MAIN CHALLENGE

Water supply companies must manage and predict water flow and consumption across various nodes in their distribution networks. Accurately forecasting these parameters is crucial for efficient resource management, reducing energy consumption, and minimizing operational costs. Traditional methods of predicting water usage are often inaccurate and inefficient, leading to resource wastage and higher operational expenses.

... HOW WE HELPED

We partnered with a water supply company to develop an advanced water consumption prediction algorithm. Utilizing graph neural networks, our solution can accurately predict water flow and consumption at different nodes within the network under various configurations. This cutting-edge approach enables the company to optimize its resource allocation, reduce energy usage, and lower operational costs.

Key features of our solution include:

- Advanced prediction of water flow and consumption using graph neural networks.
- Ability to handle different network configurations and provide accurate forecasts.
- Integration with the company’s existing systems for seamless implementation.

THE IMPACT

Our AI-driven solution has significantly enhanced the water supply company’s ability to manage its resources more efficiently. By providing accurate predictions of water consumption and flow, the company can make better-informed decisions, leading to substantial cost savings and improved sustainability.

REDUCED ENERGY CONSUMPTION

Accurate predictions enable the company to optimize water distribution, reducing the energy required to pump and treat water, thus lowering overall energy consumption.

COST SAVINGS

Optimized resource management and reduced energy usage translate into significant cost savings for the company, improving its financial performance.

ENHANCED RESOURCE ALLOCATION

With precise forecasts, the company can allocate water resources more effectively, ensuring consistent supply to all nodes and reducing the risk of shortages or over-supply.

IMPROVED SUSTAINABILITY

By minimizing resource wastage and optimizing energy use, the company can enhance its sustainability efforts and reduce its environmental footprint.

In summary, our collaboration with the water supply company has resulted in the development of a powerful AI-based prediction algorithm that enhances operational efficiency, reduces costs, and supports sustainable resource management. This innovative solution not only improves the company’s bottom line but also contributes to a more sustainable and efficient water supply system.

MAIN CHALLENGE

A large international bank faced significant inefficiencies in managing their internal ticketing system. Employees submitting tickets for support often encountered delays and inconsistencies in responses due to the complex and evolving nature of internal policies. The bank's support team struggled with inconsistencies between documents, changes in policies, and the need to maintain a clear chain of thought in addressing each query. This led to longer resolution times and reduced productivity.

... HOW WE HELPED

We partnered with the bank to develop an advanced Retrieval-Augmented Generation (RAG) chatbot designed to streamline the internal ticketing system. Our solution leverages large language models (LLMs) to provide accurate and consistent answers to employee queries by referencing the most up-to-date internal policies. The chatbot also incorporates sophisticated chain-of-thought modeling to handle complex queries effectively, even when new documents replace parts of previous policies.

Key features of our solution include:

- Advanced RAG chatbot that retrieves and generates accurate responses based on internal policies.
- Integration of chain-of-thought modeling to address complex and multi-step queries.
- Continuous updates to ensure the chatbot references the most current policy documents.

THE IMPACT

Our AI-powered solution has significantly enhanced the efficiency and accuracy of the bank's internal ticketing system, reducing response times and improving overall productivity.

REDUCED RESPONSE TIMES

The automated chatbot provides immediate answers to employee queries, drastically cutting down the time required to resolve tickets.

CONSISTENT AND ACCURATE ANSWERS

By referencing the latest policy documents and handling inconsistencies, the chatbot ensures employees receive accurate and consistent information.

ENHANCED EFFICIENCY

The streamlined ticketing process allows the bank's support team to focus on more complex issues, improving overall operational efficiency.

IMPROVED EMPLOYEE SATISFACTION

Quick and reliable responses to support tickets have led to higher employee satisfaction and reduced frustration with the internal ticketing system.

In summary, our collaboration with the international bank has resulted in a robust AI-driven solution that optimizes the internal ticketing process, delivering faster, more accurate responses and enhancing overall operational efficiency. This innovation not only saves time and resources but also significantly improves employee satisfaction and productivity.

MAIN CHALLENGE

Credit institutions face a complex and labor-intensive task in computing expected losses on their credit portfolios at the end of each fiscal year. To comply with IFRS9 regulations, banks must predict potential defaults and provision funds accordingly. This predictive process involves analyzing various factors, including central bank default data and macroeconomic variables such as unemployment rates, GDP, inflation, and housing status. The traditional approach, often reliant on manual calculations using Excel, is time-consuming and prone to errors.

