Frequently Asked Questions

Next4biz CSM is an AI-powered, omnichannel system that manages the entire customer service process on a single platform, from first contact to resolution. Phone, email, chat, WhatsApp, social media, marketplaces and self-service come together on one timeline; requests that cannot be resolved fall into category-specific workflows with SLA tracking.

Help desk tools record the ticket and assign it to an agent; the process ends there. Next4biz carries the request end to end: the category-specific workflow starts, owners and time limits are assigned step by step, escalation is triggered on delays, and at closure a reason code and root cause analysis are produced. End-to-end request management →

Yes. ERP, OMS and CRM systems are connected bi-directionally through REST APIs and webhooks; order, product, policy and customer data become usable inside workflows and screens. On the call center side it works with CTI-enabled cloud and traditional switchboards. For identity, SAML 2.0, OAuth 2.0, OpenID Connect and LDAP/Active Directory are supported, with optional SCIM for user synchronization.

No. The category tree, forms, workflows, business rules, agent scenarios, Guided Bot scenarios and message templates are updated with visual editors, without writing code. IT is involved only at the integration layer. Changes are made under role-based permissions; who changed what and when is traceable. No-code structure →

We are not tied to a specific model; we regularly compare proven large language models and run the leading model of the period on the platform. Progress in the model world therefore reaches the product without requiring a migration project on your side. Model selection and on-premise options are explained in detail on the AI page. Our AI approach →

Yes, on-premise is possible. In the default setup the leading model runs as a managed service; if on-premise is preferred, a model that supports on-premise deployment runs on infrastructure you determine, under your organization's control. If data must not leave the country, model training is carried out on our servers in that region. The options are detailed on the AI page. Our AI approach →

First contact resolution (FCR), average handling time (AHT), SLA compliance, escalation rate and customer satisfaction are tracked in standard dashboards. Thanks to the omnichannel structure these metrics are calculated without channel-level double counting. Through BI integration, ready-to-use APIs are available for Power BI, Tableau and Qlik.

next4biz CSM runs in the data center the customer chooses: the EU, the UK, the US or Türkiye. The application and its data are installed and processed in the chosen location; for organizations that choose Türkiye, both stay in Türkiye. Backups are kept within the same country or region, at a separate site. Data centers →

GDPR and KVKK compliance is committed to in writing, and the results of independent security audits are shared with customers. The platform is certified to ISO/IEC 27001, ISO/IEC 27701, BS 10012, ISO 22301, ISO 10002 and ISO 9001. Data stays in the location you select, and if you prefer, you can run it on your own systems with an on-premise license. Security and compliance →

It is a ready product; it arrives with rich functionality out of the box, then is adapted to your organization and integrated. Adaptation consists of configuring categories, forms, rules, flows, permissions and channel settings — code is needed only for external system integrations. Deployment is run with a project plan and roadmap alongside professional project managers; hands-on workshops and training make the teams' competence permanent. The intended benefits are defined together in the first meeting and tracked in built-in dashboards. Implementation roadmaps and training →

In multichannel every channel produces its own conversation and its own record; the customer repeats themselves and teams work from fragments of information. In omnichannel all channels merge into a single live conversation; history, files and actions stay under one customer and one ticket. Even when the customer moves from WhatsApp to the phone, the agent sees the entire journey.

Through identity resolution. The same person is identified across email addresses, phone numbers, social media accounts and customer IDs; interactions are automatically linked to the right customer and the right ticket. Repeat submissions about the same subject are deduplicated, so no double counting appears in reports.

Live chat and mobile app, WhatsApp, email, call center, social media (Facebook, Instagram, X, LinkedIn), review platforms (Google, App/Play Store, Trustpilot, Şikayetvar), marketplaces (Trendyol, Hepsiburada, Amazon, N11) and the self-service portal. On written channels AI Agents monitor the source, filter out irrelevant content and turn valid messages into tickets. Written channels → To compare channel coverage with help desk tools: next4biz as a Zendesk alternative →

No. The ticket, the SLA clock, the forms collected, the attachments and the approvals all continue on the same case even when the channel changes. When a customer starts in chat and moves to the phone, the agent or AI agent taking over sees the previous conversation and the research already done; the customer is not asked to explain the same thing again.

