David Farr1, Iain J. Cruickshank2, Lynnette Hui Xian Ng2, Nico Manzonelli3, Nicholas Clark1, Kate Starbird1 , and Jevin West1, 1University of Washington, 2Carnegie Mellon University, 3Cyber Fusion and Innovation Cell
Assessing classification confidence is critical for leveraging Large Language Models (LLMs) in automated labeling tasks, especially in the sensitive domains presented by Computational Social Science (CSS) tasks. In this paper, we apply five different Uncertainty Quantification strategies for three CSS tasks: stance detection, ideology identification and frame detection. We use three different LLMs to perform the classification tasks. To improve the classification accuracy, we propose an ensemble-based UQ aggregation strategy. Our results demonstrate that our proposed UQ aggregation strategy improves upon existing methods and can be used to significantly improve human-in-the-loop data annotation processes.
uncertainty quantification, large language models, stance detection, ideology identification, frames detection, ensemble models.
Thibault Rousset1, Taisei Kakibuchi2, Yusuke Sasaki2, and Yoshihide Nomura2, 1School of Computer Science, McGill University, 2Fujitsu Ltd.
This paper investigates the integration of technical vocabulary in merged language models. We explore the knowledge transfer mechanisms involved when combining a general-purpose language-specific model with a domain-specific model, focusing on the resulting model’s comprehension of technical jargon. Our experiments analyze the impact of this merging process on the target model’s proficiency in handling specialized terminology. We present a quantitative evaluation of the performance of the merged model, comparing it with that of the individual constituent models. The findings offer insights into the effectiveness of different model merging methods for enhancing domain-specific knowledge and highlight potential challenges and future directions in leveraging these methods for crosslingual knowledge transfer in Natural Language Processing.
Large Language Models · Knowledge Transfer · Model Merging · Domain Adaptation · Natural Language Processing.
Gianni Jacucci, University of Trento, Department of Information Engineering and Computer Science, Italy
Current AI models, particularly large language models (LLMs), are predominantly grounded in positivist epistemology, treating knowledge as an external, objective entity derived from statistical patterns in data. However, this paradigm fails to capture "facts-in-the-conscience", the subjective, meaning-laden experiences central to human sciences. In contrast, phenomenology hermeneutics and constructivism, as fostered by socio-technical research (16), provide a more fitting foundation for AI development, recognizing knowledge as an intentional, co-constructed process shaped by human interaction and community consensus. Phenomenology highlights the lived experience and intentionality necessary for meaning-making, while constructivism emphasizes the social negotiation of knowledge within communities of practice. This paper argues for an AI paradigm shift integrating second-order cybernetics, enabling recursive interaction between AI and human cognition. Such a shift would make AI not merely a tool for knowledge retrieval but a co-participant in epistemic evolution, supporting more trustworthy, context-sensitive, and meaning-aware AI systems within socio-technical frameworks.
AI epistemology, Large Language Models(LLMs), Consensus Domain, Human-AI Interaction, Structural Coupling.
Ahmad Mahmood1, Zainab Ahmad1, Iqra Ameer2, and Grigori Sidorov1, 1Instituto Politécnico Nacional (IPN), Centro de Investigación en Computación(CIC), Mexico City, Mexico, 2Division of Science and Engineering, The Pennsylvania State University, Abington, PA, USA
The development of medical question-answering (QA) systems has predominantly focused on high-resource languages, leaving a significant gap for low-resource languages like Urdu. This study proposed a novel corpus designed to advance medical QA research in Urdu, created by translating the benchmark MedQuAD corpus into Urdu using the Generative AI-based translation technique. The proposed corpus is evaluated using three approaches: (i) Information Retrieval (IR), (ii) Cache-Augmented Generation (CAG), and (iii) Fine-Tuning (FT). We conducted two experiments, one on a 500-instance subset and another on the complete 3,152-question corpus, to assess retrieval effectiveness, response accuracy, and computational efficiency. Our results show that JinaAI embeddings outperformed other IR models, while OpenAI 4o mini, FT achieved the highest response accuracy (BERTScore: 70.6%) but is computationally expensive. CAG eliminates retrieval latency but requires high resources. Findings suggest that IR is optimal for real-time QA, Fine-Tuning ensures accuracy, and CAG balances both. This research advances Urdu medical AI, bridging healthcare accessibility gaps.