... HOW WE HELPED

We collaborated with a company specializing in IT/ERP products for credit institutions to develop a sophisticated predictive algorithm. Our AI-driven solution accurately forecasts macroeconomic variables and calculates Probability of Defaults (PD), Loss Given Default (LGD), and Exposure at Default (EAD) in compliance with IFRS9 regulations. The algorithm is designed to be flexible, capable of integrating both structured and unstructured external data as required by specific banks.

Key features of our solution include:

- Predictive modeling of macroeconomic variables.
- Accurate calculation of PD, LGD, and EAD.
- Compliance with IFRS9 regulations.
- Flexibility to incorporate various external data sources.

THE IMPACT

The implementation of our AI-powered predictive algorithm has resulted in significant time savings and operational efficiency for banks. By automating the complex calculations previously done manually, banks have reduced errors and improved the accuracy of their expected loss computations.

REDUCTION IN MANUAL CALCULATIONS

Banks can now automate most of their expected loss calculations, leading to substantial time savings and minimizing the risk of errors associated with manual processes.

ENHANCED DATA UTILIZATION

Our solution leverages a wide range of macroeconomic variables and historical data, providing banks with a deeper understanding and more accurate predictions of potential defaults.

COMPLIANCE AND FLEXIBILITY

The algorithm's compliance with IFRS9 regulations ensures that banks meet regulatory requirements. Its flexibility to incorporate different data sources allows customization according to the specific needs of each bank.

IMPROVED OPERATIONAL EFFICIENCY

By automating predictive calculations, banks can allocate resources more efficiently, focusing on strategic decision-making and risk management rather than manual data processing.

In summary, our collaboration has empowered credit institutions with an advanced, AI-driven predictive tool that enhances accuracy, compliance, and efficiency in calculating expected losses, ultimately contributing to better financial stability and strategic planning.

MAIN CHALLENGE

The factoring division of an international bank faces a significant challenge in the form of invoice matching, or payment reconciliation. This process involves matching every incoming payment with one or more open invoices, which is a complex and time-consuming task due to the high volume of transactions and the intricate relationships between multiple payments and invoices (N:M relationships). To ensure operational efficiency and maintain customer satisfaction, the bank needed a solution to automate and streamline the invoice matching process, reducing the reliance on manual intervention and minimizing errors.

...
HOW WE HELPED

We at Dynius developed an advanced invoice matching engine leveraging machine learning and generative AI technologies. Our solution can intelligently read both structured and unstructured data accompanying the payments to accurately match them with the corresponding open invoices. Additionally, our algorithm analyzes historical reconciliation data to uncover hidden patterns, enabling it to emulate the decision-making process of bank employees when dealing with payments that lack sufficient information for straightforward matching.

Key features of our solution include:

- Automates the reconciliation of over 95% of incoming payments.
- Utilizes sophisticated pattern recognition to handle complex N:M relationships.
- Continuously learns from past reconciliation data to improve accuracy and efficiency.

THE IMPACT

Our AI-powered invoice matching engine has brought about significant improvements in the bank's operational efficiency and cost savings. The ability to automate most payment reconciliations has drastically reduced the time and effort required by the bank's employees, allowing them to focus on more strategic tasks.

REDUCTION IN MANUAL RECONCILIATION

The bank can now automate over 95% of the payment reconciliation process, leading to substantial time savings and enhanced operational efficiency.

OPTIMIZED DATA UTILIZATION

Our solution leverages historical data to continuously refine its matching algorithms, providing long-term benefits and fostering a deeper understanding of data science applications within the bank.

ACCELERATED PAYMENT PROCESSING

The implementation of our AI model has expedited the payment reconciliation process, ensuring faster turnaround times and improved customer satisfaction.

In summary, Dynius has enabled the bank to achieve a higher level of operational efficiency, reduced errors, and significant cost savings through the innovative use of machine learning and generative AI technologies in invoice matching.

MAIN CHALLENGE

A biolaboratory that provides bacteria counting services to supplement producers faced a significant challenge in manually counting bacteria colonies. This repetitive and labor-intensive process was prone to errors due to the small size and varying types of bacteria colonies, making accurate counts difficult and time-consuming. Ensuring precise bacteria counts is crucial for the supplement producers to maintain product quality and comply with safety standards.

... HOW WE HELPED

We developed a sophisticated computer vision system that automates the categorization and counting of different types of bacteria in a single Petri dish. Our solution uses advanced image recognition and machine learning techniques to accurately identify and count bacteria colonies, significantly reducing manual effort and improving accuracy.

