For Customer Service
Hybrid (Guided + LLM) bot with context-preserving handoff.
Drafts replies, next steps, and sources.
Email, social, reviews; classify and draft replies.
Predicts category, validates inputs, resolves or starts a workflow.
History and sentiment surfaced in the scenario.
Tag every ticket; surface shifts and alerts.
Spot patterns, root causes, fixes.
Concise conversation & resolution handovers.
Forecast volumes and SLA risks early.
Next4biz AI works at every stage — from initial customer contact to resolution and insights. Bots handle conversations, agents get intelligent assistance, and AI analyzes patterns to improve service quality.
AI bots handle live chat and WhatsApp. AI Agents monitor and respond on written channels. Issue Intelligence classifies, categorizes, and routes to flows.
Supervisor Bot helps agents with real-time suggestions, policy guidance, and action recommendations.
Sentiment analysis, ticket summaries, category insights, and ticket predictions provide actionable intelligence for continuous improvement.
Intelligent bots that handle conversations across channels — trained on your knowledge base, documents, and customer experiences. Resolve issues automatically or seamlessly hand off to agents with full context.
Messages from live chat are handled by either an LLM Bot—grounded in your documents, knowledge base, product manuals, and real CX history—or a rule-based Guided Bot. The bot can complete many requests end-to-end (answer, status check, update, ticket creation). When a case requires an approval, missing document, or another team, it starts the right workflow and hands the same conversation to an agent with full context.
Deploy the same bot on your website, inside your authenticated web app via SSO (so it starts with user context), and in your mobile app using our SDK with ready-made UI components for quick reuse.
When agents take over, they see the full bot conversation, rich customer context (orders, products), the complete ticket history across channels/departments/parties, plus sentiment analysis and the customer satisfaction score — ensuring a smooth, informed handoff.
Conversations on WhatsApp are handled by either an LLM Bot—grounded in your documents, knowledge base, product manuals, and real CX history—or a rule-based Guided Bot. The bot can complete many requests end-to-end (answer, status check, update, ticket creation). When approvals, missing documents, or cross-team steps are needed, it starts the right workflow and hands off with full context.
The same customer context you use elsewhere — orders, products, prior tickets across all channels/departments/parties — flows into WhatsApp. When an agent takes over, they see the full WhatsApp thread, consolidated context, plus sentiment and the customer satisfaction score for a smooth, informed handoff.
An LLM-powered assistant embedded in every step of customer service and resolution. It understands intent, ticket context, and your policies — drafting answers, proposing next steps, and surfacing the right knowledge so agents, supervisors, and back-office teams resolve faster with confidence.
Grounded in your documents, knowledge base, past resolutions, and integrated data (orders, products, SLAs), it suggests actions that respect workflows and compliance, highlights risks, and keeps every response on-brand and auditable.
AI agents that monitor channels, categorize tickets, draft responses, and trigger workflows — so handling stays consistent across every touchpoint.
Customers access self-service via website or mobile app (visitor or SSO-based). AI presents ticket history, orders, and products to answer questions proactively.
Agents receive comprehensive customer data — orders, products, full ticket history — plus sentiment analysis and customer satisfaction scores.
Monitors product-page Q&A sections on Trendyol, Hepsiburada, Amazon, N11 — filtering irrelevant entries and converting relevant questions into tickets.
Monitors Facebook, Instagram, X (Twitter), LinkedIn — including comments, mentions, and DMs. Filters irrelevant content and converts relevant items into tickets.
Monitors Google, Apple/Play stores, Trustpilot, Şikayetvar. Filters irrelevant posts and converts relevant reviews into categorized tickets.
Monitors designated support inboxes, filtering irrelevant messages and converting relevant emails into categorized tickets.
AI-powered analysis that turns customer interactions into actionable insights — from sentiment tracking to predictive forecasting.