No. Sentiment analysis, summaries, similar-case detection and policy guidance work as a single AI layer across all channels — not as separate bots per channel. Category-specific forms and workflows are also applied independently of the entry channel, which is why bringing a new channel online does not mean building the setup from scratch.

The business team. Message templates for email, chat, social media, marketplace and review replies; intake and resolution forms; validations and form rules are all configured without writing code. Approved templates and a consistent tone are managed from the same editors across every channel; a change takes effect everywhere at once, regardless of channel.

It works integrated with your CTI-enabled cloud or traditional switchboard; you do not need to replace it. The Supervisor Bot recognizes the caller as the phone rings and brings open tickets from all channels, customer data from your ERP, sentiment analysis and the CSAT score to the screen. During the conversation Issue Intelligence categorizes the request and the Supervisor Bot guides the agent into the category-specific scenario. Where appropriate the answer is prepared from the knowledge base and integrations and the ticket is concluded; if not, the right workflow is started in a single step. AI in the call center →

Volume, average handling time, first contact resolution and NPS are calculated without channel-level double counting. Because a submission the same customer made across three channels does not appear as three separate tickets, you see category and team performance as it really is; root cause analysis rests on that deduplicated data.

A ticket that cannot be resolved at first contact is routed into the workflow defined for its category. The flow consists of as many sequential steps as needed; each step has a responsible team or person, the data to be collected, a quality checklist and a time limit. Conditional routing branches between steps — for example a faster path for a VIP customer.

Help desk tools capture the call and forward the email to a department; sometimes they assign it with a keyword rule and call that a "workflow". Next4biz carries the request through to resolution: it collects the right data, verifies the status from integrated systems, selects the corrective path according to policy, closes with a reason code and metrics, and then feeds root cause analysis.

Service levels are tracked both per step and end to end. Alert messages are sent as the deadline approaches; when the threshold is exceeded, hierarchical escalation is triggered and the team lead is notified with full context. Managers see in the dashboard which stage each ticket is at, where it is delayed and which actions have been taken.

Yes. Through ERP, OMS and CRM connections, order status, product information and the customer record can be verified inside the workflow. In a delivery delay flow, for example, the shipment status or proof of delivery (POD) is checked through the integration and the return, exchange or reshipment path is selected according to the policy check.

No. Steps, owners, durations, forms, checklists, approvals and business rules are defined in the visual designer without writing code. A change can be applied to a single category or to an entire line of business; the definitions are managed from one place together with the category tree. When a regulation or policy changes, the system is adapted without waiting for a vendor request. No-code structure → For a comparison with platform-based tools: next4biz as a Salesforce Service Cloud alternative →

You correct it immediately from the same visual editor. Changes can be tested and previewed before going live, and published to specific categories or to all lines of business separately. Who changed what and when is tracked in the change log; role-based permissions determine who can make changes. No-code structure →

No. The category tree and the workflows attached to it are extended according to your organization's needs; each category is configured separately with its own form, SLA, business rules and escalation chain. Adding a new category is not a new project but a definition task in the existing editors; a shared change is applied to selected categories or to the whole line of business in one go.

Step-level SLA compliance, delayed steps, the number of escalations and category-level resolution times are tracked in dashboards. The reason codes assigned at closure feed root cause analysis; for recurring problems, corrective and preventive actions (CAPA) are triggered — the problem is not just answered, it is fixed. Maturity test →

Every layer of the operation that changes day to day: the category tree, workflows with SLAs and escalations, forms and form rules, business rules, agent scenarios, Guided Bot scenarios, message templates, reports and analytics. All of it is managed from visual editors; business teams can change it without raising a software development request.

For day-to-day changes, yes: form, flow, rule, scenario and template updates stay with the business team and require no vendor change request. IT stays involved at setup and at the integration layer — SSO, ERP/OMS/CRM connections, role-based access policies. In the words used on this page: no line of code is written except for integrations. For a comparison with tools that need a platform project: next4biz as a Dynamics 365 Customer Service alternative →

A staff member who can document business processes can design or change a process; the visual editors make configuration intuitive. No software knowledge or consultancy is required — an operations lead can, for example, define the form and the workflow for a new category themselves. During the adaptation phase, teams are given onboarding, hands-on workshops and training. Implementation and training →

Permissions and the change log limit that risk. Role-based permissions define who can change what; who changed what and when is traceable. A faulty configuration is corrected immediately from the same editor and the correction appears in the product straight away. To test and preview workflow changes before publishing them, see the end-to-end request management page. Workflow changes →

No, that is IT's domain. Core systems (SSO, ERP, OMS, CRM) are connected once to the Next4biz Inventory. Once the connection is established, business teams can use that integrated data in workflows, forms and screens without writing code. In other words the integration is built once, and after that the business side is free to move.

