Turning AI into products for customer service and process management.
We bring academic research together with real-world experience to build intelligent solutions that learn from your data and work as an extension of your team.
AI-powered capabilities that enhance both Customer Service and Business Process Management
Intelligent bots that handle conversations in chat channels — trained on your knowledge base, documents, and customer experiences. Resolve issues automatically or seamlessly hand off to agents with full context.
An LLM-powered agent assistant embedded in every step of customer service. It understands conversations, ticket context, and your policies — then proposes answers and next actions to help agents resolve faster.
Issue Intelligence first analyzes each issue and filters out noise; then, using what it has learned from historical issues and built-in categories, it determines the right category, priority, and routing decision.
Manage comments and complaints from written channels — email, social media, DMs, marketplaces, complaint sites, etc. — end-to-end automatically with Next4biz AI Agent, trained on your documents, website, experience, and web services.
Tag each ticket as positive/neutral/negative. Spot negative ones early and take action.
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Summaries by category highlight patterns, root causes, and fixes to reduce recurrence. More →
Predict how many tickets will come from each category; anticipate staffing needs and catch SLA risks early.
Learn more →Research on advanced sentiment analysis techniques for customer service applications, improving accuracy in understanding customer emotions and satisfaction levels.
Read ArticleResearch on Edge Information Assisted Decoder (EIAD) for business process anomaly detection, achieving improved F1-score from 0.32 to 0.63 using graph attention networks and edge-conditioned convolution.
Read ArticleIn the digital transformation era, efficient business process management depends on high-quality Business Process Management (BPM) solutions. These solutions naturally generate large amounts of data as process event logs—digital reflections of real-world operational flows.
Understanding anomalies in your processes is critical for operational excellence. Anomalies can indicate inefficiencies, bottlenecks, compliance issues, or potential risks that could lead to significant losses.
The upcoming Anomaly.net module will analyze your process logs using multi-graph structures, graph neural networks (GNN), and Transformer-based architectures to automatically detect anomalies, non-standard flows, and bottlenecks. It works with both design-time process models and production data, providing comprehensive insights across your entire process lifecycle.
Research & Development
Backed by cutting-edge research published in IEEE Access. The model achieves up to 22% better performance compared to state-of-the-art approaches, while reducing processing time by up to 60%.
Automatically convert natural-language process descriptions into BPMN 2.0-compliant workflows. Combines large language models (LLMs), knowledge graphs (KG), and graph neural networks (GNNs).
Our research-driven approach ensures that Next4biz products are built on cutting-edge AI technologies
Prepared by our R&D Center researchers in academic collaboration, SPECTRE is an unsupervised, multi-purpose autoencoder model that cleans erroneous data in business process event logs and detects process anomalies. Tested on 11 datasets, the study achieved up to 99.94% decontamination accuracy and a 0.996 F1 score in event-level anomaly detection, and was published in Cluster Computing, indexed by SCIE and Scopus.
Read the ArticleResearch on Turkish language processing, exploring how affixes contribute to text classification accuracy and improving NLP applications for Turkish-speaking markets.
Read ArticleResearch on enhancing chatbot performance through advanced training techniques and optimization methods, resulting in improved response accuracy and customer satisfaction.
Read ArticleNext4biz research team won the best paper award at ASYU 2024, recognizing our contributions to AI and natural language processing in business applications.
Read ArticleResearch published in IEEE Access on multi-graph structures and graph autoencoders for business process anomaly detection. Achieves up to 20% higher performance and 60% faster training and inference time using Transformer architecture.
Read ArticleThe most common questions about how the models are developed through Next4biz's own R&D, what data they are trained on, and how their accuracy is monitored after deployment.
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 →