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.
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.
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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
Research 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.
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