In parameterized software you only tick predefined options. In a no-code architecture the business logic itself is built visually: the category tree, forms and data fields, workflows, SLAs and escalations, if/then business rules, scenarios, templates, permissions and reports are designed by the business unit. A new campaign, a regulatory change or a new product goes live as a new category and flow, without waiting for a quote from the vendor. Why Next4biz →

Routine change requests drop; IT focuses on security, data and integration. Some integrations are ready out of the box: communication channels (email, WhatsApp, social media, marketplaces, review platforms) and pre-built connectors for common enterprise software. IT connects core systems such as SSO and ERP/CRM, plus any organization-specific systems, once; business teams then use that data without touching the integration. Role-based access and the change log are built in, and because vendor change requests decrease, total cost of ownership falls.

The most visible indicator is how quickly a new category, campaign or policy goes live. Alongside it: the number of change requests raised with IT, the number of development requests sent to the vendor, and the time taken to comply with regulatory changes. Because changes are published uniformly, inconsistency between brands also decreases.

At three stages of the customer journey. At capture: bots run chat and WhatsApp, PRIME filters out the noise, Issue Intelligence categorizes. At resolution: the Supervisor Bot offers the agent a draft reply and policy guidance. At analysis: sentiment analysis, churn score, category summary and ticket forecasting are produced. For a comparison where AI is part of the package rather than an add-on: next4biz as a Zoho Desk alternative →

The rule-based Guided Bot provides predictability and auditability: what is asked in which category is defined, and real data is pulled from integrations. The LLM Bot takes over when the flow stalls or the customer goes outside the scenario. Together they deliver a consistency and traceability that an LLM alone cannot offer.

PRIME (Priority Ranking & Interaction Management Engine) listens to every written channel and decides at the door whether a piece of content is really a ticket that needs handling. Emojis, short contextless content and misdirected emails are filtered out; every valid message is assigned a type and an operational priority. PRIME →

Your organization's knowledge base, product manuals, internal documents, past resolutions and live data from integrated systems (orders, products, SLAs). Issue Intelligence also learns from past tickets and your existing category tree to classify consistently across all channels. General internet knowledge is not a source for answers. Issue Intelligence →

The business team manages it. The category tree, Guided Bot scenarios, message templates and business rules are defined with visual editors; model training and the infrastructure side stay with Next4biz. You do not need to hire a data scientist or learn to write prompts; monitoring and improving model quality is also carried out by Next4biz. Implementation and training →

We are not tied to a single model. We continuously evaluate well-known, proven large language models, determine the leading model for each period and run that one on the platform. This approach means leaps in model performance reach the product without requiring a migration project on your side.

Yes. We offer three options. By default the leading model runs as a managed service. If on-premise is requested, one of the models that support on-premise deployment is run on infrastructure you determine, under your organization's control. If data must not leave the country, it is stored and processed locally, and we run model training on our own servers hosted in that region without requiring hardware from you. Data centers and certificates →

The Guided Bot only moves within defined scenarios and pulls real data from integrations, so there is little room to make things up. When the LLM steps in and the flow stalls, the conversation is handed to a human agent with its full history. Every step — bot prompts, user input, AI suggestions, actions taken — is recorded and auditable.

Tickets resolved by the bot and those transferred to an agent are reported separately; the effect on first contact resolution and average handling time is tracked. The Category Summary shows which problem recurs, and ticket forecasting shows the load of the coming period. The intended benefits are defined together at the start of the project and tracked in the same dashboards. Category Summary →

It means a request is received, filtered, classified, resolved where possible and routed to the right workflow when it cannot be — without human intervention. AI bots handle chat, WhatsApp and self-service; written-channel agents monitor email, social media, reviews and marketplaces and turn them into tickets. Workflows take over the rest.