Information retrieval, retrieval-augmented generation, cache-augmented generation, fine-tuning, Urdu medical question-answering.
Yang Liu1, Soroush Mirzaee2, 1Esperanza High School, 1830 Kellogg Dr, Anaheim, CA 92807, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620
StopCV aims to improve road sign detection for autonomous and assisted driving systems [1]. One of the main challenges is that current systems struggle with poor lighting, weather conditions, and obstructed signs. To address these issues, we developed StopCV, a vision-based detection system using a Raspberry Pi 5, a high-quality camera, and a custom-trained YOLOv8 model for real-time recognition [2]. Additional sensors, such as ultrasonic and LiDARreplicating systems, enhance object detection accuracy. Through multiple experiments, including real-world testing and public perception surveys, we identified limitations in low-visibility conditions and obscured signs. We mitigated these issues using improved image processing, infrared cameras, and AI training using different datasets. Our results show that enhanced sensor and AI integration can significantly improve accuracy [3]. Ultimately, StopCV demonstrates the potential of AI-driven vision systems to help improve driving safety, and further testing paves the way for autonomous driving applications.
Road Sign Detection, YOLOv8, Autonomous Driving, Multisensory Vision System.
Hongjia Meng1, Moddwyn Andaya2, 1Kantonale Mittelschule Uri, Gotthardstrasse 59, 6460 Altdorf, Uri, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620
Language barriers continue to challenge immigrants as they adapt to new environments, often limiting their confidence and social integration. Many existing language-learning applications fail to provide immersive, adaptive, and emotionally supportive experiences—particularly for beginners. This game is about learning language by talking and interacting with NPCs in real life scenes [1]. The aim is to remove the language barrier of a newcomer who doesn’t have enough motivation and bravery to talk in real life by simulating real life in a game, where the player only talks to NPCs. This paper introduces an AI-powered language learning game designed to simulate real-world conversations in a safe and engaging virtual environment. Players navigate through a city, interact with dynamic NPCs, and receive language support through a personalized guide who speaks their native language. This guide gradually shifts to the target language, helping users build confidence and fluency over time. The system leverages OpenAIs GPT-4o model to deliver context-aware, level-appropriate dialogue, ensuring that players are neither overwhelmed nor under-challenged. A user study showed that the game effectively fosters engagement and supports language acquisition, though it also highlighted areas for improvement in navigation and user interface design. Compared to traditional apps, this game offers a richer, more supportive learning experience by combining adaptive AI, immersive storytelling, and real-time conversational practice. Ongoing development will enhance usability and explore features such as multiplayer interaction to further support language learners.
Language Learning Gamification, NPC Interaction, Immersive Language Acquisition, Real-Life Simulation.
Nikitha Merilena Jonnada, University of the Cumberlands, USA
In this paper, the author discusses the importance of IoT, its security measures, and device protection. IoT devices have become a trend as they allow users to easily use and understand the devices. IoT has become a widely used technique within many industries like banking, agriculture, health care, and others. It made the users experience easy. IoT without AI has been a good investment for many users as its connectivity helps them use multiple devices from a single device and sometimes with a single click.
Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Security, Hacking, Risks.
Lakmali Karunarathne, York St John University, UK
Smart garbage bins which are automatically opened the bins doors when the person is standing in front of the smart bins are the perfectly innovated garbage bins by the IT industries and developers. The IR sensor is used to sense the waste and then its identified the which category the waste is by the support of sensors like metal proximity sensor, capacitive proximity sensor and the inductive proximity sensor. The expected services are aimed to provide by this entire system. The entire project is included things are, identifying the bins category, dispose the waste based on that category, send notifications and provide the reports for the purpose of getting awareness about the users garbage management. The IOT product is combined with the SMART GARBAGE MASTER (SGM) mobile application to interact with the entire IOT system via the cloud based to provide effective and efficient service to the users who use this system. The data is sent the Arduino for taking the decision that the garbage is either metal or non- metal.
Smart, garbage, segregation, plastic, paper, sensors, ultrasonic sensor, IR sensor, bin, level, percentage, IOT, Arduino, Cloud Databases.