Key features of our solution include:

- Automated detection and counting of bacteria colonies using computer vision.
- Accurate categorization of different types of bacteria within the same Petri dish.
- Integration of machine learning to continuously improve counting accuracy.

THE IMPACT

Our computer vision system has transformed the bacteria counting process for the biolaboratory, delivering significant improvements in efficiency and accuracy.

REDUCED MANUAL LABOR

Automating the counting process reduces the need for manual intervention, allowing lab technicians to focus on more complex tasks and analysis.

INCREASED ACCURACY

Advanced image recognition ensures precise counting and categorization of bacteria colonies, minimizing the risk of errors and enhancing the reliability of the results.

ENHANCED EFFICIENCY

The automated system speeds up the bacteria counting process, enabling the lab to handle higher volumes of samples and deliver faster results to supplement producers.

IMPROVED PRODUCT QUALITY

Accurate bacteria counts help supplement producers maintain high-quality standards and comply with safety regulations, ensuring better product consistency.

In summary, our collaboration with the biolaboratory has led to the development of an AI-driven computer vision system that automates the tedious task of bacteria counting. This innovation not only improves accuracy and efficiency but also allows the lab to better serve its clients, ensuring high-quality and safe supplement products.

MAIN CHALLENGE

Credit service providers dealing with non-performing loans (NPLs) face the daunting task of processing large collections of NPLs to assess their value and formulate debt collection strategies. This labor-intensive process, traditionally performed by lawyers, involves categorizing unorganized piles of digitalized documents, identifying missing appraisals or warranties, extracting structured information from unstructured data, and drafting debt collection plans based on the extracted information. The manual nature of this work makes it time-consuming and prone to errors.

... HOW WE HELPED

We collaborated with a credit service provider to develop an advanced algorithm leveraging generative AI and Natural Language Processing (NLP) to automate the NPL processing workflow. Our innovative solution can automatically categorize documents, identify missing appraisals or warranties, extract structured information from unstructured documents, and draft comprehensive debt collection plans.

Key features of our solution include:

- Automated document categorization to organize unstructured data.
- Identification of missing appraisals or warranties.
- Extraction of structured information from unstructured documents using NLP.
- Generation of debt collection plans based on extracted information.

THE IMPACT

Our AI-driven solution has significantly enhanced operational efficiency for the credit service provider, enabling them to process multiple NPLs in parallel and reducing the reliance on manual labor. This automation has resulted in considerable time savings and improved accuracy in assessing NPLs and formulating debt collection strategies.

REDUCTION IN MANUAL LABOR

The automated processing of NPLs reduces the workload for lawyers, allowing them to focus on higher-value tasks and strategic decision-making.

INCREASED PROCESSING CAPACITY

The ability to process multiple NPLs simultaneously enhances the provider’s capacity to handle larger volumes of NPLs, leading to faster and more efficient operations.

IMPROVED ACCURACY AND CONSISTENCY

By leveraging AI and NLP, the solution ensures more accurate and consistent extraction of structured information, reducing the risk of errors and improving the reliability of debt collection plans.

ENHANCED OPERATIONAL EFFICIENCY

The streamlined workflow and automation of tedious tasks enable the credit service provider to achieve greater efficiency, ultimately leading to better outcomes and higher client satisfaction.

In summary, our collaboration has empowered the credit service provider with an AI-driven solution that automates the complex process of NPL management, resulting in significant time savings, improved accuracy, and enhanced operational efficiency.

MAIN CHALLENGE

Proteomics research generates vast amounts of complex data, making it challenging for researchers to uncover meaningful insights and correlations. An important and prestigious proteomics lab at a Swiss university faced difficulties in making sense of their extensive datasets and identifying hidden gene correlations that drive bacterial growth. Additionally, staying abreast of existing literature and understanding how similar problems have been approached in the past further complicated their research efforts.

... HOW WE HELPED

We collaborated with the proteomics lab to develop advanced data analysis tools and an innovative AI-powered chatbot to streamline their research process. Our solution enables the lab to efficiently analyze large proteomics datasets, uncovering hidden gene correlations that influence bacterial growth. Furthermore, the chatbot we provided allows researchers to crawl through proteomics literature, helping them understand previous approaches to similar problems and facilitating knowledge discovery.

Key features of our solution include:

- Advanced data analysis tools for mining hidden gene correlations in large proteomics datasets.
- An AI-powered chatbot designed to search and summarize relevant proteomics literature.
- Enhanced ability to identify key insights and drive bacterial growth research forward.