A chatbot runs the conversation and stops there. An autonomous setup covers what comes after it as well: it categorizes the ticket, pulls order and customer data from integrated systems, selects the corrective path according to policy checks, starts the SLA clock and triggers escalation when needed. The agent who takes over sees the full context.

Auto-Responder handles this. As the ticket moves through the workflow every development is written to the ticket history; Auto-Responder analyzes that history and the current status to decide when the customer should be updated, and informs them automatically on their preferred channel at those points.

Automatic resolution requires access to real data: ERP/OMS for orders and stock, CRM for the customer, SSO/LDAP for identity, channel services for communication. These connections are established bi-directionally through REST APIs and webhooks; the data then becomes usable inside both the bots and the workflows.

Yes. Guided Bot scenarios, the category tree, business rules, forms and workflows are configured with visual editors, without writing code. What the bot asks in which category, how far it goes and at which point it hands over to an agent are all determined by scenarios the business team defines. No code is written except for integrations.

Yes. Churn Analysis produces a graded satisfaction score by taking into account the sentiment data across all of a customer's tickets. Customers at risk of dissatisfaction or likely to be lost are spotted early through this score, and automated actions can be started for them. AI Analyzer →

We are not tied to a single model; proven large language models are monitored and the leading model of the period is used, and switching requires no project on your side. The rule-based Guided Bot and the LLM Bot work together: the flow handles the predictable part, and the LLM takes over when the conversation goes outside the scenario. More on our AI approach →

Yes, there are three options. By default the leading model runs as a managed service. If on-premise is preferred, a model that supports on-premise deployment is run on infrastructure you determine, under your organization's control. If data must not leave the country, it is stored and processed locally, and training is carried out on our own servers in that region without requiring hardware from you. Security and compliance →

Tickets resolved by the bot, transferred to an agent and routed into a workflow are reported separately; you can see in which categories automation holds and where it stalls. Sentiment analysis, ticket forecasting and category summaries show the areas where you can extend autonomy. Reports are followed in built-in dashboards and can also be exported to external BI tools. AI Analyzer →

Yes. Every ticket's history records who opened it, how long it waited at each step, who wrote what, and which type, category and sentiment prediction the AI made with which confidence score; whether the agent corrected that prediction is visible too. An auditor can reconstruct the entire process from a single ticket; this trail feeds quality management, compliance and agent training together. Maturity test →

Bots and agents do not work with general internet knowledge; they work with the organization's own documents, knowledge base, past resolutions and live data pulled from integrated systems through APIs. Answers rest on those sources, and they are traceable and auditable; new or changed documents can be added after training as well. We have collected these principles in a written framework. Responsible AI Framework →

Issue Intelligence analyzes the ticket that has passed PRIME's initial filter; using what it has learned from past tickets and the organization's category tree, it produces the category, priority and routing decision. The ticket's type, subject and context are determined automatically, so the agent does not have to pick a category by hand. The filtering at the door belongs to PRIME, classification and routing to Issue Intelligence. PRIME →

Your own category tree is used; there is no requirement to conform to a predefined scheme. Issue Intelligence places tickets into categories defined the way your organization works and combines that with what it has learned from past tickets. Your existing tree is preserved and classification learns against it; when category definitions change, no new project is needed — the definition is updated in the no-code editor.

Yes. The moment the category is assigned, the ticket is routed to the team, the SLA and the workflow attached to that category; the actions predefined for the category start and the question "who should we assign this to?" disappears. The same problem being assigned to different categories by different agents, and the ticket waiting with the wrong team, are prevented; reports become reliable as well. Team, SLA and flow definitions are managed on the workflow side. Workflow and SLA →

No; whatever the source, the same classification logic is applied. Tickets from email, live chat, WhatsApp, social media, review platforms, marketplaces, the call center and self-service portals are evaluated on a single learning categorization foundation. That creates a shared language instead of different interpretations by different teams, and reduces manual categorization effort and the operational errors that come with it. How the channels are connected is explained on the omnichannel page. Omnichannel →

Yes; every category an agent corrects improves the model's next decision. The corrected example is fed back into training, and as corrections accumulate Issue Intelligence adapts better to your organization's own ticket language and category tree. You can see whether this loop is working in your organization with the maturity test. Maturity test →

