@contractspec/lib.support-bot

Retrieves relevant support documentation and historical ticket data using semantic similarity search over embedded knowledge bases. The system converts incoming support queries into vector embeddings, searches against a pre-indexed corpus of FAQs, documentation, and past ticket resolutions, and ranks results by relevance score to inject contextual information into the LLM's response generation. This enables the bot to ground answers in organizational knowledge without requiring full context in the prompt.

Maintains conversation state across multiple turns, automatically extracting and updating ticket metadata (priority, category, customer intent) from dialogue context. The system uses the LLM to parse natural language interactions, identify when a new ticket should be created or an existing one updated, and manages the state machine transitions (open → in-progress → resolved) based on conversation flow. This enables seamless ticket lifecycle management without explicit user commands.

Aggregates ticket data to generate analytics and reports on support performance, including metrics like resolution time, customer satisfaction, common issues, and bot accuracy. The system tracks ticket lifecycle events, computes derived metrics (MTTR, first-response time, resolution rate), and exposes data through dashboards or API endpoints. This enables data-driven decisions about support operations and bot improvements.

Provides bidirectional sync with external ticket management systems, automatically creating/updating tickets in Jira, Zendesk, or GitHub Issues based on bot conversations, and pulling ticket status back into the bot for context. The system handles API authentication, field mapping between bot schema and external system schema, conflict resolution for concurrent updates, and maintains sync state. This enables the bot to work within existing support infrastructure.

Automatically scores conversation quality based on metrics like resolution success, customer satisfaction signals, and bot accuracy, and collects explicit feedback from customers or support staff. The system computes quality scores using heuristics (e.g., customer said 'thanks', ticket resolved quickly) or explicit ratings, tracks quality trends, and identifies low-quality conversations for review. This enables continuous improvement of bot responses.

Detects duplicate or related support tickets by computing semantic similarity between incoming queries and existing tickets using embeddings. The system clusters similar tickets together, suggests merging candidates to support staff, and automatically links related tickets to prevent fragmented conversations. This reduces redundant support work and helps identify systemic issues affecting multiple customers.

Constructs LLM prompts dynamically by injecting relevant ticket history, customer profile, and knowledge base context retrieved via RAG. The system builds a context window that includes previous interactions with the customer, similar resolved tickets, and relevant documentation, then formats this into a structured prompt template that guides the LLM toward consistent, contextual responses. This enables the bot to provide personalized answers without requiring fine-tuning.

Provides a unified interface to multiple LLM providers (OpenAI, Anthropic, local models) with automatic fallback routing if the primary provider fails or rate-limits. The system abstracts provider-specific API differences, handles token counting and context window constraints per model, and routes requests to alternative providers based on cost, latency, or availability. This enables resilience and cost optimization without changing application code.

Parses natural language customer messages to extract structured ticket fields (customer name, email, issue category, priority, product/service affected) using LLM-guided extraction with optional schema validation. The system uses few-shot prompting or function calling to map conversational input to a predefined ticket schema, handles missing or ambiguous fields with clarification prompts, and validates extracted data against business rules. This enables automatic ticket creation without manual form filling.

Maintains conversation state across multiple turns while optimizing token usage by summarizing old messages, truncating irrelevant context, and prioritizing recent interactions. The system tracks conversation length, estimates token count per message, and automatically summarizes or removes older turns when approaching LLM context limits. This enables long conversations without exceeding token budgets or losing important context.

Analyzes customer sentiment from chat messages using LLM-based classification or dedicated sentiment models, automatically escalates frustrated or angry customers to human agents, and tracks sentiment trends over conversation. The system classifies messages as positive, neutral, negative, or urgent, triggers escalation workflows when sentiment crosses thresholds, and provides sentiment metadata to support staff for context. This enables proactive handling of dissatisfied customers.

Automatically indexes new documentation, FAQs, and ticket resolutions into the RAG knowledge base with incremental updates rather than full re-indexing. The system monitors source documents for changes, generates embeddings for new content, updates the vector store, and maintains version history. This enables the knowledge base to stay current without manual intervention or expensive full re-indexing cycles.

Defines reusable response templates with conditional logic (if-then rules) that customize bot responses based on ticket metadata, customer profile, or extracted context. Templates support variable interpolation, branching logic, and fallback responses, enabling consistent messaging without hardcoding responses in code. This allows non-technical support staff to customize bot behavior through template configuration.