Tanja Steigner and Mohammad Ikbal Hossain, Emporia State University, Kansas, USA
Bitcoin mining, often criticized for its substantial energy consumption, holds significant potential to drive energy innovation and sustainability. This paper reevaluates Bitcoin minings environmental impact, focusing on its ability to utilize surplus and renewable energy sources. Mining operations absorb excess energy, such as curtailed wind and solar power, that would otherwise go to waste, contributing to grid efficiency and renewable energy integration. The increasing shift toward renewables, now accounting for over 50% of mining’s energy mix, underscores the industrys progress toward sustainability. Through the analysis of industry data, this paper highlights Bitcoin minings dual role as both a flexible energy consumer and a catalyst for green energy investments. Despite challenges like e-waste and the industrys reliance on energy-intensive proof-of-work mechanisms, the findings demonstrate how targeted policies, and technological advancements can transform Bitcoin mining into a force for environmental and economic benefits. The study emphasizes the need for collaborative efforts among stakeholders to unlock Bitcoin minings full potential in supporting the global energy transition.
Bitcoin mining, renewable energy, grid stabilization, green energy investments, proof-of-work (PoW), carbon footprint reduction, e-waste management, decentralized energy systems
Avnish Kumar Singh and Rachit Ahulwalia, MILIT, Pune, India
The Internet of Things (IoT) has revolutionized how businesses interact and run. Unlike the Internet, which found its genesis in a military environment, credit for IoT goes to the civil industry, academia, and researchers. IoT is a disruptive technology and has changed the world in many unfathomable ways. Its genesis has paved opportunities that have reaped a plethora of benefits for various sectors managing a multitude of assets and engaging in the coordination of complex and intricate processes. Armed forces around the world also have shown interest in adopting this revolutionary technology and thereby reaping its tremendous benefits. In this research, we intend to bring out the opportunities in the field of IoT for defence forces, specifically in the context of military base operations. The main aim of this research is to study various IoT technologies available in the open domain, compare them, and recommend the most suitable one for military base operations. Further, this research is intended to study the effect of various input parameters on the performance of an IoT network by carrying out multiple simulations and analyzing them. Additionally, the research intends to propose a scenario with suitable input parameters to achieve optimum network efficiency and even recommend a deployment model for the IoT network. In this paper, we work towards proposing the optimum conditions required to achieve a high network performance in an IoT network. Finally, we have proposed a simulator to design an optimum IoT network based on certain input parameters. Also, we have recommended IoT deployment in Tactical Battle Area.
LoRa, LoRaWAN, Spreading Factor, Data Extraction Rate, Chirp Spreading Spectrum, Network Efficiency, Optimum Network Performance, Simulations, Network Energy Consumption, Tactical Battle Area
Gawande Krishna Ashok1 and Vandana B. Patil1, Gawande Krishna Ashok1 and Vandana B. Patil2, 1Dr. D. Y. Patil Institute of Engineering, Management and Research (DYPIEMR), 2School of Engg. Management and Research, D. Y. Patil International University, India
The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) represents a transformative paradigm shift in modern technology, unlocking unprecedented opportunities for automation, optimization, and innovation. AI empowers IoT devices to process and analyze vast streams of real-time data at scale, enabling advanced capabilities such as predictive maintenance, anomaly detection, intelligent decision-making, and seamless operational automation. This integration is driving disruptive applications across diverse sectors, including smart manufacturing, precision healthcare, industrial automation, smart cities, and environmental monitoring, revolutionizing workflows and enhancing efficiency. This paper provides a detailed exploration of the synergistic relationship between AI and IoT, focusing on their combined ability to improve system intelligence, adaptability, and operational reliability. It also highlights critical challenges, including data governance, privacy, cybersecurity, standardization, and the scalability of AI-IoT solutions in increasingly complex and interconnected ecosystems. By analyzing state-of-the-art advancements, innovative use cases, and emerging trends, this study aims to offer a comprehensive perspective on the transformative potential of AI-driven IoT, serving as a strategic guide for industrial stakeholders, policymakers, and researchers seeking to leverage these technologies for sustainable growth and competitive advantage.
Artificial Intelligence, Internet of Things, Automation, AI Models, Machine Learning, Neural Networks, Signal Optimization, Operational Reliability, Real-time Data Processing.