No. PRIME (Priority Ranking & Interaction Management Engine) decides at the door whether incoming content really is a customer ticket that needs handling: it listens to every written channel, filters out the noise or marks it low priority, and assigns a type and a priority to valid messages. Tickets that pass the door are categorized by Issue Intelligence and routed into the relevant workflow. Issue Intelligence →

Emojis, short contextless content, irrelevant messages from automated email lists, newsletter copies and misdirected emails count as noise. That content is either filtered at the door or marked as low priority; every decision is produced with a confidence score and stays on record, so it is auditable and the agent can correct it when needed. As a result only real work stays in the team's queue, and genuine complaints do not wait in line behind the noise. PRIME triage →

Priority is assigned as urgent, high, normal or low by weighing the tone, subject and impact of the content together. In the same step the message is typed as a complaint, request, information, thanks or spam. The decision does not rest on a single signal: rule-based features, a transformer sentiment model and LLM context evaluation combine into one decision, and every result carries a confidence score. This priority assigned at intake becomes the input for the dynamic priority that is recalculated in the workflow according to waiting time and repetition. Why Next4biz →

All written channels are covered: support email addresses, social media comments, mentions and direct messages, marketplaces and review platforms. The same pre-filtering layer works with the same logic on every channel; in email it filters out auto-replies and newsletter copies, in social media emojis and short contextless comments. When a flood of messages arrives on campaign days, the team works with real tickets sorted by priority rather than with noise. You can measure your own channel coverage with the written channels section of the maturity test. Maturity test →

No; PRIME starts working with a general model and ready-made rules. If you wish, it can be trained before go-live with your own historical data or the records of your previous system. Adaptation to your organization continues in use: as examples the agents confirm and correct accumulate, the filtering and prioritization decisions adapt to your organization's own message traffic, and the agent's correction stays visible in the record. The principles on the scope in which training data is used are set out in a written framework. Responsible AI Framework →

The Category Summary (InsightX) answers the question "what is happening most?" by reading the complaints in each category together with the resolutions given to them. Instead of going through tickets one by one, it surfaces the patterns that recur in that category, highlights possible root causes and offers preventive improvement suggestions to reduce repetition. The result is a readable summary that management and quality teams can turn directly into action. AI Analyzer →

A volume report shows how many tickets have accumulated in a category; the category summary explains why they arrived. Instead of numbers, the complaint texts and resolution records are read as content, and the result is presented as an interpreted summary. The question "what is happening most?" is then answered with data rather than impression, and a shared language forms between teams for SLAs, team capacity and process improvements.

Yes. The summary is written in language that management and quality teams can read and act on directly: the recurring patterns in the category, the possible root causes behind them and suggestions for preventing repetition. We can go through how it would look with your own category tree and your own data together in a demo session. Schedule a demo →

Yes. The Category Summary is one of the AI analyzers inside Next4biz CSM and works complementarily with the others at category level: sentiment analysis labels every ticket and message and raises an alert for conversations turning negative, ticket forecasting shows the coming period's volume and SLA risk in advance, and the category summary explains the reasons behind that volume. The summaries are used alongside BI dashboards, so you have interpreted insight next to the numerical indicators. Analyzers →

It monitors email, marketplaces, social media and review platforms. Support inboxes; the question-and-answer areas of marketplaces such as Trendyol, Hepsiburada, Amazon and N11; comments, mentions and DMs on Facebook, Instagram, X and LinkedIn; review sites such as Google, the App/Play Store, Trustpilot and Şikayetvar are all in scope. It filters out irrelevant content, automatically categorizes valid messages and turns them into tickets, prepares a reply in your brand's language and, where needed, starts the relevant workflow.

First filtering, then classification, then the reply. Incoming content passes through PRIME first; noise is filtered out there or marked low priority. The remaining message is placed into the right category in your organization's category tree, becomes a ticket and receives a sentiment label. A draft reply is then prepared, or the workflow attached to the category is started; the agent steps in with a ready draft when needed. PRIME →

On your organization's own sources: your documents, your website, your past resolution experience and your web services. It does not produce answers from general internet knowledge; the answers rest on those sources and are auditable. Answer quality therefore depends on how current the documentation feeding it is. Which language model is used and how your data is processed is explained on the AI page. Our AI approach →