THE IMPACT

Our solutions have significantly enhanced the proteomics lab's research capabilities, enabling them to derive valuable insights from their datasets and stay informed about existing literature. This has led to more efficient research processes and a deeper understanding of bacterial growth mechanisms.

ENHANCED DATA ANALYSIS

Our advanced tools enable researchers to mine hidden gene correlations, providing critical insights that drive bacterial growth research.

IMPROVED KNOWLEDGE DISCOVERY

The AI-powered chatbot facilitates literature review, allowing researchers to quickly understand past approaches and integrate relevant findings into their work.

STREAMLINED RESEARCH PROCESSES

By automating data analysis and literature review, our solutions save researchers valuable time and effort, allowing them to focus on more strategic aspects of their research.

INCREASED RESEARCH OUTPUT

With improved efficiency and deeper insights, the lab can accelerate its research, leading to more significant discoveries and contributions to the field of proteomics.

In summary, our collaboration with the Swiss university's proteomics lab has empowered researchers with advanced tools and AI-driven solutions, enhancing their ability to analyze complex datasets and stay informed about relevant literature. This has resulted in more efficient research processes, deeper insights, and accelerated scientific discoveries.

MAIN CHALLENGE

Recruiters often spend a significant amount of time reading through resumes and CVs, searching for relevant information. Each document presents this information differently, making the process repetitive, time-consuming, and prone to human error. This inefficiency can delay the hiring process and divert recruiters' attention from more strategic tasks.

... HOW WE HELPED

We developed an advanced algorithm specifically designed to automate the CV and resume screening process for HR departments. Our solution leverages machine learning and natural language processing to scan and extract structured information from variously formatted CVs. The algorithm retrieves key details such as personal data, skills, experience, education, and more, streamlining the initial screening process.

Key features of our solution include:

- Automated scanning and extraction of structured data from unstructured CVs.
- Identification and categorization of personal information, skills, experience, and education.
- Integration with existing HR systems to ensure seamless data transfer and usability.

THE IMPACT

Our AI-powered solution has significantly enhanced the efficiency and effectiveness of the recruitment process, allowing recruiters to focus on higher-value tasks and improving the overall hiring workflow.

REDUCED TIME SPENT ON SCREENING

By automating the extraction of key information from CVs, our solution reduces the time recruiters spend on initial screenings, allowing them to process a larger number of applications more quickly.

INCREASED ACCURACY

The use of machine learning ensures consistent and accurate extraction of relevant information, minimizing the risk of human error and ensuring that no critical details are overlooked.

ENHANCED RECRUITER PRODUCTIVITY

With less time spent on manual data entry and more accurate information at their fingertips, recruiters can focus on engaging with candidates and making more informed hiring decisions.

OPTIMIZED HIRING PROCESS

The streamlined workflow enables faster identification of suitable candidates, reducing the overall time-to-hire and improving the efficiency of the recruitment process.

In summary, our collaboration with HR departments has led to the development of an AI-driven solution that automates the tedious task of CV screening. This innovation not only saves time and reduces errors but also enhances recruiter productivity, leading to a more efficient and effective hiring process.

MAIN CHALLENGE

Companies and SMEs often receive a high volume of passive invoices from suppliers in PDF format. Accountant employees must manually extract information from these PDFs and enter it into their IT systems, a repetitive and error-prone task. This process consumes significant time and resources, reducing overall operational efficiency and increasing the risk of data entry errors.

... HOW WE HELPED

We developed an advanced AI solution that automates the registration and categorization of invoices for companies and SMEs. Utilizing the latest in generative AI and large language models (LLMs), our product accurately extracts structured information from unstructured invoice data in PDF format. The AI system then categorizes the invoices according to customizable criteria, seamlessly integrating the data into the company’s IT system.

Key features of our solution include:

- Automatic extraction of structured data from unstructured PDF invoices using generative AI and LLMs.
- Flexible categorization of invoices based on user-defined criteria.
- Seamless integration with existing IT systems for efficient data entry.

THE IMPACT

Our AI solution has dramatically improved the efficiency of invoice processing for companies and SMEs, reducing the time and effort required for manual data entry and minimizing the risk of errors.

REDUCTION IN MANUAL WORK

Automating the extraction and registration process significantly reduces the workload for accounting employees, freeing them up to focus on more strategic tasks.