Aakaash Kurunth, Adithya S Gurikar, Tejas B, Sean Sougaijam and Kamatchi Priya L, PES University, Bengaluru, India
Spirulina platensis, a microalgae known for its high nutritional value and sustainability, is widely used in food, pharmaceuticals, and bioenergy. Its growth depends on factors like temperature, irradiance, pH, and nutrients, but optimizing these conditions is challenging due to their complex interactions. To address this, we integrate predictive analytics with an intelligent recommendation system to optimize cultivation. We evaluate multiple regression models, including Stacking, xGBoost, CatBoost, Gradient Boosting Machine (GBM), Support Vector Regressor (SVR), and Neural Networks, to determine the most accurate predictor of Spirulina optical density. The best-performing model powers a hybrid recommendation engine that combines content-based filtering and rule-based logic. This system identifies optimal growth conditions and provides precise recommendations for farmers and researchers, enhancing efficiency in Spirulina cultivation. By leveraging machine learning, this approach ensures data-driven insights for maximizing yield and sustainability.
Spirulina Growth Prediction, Environmental Factors, Machine Learning, Sustainable Cultivation, Regression Models
Vaani Bansal, R Navaneeth Krishnan, Punith Anand, Aditya Kumar Sinha, and Prof.Sheela Devi, Department of Computer Science Engineering, PES University, Bangalore, India
Counterfeit medicines absolutely show a high threat to public health and non-functional areas in the pharmaceutical industry that do not have proper regulatory mechanisms in place. Such counterfeit drugs might contain the wrong doses or even some hazardous materials. Hence, they break the trust formed between the healthcare system and patients and expose patients to severe health risks. This project presents a complete solution integrated with blockchain technology and machine learning features to ensure drug authenticity and to protect the pharmaceutical supply chain. Blockchain module built on Hyperledger Fabric gives the tamper-proof, decentralized ledger for medicine logistics tracking. Each medicine has a unique QR code that links together with its whole manufacturing and regulatory information. This provides the scope for customers and employees to check medicines authenticity at the same time just by scanning the code. In other words, this encourages transparency and makes traceability, thus preventing counterfeit drugs entering the supply chain. The protection of the blockchain infrastructure is ensured by employing an anomaly detection model using XGBoost-based machine learning. Trained on the NSL-KDD dataset, the model is capable of identifying and nullifying network malicious activities such as unauthorized access attempt, thereby ensuring reliability and security of the system. By combining these technologies, an all-in-one, scalable solution for minimizing counterfeiting medicines is available. The data within the framework can be kept by blockchain integrity and accessibility while providing machine learning security and thus forming a complete counterfeiting regime. The system not only protects public health but also improves the culture of trust and transparency in the pharmaceutical supply chain, making it a feasible approach for large-scale implementation in the industry.
Blockchain, Machine Learning, Pharmaceutical Supply Chain, Counterfeit Drugs, Hyperledger.
Ali Ebadi, Yaser Al Mtawa, and Qian Liu, Department of Applied Computer Science, The University of Winnipeg, Canada
Molecular property prediction is a critical step in discovery of new drugs or materials. Traditional computational methods models face limitations in computational efficiency, scalability, and reliance on handcrafted descriptors. Graph Neural Networks (GNNs) have recently emerged as a state-of-the-art solution by directly leveraging molecular graph structures, effectively capturing spatial and topological relationships. Despite their promise, GNNs are highly sensitive to hyperparameter configurations, posing challenges for their deployment in diverse applications. To address this, Nature-Inspired Metaheuristic Algorithms (NIMAs) based hyperparameter optimization offer a robust approach for navigating highdimensional, complex search spaces. This study systematically evaluates the performance of various NIMAs for optimizing GNN hyperparameters in molecular property prediction tasks. Benchmarking these methods on a large dataset, we assess their effectiveness in terms of prediction accuracy, computational efficiency, and scalability. Our findings demonstrate the potential of metaheuristic algorithms in enhancing GNN performance while addressing the challenges of traditional optimization methods.
Graph Neural Networks, Hyperparameter Optimization, Metaheuristic Algorithm, Evolutionary Algorithm, Molecule Property Prediction.