The organization decides, and the decision is made per category. In low-risk categories such as shipment status the reply goes out directly and the ticket closes; in sensitive categories the AI Agent only prepares a draft, and the agent previews it, edits it if necessary and approves it; in some categories it only classifies and prioritizes. Every AI decision is recorded in the ticket history together with its confidence score; the agent's corrections stay visible and improve the model. Maturity test →

Yes. For a question whose category is clear, the AI Agent prepares the answer using the knowledge base, approved templates and data in ERP, CRM or OMS (order status, shipment check); on marketplaces and review platforms it posts the reply back to that platform. Where a transaction such as a return or an exchange is required, the ticket is handed to the relevant workflow and routed to the right team. Even on tickets that reach an agent, the screen is not empty; the agent works from a prepared draft.

Yes. Every ticket the AI Agent creates receives an AI sentiment label, regardless of channel. When a conversation turns negative the ticket is moved forward and, if needed, an alert goes to a manager, so an unhappy customer does not wait in the queue. How sentiment data is aggregated at customer level and turned into churn risk is explained under Churn Analysis. Churn analysis →

No; the bot answers any question that has an answer in your organization's knowledge. The Next4biz Hybrid LiveChat Bot is trained on your knowledge base, documents, website, product manuals and past resolution experience, and produces answers in your corporate language. Whether the question is frequently asked or not makes no difference. The same bot works with the same knowledge on the website, in an authenticated web application and on WhatsApp. Maturity test →

Yes; the bot queries status from your integrated systems, creates a new ticket, updates an existing one and concludes suitable requests end to end within the chat. When approval, a missing document or another team's step is required, it starts the relevant workflow itself and hands the conversation over to an agent with its context. That prevents the pattern where bots that can only inform push the customer back into the queue a step later. Request management →

The split is made according to the nature of the request. Standard, verifiable transactions run through rule-based Guided Bot flows; free text and more complex requests bring the LLM in; situations that need approval or an exception are taken over by a human agent. The three layers work inside the same conversation, the context is not fragmented and the customer experience stays uninterrupted. We explain the rationale for the hybrid approach in detail on the AI page. Our AI approach →

No. The agent takes over on a single screen with the full transcript, customer information, transaction details, related records and attachments, sentiment and satisfaction signals and the suggested next step. The category the bot determined and the information it collected are on that screen too. The handover happens inside the same conversation; the customer does not repeat their details and the agent continues from where things stood. How the bot works →

It works in both. The bot is embedded into your website, into web applications entered through SSO, and into your native mobile app with the Mobile SDK. On mobile you are not limited to a web view: with the native SDK and visual components the support experience becomes a natural part of your app. Customers get support in a familiar interface without switching apps, and the handover to an agent continues inside the same conversation. Omnichannel →

Three layers come into play in sequence within the same WhatsApp conversation: the Guided Bot runs standard, verifiable transactions step by step, the LLM understands and answers complex requests that arrive as free text, and a human agent takes over at the point where approval or an exception is needed. The transitions are not felt by the customer and the context is preserved. We explain the rationale for the hybrid approach on the AI page. Why hybrid? →

It is not limited to giving information; it concludes the request inside WhatsApp. Pulling transaction history and related records from your integrated systems, it checks status, creates or updates a ticket and starts the workflow that is needed. The customer writes whenever they want and gets an answer without depending on office hours. At the point where a human is needed, the conversation is handed to an agent inside the same WhatsApp chat with its full history and context; the customer explains nothing from the beginning. Capabilities of the hybrid bot →

It does not. The WhatsApp bot is fed by the same knowledge as the bot on your website: your organization's documents, website content, past experience and web services. Answers are produced in your corporate language; the bot does not invent an answer to a question the organization's knowledge does not cover, it hands it to an agent. The whole training and data approach is on the AI page. Our AI approach →

Yes. Even when the channel changes, conversation and case continuity is preserved; the context travels with the customer journey, and history, files and actions stay under one ticket. The customer does not switch apps, and your team manages WhatsApp together with the other channels from a single screen. How the channels are managed together is explained on the omnichannel page. Omnichannel →

The Supervisor Bot is an LLM-based agent assistant embedded in every step of customer service. It understands the conversation, the ticket context and company policy; without the agent leaving the conversation, it brings possible causes, step-by-step procedures, policy nuances and warnings into view. It suggests the questions that should be asked, points to the relevant knowledge articles and drafts a reply or an internal note. The same support is offered to supervisors and back-office teams. How it works →

