Answerly

Routes incoming customer queries to pre-built FAQ response templates using pattern matching and keyword extraction rather than semantic understanding. The system maintains a knowledge base of common questions and maps incoming messages to the closest template match, returning curated responses without requiring real-time LLM inference. This approach trades contextual accuracy for speed and cost efficiency, enabling sub-100ms response times on routine queries.

Maintains independent conversation threads for each customer without persistent state storage, processing each message independently against the FAQ template database. The system assigns session IDs to track conversation continuity within a single chat window but does not retain conversation history across sessions or between customers. This stateless architecture enables horizontal scaling and eliminates database overhead but prevents context carryover across interactions.

Analyzes incoming customer messages for sentiment (positive, negative, neutral) and adjusts chatbot response tone accordingly. Negative sentiment triggers empathetic responses with apology language, while positive sentiment enables lighter, more casual tones. The system uses simple lexicon-based sentiment scoring rather than ML models, enabling fast inference without external API calls.

Records all chatbot conversations in a searchable database with timestamps, customer identifiers, and full message history. The system provides audit trail exports in compliance-friendly formats (CSV, JSON) for regulatory requirements. Conversations are retained according to configurable policies (e.g., delete after 90 days) and can be manually archived or deleted on request.

Provides a visual interface for non-technical users to design chatbot conversation flows using pre-built blocks (questions, responses, branching logic) without writing code. The builder uses a node-and-edge graph model where each node represents a message or decision point and edges define conversation paths based on user input. The system compiles these visual flows into executable conversation logic that runs on Answerly's infrastructure.

Accepts customer messages from multiple sources (website chat widget, email, SMS, social media) and routes them through a unified conversation engine before delivering responses back to the originating channel. The system maintains channel-specific adapters that translate between platform APIs (e.g., Slack API, Facebook Messenger API) and Answerly's internal message format, enabling a single chatbot logic to serve multiple channels without duplication.

Offers a free tier with limited message volume (typically 100-500 messages/month) and basic features, automatically escalating to paid tiers as usage increases. The system tracks message counts in real-time and displays usage dashboards showing current tier and upgrade triggers. Customers can manually upgrade to unlock higher limits, additional channels, or advanced features without changing their chatbot configuration.

Tracks and displays aggregate metrics including total messages handled, chatbot response rate, conversation completion rate, and customer satisfaction scores (if surveys are enabled). The dashboard presents time-series graphs and summary statistics but lacks granular conversation-level analysis or performance attribution. Data is aggregated at the account level without segmentation by conversation type, customer segment, or channel.

Provides starter templates for common industries (e-commerce, SaaS, restaurants, etc.) pre-populated with relevant FAQ responses and conversation flows. Templates include industry-specific terminology, common objections, and best-practice response patterns. Users can deploy a template in minutes and customize it for their specific business without starting from a blank canvas.

Detects when a customer query exceeds chatbot capability (e.g., complex billing issue, complaint) and routes the conversation to a human support agent with full context. The system maintains a queue of escalated conversations and notifies assigned agents via email or dashboard. The conversation history is passed to the human agent, enabling seamless context transfer without requiring the customer to repeat information.

Embeds a chat widget on customer websites via a single line of JavaScript code. The widget is fully customizable with brand colors, logos, welcome messages, and positioning (bottom-right, bottom-left, etc.). The widget maintains conversation state across page navigation and persists messages in browser local storage, enabling customers to continue conversations as they browse.

Accepts customer support emails and routes them through the chatbot, returning responses via email reply. The system maintains email thread continuity by parsing incoming emails and matching them to existing conversations based on sender address and subject line. Responses are sent as email replies, enabling customers to interact with the chatbot without visiting a website or chat interface.