Manpreet Kuar, Yaser AI Mtawa, Qian Liu, Department of Applied Computer Science, The University of Winnipeg, Winnipeg, Manitoba, Canada
Understanding the relationship between a material’s structure and its properties is key to improving existing materials and developing new ones for various applications. Graph Neural Networks (GNNs) offer a promising approach for this task by modeling material structures as graphs. However, they face challenges with hyperparameter optimization (HPO), particularly when accounting for atomic, bond, and global features while simultaneously predicting multiple properties. This study proposes the incorporation of Operations Research (OR) techniques, including Genetic Algorithms (GA) and Simulated Annealing (SA), into GNN HPO for multi-property prediction in materials. The results show that SA achieved 15.73% lower Mean Absolute Error than GA, demonstrating superior predictive accuracy. However, GA converged 10% faster, while both outperformed baseline Random Search (RS), which had the highest error despite the shortest optimization time. Ultimately, this study highlights that OR could provide an effective framework for enhancing the efficiency of HPO in predictive models for material science. The source code files have been made available at MEGNet HPO.
Material Science, Graph Neural Networks, Multi-property Prediction, Operation Research, Hyperparameter Optimization.
Samah Kansab1, Matthieu Hanania1, Francis Bordeleau1, and Ali Tizghadam2, 1Ecole de technologie sup´erieure ( ´ ETS), Montr´eal, Canada, 2TELUS, Toronto, Canada
Context: DevOps integrates collaboration, automation, and continuous improvement in software development, enhancing agility and ensuring consistent software releases. GitLab’s Merge Request (MR) mechanism plays a critical role in this process by streamlining code submission and review. While extensive research has focused on code review metrics like time to complete reviews, MR data can offer broader insights into collaboration, productivity, and process optimization. Objectives: This study aims to leverage MR data to analyze multiple facets of the DevOps process, focusing on the impact of environmental changes (e.g., COVID-19) and process adaptations (e.g., migration to OpenShift technology). We also seek to identify patterns in branch management and examine how different metrics impact code review efficiency. Methods: We analyze a dataset of 26.7k MRs from 116 projects across four teams within a networking software solution company, focusing on metrics related to MR effort, productivity, and collaboration. The study compares the impact of process and environmental changes, and branch management strategies. Additionally, we apply machine learning techniques to examine code review processes, highlighting the distinct roles of bots and human reviewers. Results: Our analysis reveals that the pandemic led to increased review effort, although productivity levels remained stable. Remote work habits persisted, with up to 70% of weekly activities occurring outside standard hours. The migration to OpenShift showed a successful adaptation, with stabilized performance metrics over time. Branch management on stable branches, especially for new releases, exhibited effective prioritization. Bots helped initiate reviews more quickly, but human reviewers were essential in reducing the overall time to complete reviews. Other factors like commit’s number and reviewer experience also impact code review efficiency. Conclusion: This research offers practical insights for practitioners, demonstrating the potential of MR data to analyze and improve different aspects such as productivity, effort, and overall efficiency in DevOps practices.
Software process, DevOps, Merge request, GitLab, Code review.
Kailash Thiyagarajan, Independent Researcher, USA
The rapid rise of Large Language Models (LLMs) has revolutionized AI-driven applications but has also raised critical concerns regarding bias, misinformation, security, and accountability. Recognizing these challenges, governments and regulatory bodies are formulating structured policies to ensure the responsible deployment of LLMs. This paper provides a comprehensive analysis of the global regulatory landscape, examining key legislative efforts such as the EU AI Act, the NIST AI Risk Management Framework, and industry-led auditing initiatives. We highlight the gaps in current frameworks and propose a structured policy approach that promotes both innovation and accountability. To achieve this, we introduce a multi-stakeholder governance model that integrates regulatory, technical, and ethical perspectives. The paper concludes by discussing the future trajectory of AI regulation and the critical role of standardized auditing in enhancing transparency and fairness in LLMs. .
LLM Auditing, AI regulation, Ethical AI, Algorithmic Transparency, Bias and Fairness in AI, Explainability.
WANG Wei and ZHOU Weihong, Beijing International Studies University, Beijing, China
The current research investigates the effectiveness of advanced prompting techniques in Large Language Model (LLM)-assisted translation from Korean, a low-resource language, to English, a high-resource language. The research explores two primary factors: the role of English as an intermediary language in the translation process and the influence of carefully refined contextual prompts on translation quality. Through a comprehensive empirical methodology, the study integrates multiple automated evaluation metrics—namely BLEU, METEOR, COMET, chrF++, and TER—to assess key aspects of translation performance, including accuracy, faithfulness, naturalness, and idiomatic expression. The findings contribute valuable insights into the optimization of prompt engineering, offering practical implications for improving LLM-driven translation models, particularly for low-resource languages. Furthermore, the study highlights potential avenues for enhancing translation workflows and addresses challenges associated with leveraging LLMs for less commonly studied languages.
