyword/intent analysis to surface systemic problems. Alerts support leadership and product teams to issues that may indicate product bugs, outages, or widespread customer dissatisfaction.

Analyzes support conversations and customer feedback to extract sentiment (positive, negative, neutral) and satisfaction indicators. Tracks sentiment trends over time and correlates with support metrics (resolution time, issue type, agent) to identify factors affecting customer satisfaction. Provides per-agent sentiment scores and team-level satisfaction dashboards. Uses aspect-based sentiment analysis to identify specific product/service areas driving satisfaction or dissatisfaction.

Analyzes historical support tickets to automatically identify frequently asked questions and generate FAQ entries and knowledge base articles. Uses clustering to group similar issues, extracts common questions and solutions, and generates article drafts that support teams can review and publish. Continuously updates FAQ recommendations as new ticket patterns emerge. Reduces manual knowledge base maintenance burden while ensuring documentation stays current with actual customer questions.

Aggregates support metrics (response time, resolution time, customer satisfaction, ticket volume, escalation rate) across team members and time periods to provide performance dashboards and benchmarking. Identifies top performers and underperformers, tracks trends, and correlates metrics with outcomes (customer satisfaction, repeat tickets). Provides per-agent and team-level analytics with drill-down capabilities. Supports custom metric definitions and comparison against industry benchmarks.