INCREASED ACCURACY

By leveraging advanced AI, the solution minimizes data entry errors, ensuring that invoice information is accurately captured and recorded.

ENHANCED OPERATIONAL EFFICIENCY

The automation of repetitive tasks leads to substantial time savings, allowing companies to operate more efficiently and allocate resources more effectively.

COST SAVINGS

With reduced manual labor and increased accuracy, companies can achieve significant cost savings, improving their bottom line.

In summary, our AI-driven solution empowers companies and SMEs to automate the tedious task of invoice processing, enhancing accuracy, efficiency, and overall operational performance. This innovation not only saves time and reduces errors but also allows businesses to better utilize their resources and achieve cost savings.

MAIN CHALLENGE

Banks must conduct thorough analyses and evaluations of their credit positions annually, a process that requires reviewing extensive amounts of structured and unstructured data. This includes financial statements, central bank data, and textual notes from analysts who evaluated the positions in previous years. The sheer volume of information often leads to information overload, particularly for analysts and board members tasked with making critical decisions.

... HOW WE HELPED

We partnered with a company specializing in IT/ERP systems for credit institutions to develop an AI-based algorithm that synthesizes and summarizes vast amounts of data. Our solution leverages advanced AI techniques to provide a comprehensive overview of each credit position, highlighting key financial statements, trends, and potential alarms based on big data analysis.

Key features of our solution include:

- Automated summarization of structured and unstructured data.
- Identification of key financial trends and potential risks.
- User-friendly overview for analysts and board members.
- Enhanced decision-making support through data-driven insights.

THE IMPACT

Our AI-driven solution has significantly improved the efficiency and effectiveness of credit position evaluations. By providing concise and relevant summaries, it enables analysts and board members to make more informed decisions without being overwhelmed by excessive data.

REDUCTION IN INFORMATION OVERLOAD

The automated summarization reduces the burden of sifting through large volumes of data, allowing analysts and board members to focus on key insights and critical information.

IMPROVED DECISION-MAKING

With clear and comprehensive overviews of financial statements, trends, and potential risks, decision-makers can take more informed and strategic actions, improving the bank's overall performance and risk management.

ENHANCED DATA UTILIZATION

Our solution leverages both structured and unstructured data, maximizing the value extracted from diverse information sources and providing a holistic view of each credit position.

INCREASED OPERATIONAL EFFICIENCY

By automating the data synthesis and analysis process, the bank can save significant time and resources, allowing analysts to concentrate on higher-level tasks and strategic planning.

In summary, our collaboration has empowered the bank with an AI-based tool that streamlines the evaluation process, reduces information overload, and enhances decision-making capabilities, ultimately leading to better business outcomes and more efficient operations.

MAIN CHALLENGE

Public administrations face a significant challenge in ensuring that their websites do not contain sensitive information that could lead to identity theft or other privacy breaches. It is illegal to publish full names, dates of birth, signatures, and other sensitive information in certain official documents, but due to process inefficiencies, these documents often end up online. This not only poses a security risk but also exposes the entities to potential fines and legal liabilities.

... HOW WE HELPED

We developed an innovative product that enables public administration organizations to automatically crawl their own websites and identify documents containing sensitive information. Using advanced machine learning and natural language processing techniques, our solution can accurately detect and flag any content that violates data protection regulations, ensuring compliance and enhancing data security.

Key features of our solution include:

- Automated crawling of websites to identify sensitive information.
- Advanced detection algorithms using machine learning and NLP.
- Comprehensive reports highlighting non-compliant documents.
- Recommendations for removing or redacting sensitive information.

THE IMPACT

Our solution has significantly improved the ability of public administrations to maintain compliance with data protection regulations. By automating the detection of sensitive information, we have helped these entities avoid potential fines and legal issues while ensuring the privacy and security of personal data.

IMPROVED COMPLIANCE

Public administrations can now proactively identify and address non-compliant documents, significantly reducing the risk of data breaches and legal penalties.

ENHANCED DATA SECURITY

By removing sensitive information from public websites, we help safeguard personal data, preventing identity theft and other security issues.

OPERATIONAL EFFICIENCY

The automated nature of our solution reduces the manual effort required to review and monitor website content, allowing staff to focus on other critical tasks.

COST SAVINGS

By avoiding potential fines and mitigating the risk of legal action, public administrations can achieve significant cost savings, reallocating resources to more impactful initiatives.

In summary, our product empowers public administrations to ensure their websites are free of sensitive information, enhancing compliance, data security, and operational efficiency while preventing costly fines and legal issues.