Suggestions are produced from your organization's own sources, not from general internet knowledge: the knowledge base, product manuals, internal documents, past tickets and what the customer experience team has learned. The assistant can be trained to capture patterns in similar cases, so the guidance fits how your business actually works. Because the sources belong to the organization, every suggestion is auditable. See the AI page for the language model used and the data policy. Our AI approach →

The final word always belongs to the agent. The Supervisor Bot prepares the reply or the internal note as a draft; the agent edits it, approves it or does not use it. This is a deliberate design choice: the assistant is not a bot talking to the customer but a guide standing behind the agent. It flags risky steps and checks that must not be skipped, but responsibility for the text that reaches the customer stays with a person. For categories that need automated replies on written channels, the AI Agent is a separate component. Written channels →

By turning the knowledge base from somewhere the agent has to search into an assistant standing next to them. Under pressure an agent does not search; they write what they know. Because the Supervisor Bot brings the correct procedure and policy rule into view at that moment, answers stay consistent and compliant regardless of who is answering. Learning from past resolutions, the assistant carries experienced agents' accumulated knowledge into organizational memory, and new agents become competent faster. You can measure where you stand with the complaint management maturity test. Maturity test →

No; alongside the front-line agent, the Supervisor Bot supports supervisors and back-office teams in the same way: the draft reply and the policy guidance arrive with the same context in every role. It also works in the call center: it recognizes the caller as the phone rings and surfaces the relevant scenario during the conversation; the detail is on the omnichannel page. So no matter which channel the ticket arrives from, the same policy guidance is used at every step of the resolution. Call center →

It is our own R&D. Next4biz operates as a registered R&D center, with PhD-level and professor-titled researchers on its staff. The work has been published in IEEE Access and in Springer Nature's Cluster Computing journal, and has won best paper awards at the ASYU and UBMK conferences. That research finds its way into the product: sentiment analysis, Issue Intelligence and the forthcoming Anomaly.net module are outputs of these studies. Building a prototype is easy; productizing it with data cleaning, model selection, multi-criteria evaluation and observability layers is the real work. Why Next4biz →

No, the data is cleaned before it touches the model; a strong model cannot compensate for low-quality data, it learns the errors in it as well. First a language and meaning filter removes the noise. Then repeated examples are cleaned out by semantic similarity; the aim is not to shrink the volume but to prevent the same example landing in both the training and the test set and distorting the measurement — that is, to prevent data leakage. Class imbalance is corrected only in the training set; the test set keeps the real-world distribution so that the results reflect the field.

It is not enough; a correct output is not proof of correct learning. The "Clever Hans effect" in machine learning describes this: the horse that appeared to solve arithmetic was actually reading people's body language. So in evaluation we do not settle for a single accuracy score; the model's errors are read in a confusion matrix. If two categories are constantly confused, or the model retreats into catch-all categories such as "General", the problem may lie in the category definitions; those findings are handed back to you as a proposal for an ideal category tree.

No project starts with a single architecture. Transformer classifiers, embedding-based structures, locally adapted models and hybrids compete on the same training and validation splits; the winner goes into production as the "champion model". The criterion is not overall accuracy alone: per-class recall, confidence calibration, latency, model size and running cost are all weighed together. Experiments are designed to be reproducible. Which language model runs on the platform is answered on the AI page. Our AI approach →

It can; the model stays fixed while the world changes, so going into production is the beginning of the work, not the end. Different kinds of drift are monitored: data drift (the language of complaints changing after a new mobile app release), concept drift ("my card doesn't work" shifting from a physical card to a digital payment problem) and taxonomy drift (a growing category needing sub-branches). The monitor, detect, find the root cause, improve loop runs continuously; retraining is only one of the options. Decisions the agents correct are fed back into the model as well.

In retrieval-augmented generation (RAG), finding the right passage determines the quality of the answer; multilingual embedding models remain weaker on non-English text. A study our R&D team presented at ASYU showed that translating documents into English first and producing the embedding vectors over the translation — while keeping the original text — improves context precision and recall. This approach is used in Next4biz chatbot systems. Summary of the study →