Group CEO, Lutech
"Our collaboration with Dynius is highly beneficial. As an affiliated startup to our Lutech Campus, we frequently work with Dynius on innovative AI projects for our customers with mutual benefits in a number of business domains. Their technical expertise and professional approach have made working with them a pleasure."
CEO, Finwave SpA
"Dynius enables us to integrate sophisticated AI features into our products, enhancing areas such as credit information synthesis, invoice matching, credit risk assessment, and early warning systems in the domains of factoring, lending, and customer finance. The professionalism and innovative solutions from Dynius have significantly contributed to our progress, solidifying them as a key partner in our path to continuous innovation."
Co-Founder & COO, Verity AG
"Dynius has been instrumental in enhancing our data analytics capabilities at Verity. Their expertise in data science enabled us to make sense of our warehouse data, developing advanced algorithms for root cause analysis to explain discrepancies. The innovative solutions and professional approach from Dynius have helped our customers find hidden patterns and realize savings."
Chief Research Officer, Wideverse srl
"We have worked with Dynius on a research project to introduce AI in the energy sector. Dynius showed proactivity and professionalism and was able to deliver the AI algorithms quickly according to our timelines. They designed and implemented an innovative approach to water consumption modeling that enabled our customer to realize consistent money and energy savings."
Chief Executive Officer, Murex CS srl
"We work with Dynius on a variety of research and innovation projects. Dynius is our go-to partner for every AI application because they provide professionalism, scientific-backed innovation, and impeccable project and deadline management."
Co-Founder, Board Member & Head of Medical AI, QuantumSPEKTRAL
"The strategic partnership between QuantumSPEKTRAL and Dynius is a valuable asset to our company. Their expertise in quantum computing and sustainable technology has significantly advanced our capabilities, enabling us to develop innovative AI solutions that reduce energy consumption and enhance operational efficiency."
Founder & Member of the Board of Trustees, Quadrans Foundation
"We have established a fruitful collaboration between Dynius and Quadrans to build products and services powered by AI and the blockchain. Their deep expertise and innovative approach have enabled us to create cutting-edge solutions that drive efficiency and security, positioning us at the forefront of technological advancement in our industry."
Senior Expert Proteome Informatics, Functional Genomics Center, ETH Zurich
"Dynius has deepened our understanding and the importance of AI in proteomics research. Working with them to identify the most relevant use cases was inspiring, as they showcased various innovative ways in which AI can help uncovering and connecting protein functions and help explain regulating mechanisms of differential abundances."
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Optimize processes and realize savings with production-ready AI solutions. Quick PoCs efficiently deployed to production. Rapid results and agile iterations.
Unlock the full potential of your data with tailored AI solutions designed to drive innovation and efficiency across your organization.
Develop a robust data and AI strategy that aligns with your business goals, ensuring sustainable growth and competitive advantage.
Harness the power of scalable, secure cloud infrastructure to support your AI initiatives and optimize performance.
Streamline your machine learning operations with our MLOps services, ensuring seamless integration, deployment, and monitoring of models.
Leverage the latest advancements in Generative AI to create innovative solutions that enhance creativity and automate complex tasks.
Empower your business with Large Language Model agents that deliver sophisticated conversational experiences and intelligent automation.
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.
Our offerings adhere to the highest standards of quality and security, with the flexibility to be tailored to your specific needs.
We go beyond proof-of-concepts, ensuring seamless deployment and integration of AI algorithms into your production environment. Our robust, reliable, and scalable AI solutions deliver real business value, making your AI investments ready for consistent performance in the real world.
We offer fair, transparent pricing that aligns with your success, minimizing initial investment and tying a significant portion of our fees to project success. As a dynamic startup, we focus on real business value, aiming to build lasting partnerships based on mutual success and continuous innovation.
Our AI models prioritize security and privacy, ensuring your data remains confidential and protected.
Choose the best deployment option for your needs, whether it's a secure private cloud infrastructure or on-premise solutions for maximum control.
Gain complete control over your AI models with our robust governance features, allowing you to manage and monitor them effectively.
We specialize in crafting AI solutions that are as unique as your business and your needs, addressing your specific challenges and objectives.
Count on consistent and reliable model performance to drive your business forward, regardless of commercial models’ upgrades or updates.
As adoption expands, our AI solutions can effortlessly scale or be deployed across different business units as required.
We uphold the highest standards of Swiss precision and reliability in all our AI solutions, ensuring excellence and trust.
Our commitment to ethical AI means developing solutions that are transparent, fair, and respect privacy and human rights, in alignment with the guidelines set forth by the EU AI Act.
If you're not satisfied, we'll refund your money — no questions asked.
We are a young, dynamic and international
team
aiming at delivering the highest quality for our clients.
Federico is the CEO and founder of Dynius. Under his leadership, Dynius has become a recognized spin-off of ETH Zurich and an affiliated startup of the ETH AI Center. In the past two years, the company has doubled its revenues and personnel annually, serving clients across various industries. Federico is dedicated to integrating advanced AI tools both for clients and within Dynius to enhance efficiency and drive continuous improvement. Outside of work, Federico enjoys skiing, hiking, sailing, and mountain biking, embracing the great outdoors whenever possible.
Dario is the COO and co-owner of Dynius, passionate about transforming and automating B2B processes with innovative AI solutions. During his previous tenure at Accenture Technology in Zurich, he gained crucial expertise in IT Project Management as a Business Analyst, Scrum Master, and Deputy Product Owner. Dario has hands-on experience in generative AI, deep learning, and machine learning, alongside data analytics and business intelligence. In his role as COO and Project Manager at Dynius, he is actively involved in defining Dynius' strategy and is responsible for coordinating and executing the company's AI projects in Switzerland, Italy, and Germany. Dario has a passion for aviation and enjoys playing padel, tennis, squash, and various other racket sports in his free time.
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