LLM-assisted translation, low-resource languages, prompting strategies, translation quality, Korean-to-English translation, pivot translation, DeepSeek, ChatGPT-4o, Grok-2.
Bruno Campos, Department of Design, MacEwan University, Edmonton, Canada
This study compares the impact of "juiciness" on user engagement and short-term information retention in interactive infographics. Juicy designs generally showed a slight advantage in overall user engagement scores compared to dry designs. Specifically, the juicy version of the Burcalories infographic had the highest engagement score. However, the differences in engagement were often small. Regarding information retention, the results were mixed. The juicy versions of The Daily Routines of Famous Creative People and The Main Chakras infographics showed marginally better average recall and more participants with higher recall. Conversely, the dry version of Burcalories led to more correct answers in multiple-choice questions. The study suggests that while juicy design elements can enhance user engagement and, in some cases, short-term information retention, their effectiveness depends on careful implementation. Excessive juiciness could be overwhelming or distracting, while well-implemented juicy elements contributed to a more entertaining experience. The findings emphasize the importance of balancing engaging feedback with clarity and usability. .
Infographics, Juiciness, Interactive, Engagement.
Shilei Cao1, Jonathan Sahagun2, 1Arnold O. Beckman High School, 3588 Bryan Ave, Irvine, CA 92602, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620
Dog poop is a concern that people seem to get irritated by, and yet don’t take the time to consider, even if it is also a major issue for the environment. As the problem would simply be solved with the enforced responsibility of dog owners, we decided to create a marketable, smart robotic arm with a connected app that encourages dog owners to pick poop up while climbing up rankings on a mobile app. It solves the idea of insanitary rejection with the safe distance, and creates competition that will help owners pick up poop voluntarily. The main problems were design miscalculations, mechanical restrictions, and advanced code features [2]. In experimenting, tests were performed on the sensor’s accuracy to detect poop and the reliability of the app’s ranking system. In the end, the correct sensor that correlates with dog poop’s elements, and a method of spaced time for accurate and fair poop pick up counts was used. This idea is convenient, and methodically incorporates the responsibility of dogs onto owners.
Impactful, Methodical, Convenient, Smart Sensors.
Julian Del Bel, Independent Researcher, Canada
Through diligent application of adelic integration and quantum arithmetic, we demonstrate the Egyptian fraction system’s remarkable anticipation of both number-theoretic profundities and quantum measurement theory. Our findings reveal a quantum-arithmetic framework revealing profound structural patterns in Egyptian fractions. We demonstrate these ancient decompositions exhibit: • Adelic balance through multiplicative normalization • Prime entanglement with optimized logarithmic spread (σlogs ≈ 0.17) • Dyadic quantization in Eye of Horus fractions • Computational validation via modern thresholding (< 10−12) Statistical analysis shows Egyptian σlogs values significantly below Erdős-Kac predictions (p < 0.001), evidencing non-random optimization. Our adelic unity framework connects these features through number-theoretic quantum analogs, revealing an ancient system anticipating modern mathematical principles.
Rahul Ambekar, Atharv Agharkar, Lalit Bagul, Niraj Bade, Department of Computer Engineering, A. P. Shah Institute of Technology, Thane, India
The increasing demand for well organized client and project management solutions has led to the rise of SaaS-based platforms that simplify business operations. This research presents a scalable SaaS solution that enables agencies to manage clients, payments, and projects through three core features: a Stripe-integrated dashboard, a Kanban-based project management system, and a no-code funnel builder. The Stripe integrated dashboard automates subscription management, transactions, and revenue tracking using Stripe Connect, ensuring seamless financial operations for agencies, clients, and the SaaS provider. The Kanban board simplifies task organization, team collaboration, and workflow tracking, improving project efficiency. The drag-and-drop funnel builder allows non-technical users to create sales funnels, integrate custom checkouts, and capture leads effortlessly. Built with Next.js 14 for frontend efficiency, Bun for runtime optimization, and Prisma for seamless database management, the platform ensures a futureproof architecture.
Software as a Service (SaaS), Business-to-Business-to-Business (B2B2B), Stripe Integration, Agency Management, Client Management, Kanban Board, Funnel Builder, MultiTenant Platform.