Learns deeply from past notifications and established categories, then automatically categorizes incoming tickets from every channel. This feature works seamlessly across all communication channels — email, live chat, WhatsApp, social media, review platforms, marketplaces, phone calls, and self-service portals.
Uses advanced content analysis to ensure consistent classification across all touchpoints, reducing manual categorization effort and eliminating human error. The system applies the same intelligent categorization logic regardless of where the customer inquiry originates.
This intelligent classification helps streamline ticket routing, improves response efficiency, and ensures that each ticket reaches the right team with the correct priority and context.
Analyzes the emotional tone (positive, negative, or neutral) of each message to determine customer satisfaction. Sentiment tags are attached to every ticket and accessible to all users, enabling proactive actions and comprehensive analysis.
Presents the complete ticket lifecycle — from initial communication across channels to resolution history — in a concise summary. Provides users from various departments with a quick and clear understanding of the ticket's journey and current status.
Analyzes and summarizes complaints and their resolutions by category. Identifies repeating issues, root causes, and offers actionable suggestions for preventive improvements to enhance service quality and reduce recurring problems.
Forecasts the expected number of tickets in each category for upcoming periods using historical data and trends. Helps organizations plan human resources, allocate workloads, and set appropriate SLAs to maintain service quality.
The best of both worlds: structured flows for predictable tasks, AI intelligence for free-form questions. One bot that adapts to each conversation.
Higher first-contact resolution: Guided flows collect the right data; LLM fills the gaps when customers type freely.
Lower handling time: No back-and-forth for missing info; automations can resolve instantly when conditions are met.
Fewer escalations: LLM clarifies edge cases; only complex exceptions reach agents—with all context attached.
Audit-ready and consistent: Every step (bot prompts, user inputs, AI suggestions, actions taken) is logged and traceable.
Omnichannel continuity: Customers can switch channels without repeating themselves; agents pick up where the bot left off.
AI capabilities integrated throughout the entire customer service lifecycle — ensuring consistent quality, faster resolutions, and continuous improvement.
LLM Bots on live chat & WhatsApp: Handle messages using your documents/KB; resolve many requests end-to-end, and when approvals or other teams are required, start the right workflow and hand off with full history and context.
Self-Service portal (web/app): Customers track previous tickets/operations; AI shows a brief history of their tickets, orders, products, and services—aiming to answer before they ask.
Auto-category & smart intake: AI predicts the ticket category and, when needed, requests the missing information and starts the relevant workflow.
Knowledge Base search: Users can search; as authorized users update content, the bot's answers improve.
Supervisor Bot: An LLM trained on your documents and policies supports agents during resolution.
Call Center AI Agent: While the call is active, AI identifies the category. If there's a ready solution, it prepares a template-based reply and sends it to the customer; if not, the AI shows the correct, category-specific form and can start the predefined workflow.
Channel Agents (Email, Social, Marketplace, Review): Monitor designated sources, skip irrelevant content, turn valid messages into tickets, auto-categorize, fetch the right customer/order info, draft or post the reply, and close the ticket. Each ticket is tagged with sentiment.
AI Sentiment Analysis: Derives customer satisfaction by analyzing emotions across interactions; stores a sentiment tag for analysis and proactive actions.
AI Ticket Summarization: Condenses the full conversation and actions so agents and supervisors grasp "what happened / what's next" quickly.
AI Category Summarization: Reviews complaints and resolutions by category, highlights common issues/root causes, and suggests preventive improvements.
AI Ticket Prediction: Forecasts the number of tickets per category for upcoming periods to help plan human resources and SLAs.
Correct category, correct form, and ready solutions/templates reduce back-and-forth.
Faster categorization, smart intake, and automated resolutions speed up service.
Category-specific forms and predefined workflows keep resolutions uniform across channels.
Sentiment, summaries, category insights, and forecasts guide proactive fixes and staffing.
Omnichannel continuity: Whether it starts in chat, WhatsApp, email, social media, review sites, marketplace Q&A, self-service, or the call center—conversation and actions remain consistent within the same ticket.