The naval transportation industry faces unique challenges. The distribution and validation of paper documents can face delays and other mistakes due to the nature of the industry. Digitisation of the records makes the processes much more efficient and quicker. However, delicate matters need an infallible level of security against
To ensure protection against data breaches, insider threats and cyber attacks, the security solutions must be free from a single point of failure that could eradicate it. The system would also have to ensure data manipulation is easily detected.
Despite being the backbone of global trade, the maritime industry needs to catch up to digitisation compared to other sectors. Nevertheless, several facets of the naval area have been modernised, and blockchain has found its place among the used solutions.
Therefore, the proposed solution must address multiple issues and stay a few steps ahead of possible cyber threats, unauthorised access, or undetectable data manipulation. Fortunately, we found one that works from various angles and helps to ensure proper authentication.
Rexs.io is an authentication and notarisation solution that bases its work on blockchain. Using blockchain – a modern distributed ledger – allows different parties to record information in a permanent, immutable, tamper-proof, and transparent manner. The data is stored on a decentralised ledger and in a distributed system without the aid of a central authority.
The integrity of the blockchain is secured by trustless consensus algorithms and guarded by cryptography, making it impossible to append or modify historical data illegally. Therefore, blockchain is a decentralised, tamper-proof, immutable, and constantly growing list of cryptographically linked records called blocks.
Digitising the documentation process will help save money, paper and human resources globally. Digitisation in the maritime sector has faced some hindrances on account of security and possible threat concerns. Our solution addresses those issues and reduces worries and vulnerabilities.
We developed the Rexs.io framework to help ensure the security of document flow and accelerate digitisation. It can be integrated into the existing IT infrastructures, which makes it easily adaptable across various industries, as it doesn’t require a complete tech stack overhaul. It registers the creation of a specific data stream to ensure that it wasn’t manipulated or altered by suspicious activity or a malicious actor.
Rexs.io has two key components: the Secretary and the Notary. The secretary is a proxy agent designed to emulate the target’s endpoint. The secretary uses the same API and technology as the original target, allowing to seamlessly plug into an existing data pipeline on the data consumer’s side. This proxy calculates the hash of the proxied data and seamlessly transfers the unmodified data to the original target endpoint.
The generated hash of the product is, at the same time, then transferred to a queue, where the notary component later handles it. The latter communicates directly with the blockchain infrastructure. It gets the information from the secretary containing the file hash and handles storing it on the blockchain.
Suppose the original data piece is later manipulated. In that case, its hash – or a digital fingerprint – will inevitably change, resulting in a new hash for this data, unknown to the blockchain, proving that it has been modified and is different from the original.
Since blockchain is a tamperproof solution, once the original fingerprint is stored, it can’t be deleted or modified afterwards.
Significant reduction of unnecessary costs
Blockchain adoption rate is closely related to its understanding by the end-uses
It is essential to lower the entry barrier for non-blockchain savvy end-users and organisations
To boost adoption, applications should be capable of seamless integration with the existing IT infrastructure
It is essential to raise awareness among maritime companies and stakeholders that it is possible to reap benefits from blockchain solutions such as Rexs.io without having the advanced technical know-how of blockchain/DLT
About the client
Problem overview
Swiftly, a Stockholm-based startup, grappled with two significant challenges within their job portal. Firstly, accurate categorization of job listings posed difficulties, leading to suboptimal user experiences and ineffective job matching. Secondly, the manual job application process was time-consuming and resource-intensive, restricting scalability.
Proposed solution
Our approach comprised two pivotal components:
Collaborative discussions between Swiftly’s leadership and DAC.digital’s technical team laid the groundwork for a productive partnership, aligning expectations and goals.
An 8-person team, comprising ML Engineers, Embedded Systems Engineers, Data Scientists, and Fullstack Developers, came together to tackle the project.
Daily stand-up meetings and ongoing communication facilitated iterative development and enhancements.
The project concluded with the creation of an advanced SOTA solution that effectively addressed Swiftly’s challenges. This solution improved job listing categorization precision and streamlined the application process. The SOTA also offered a proof-of-concept for refining job listing keywords and automating application field population.
Results
Swiftly’s collaboration with DAC.digital resulted in the successful resolution of their job portal challenges through the implementation of innovative automation solutions. The web scraping tool and the SOTA presentation highlighted the potential of technology to enhance processes, elevate user experiences, and pave the way for future enhancements.
Key numbers
Our Client
Challenge
The client noticed that the dairy industry was heavily reliant on traditional practices, leading to inefficiencies. Recognizing the opportunity for improvement, the client aimed to create a digital system that could streamline milk delivery management and procurement processes.
Solution
DAC.digital’s team collaborated closely with the client to create a comprehensive end-to-end product. We decided together on the features and improved the concept collaboratively.
The team comprised a Product Owner, 3 backend developers, 2 frontend developers, a UI/UX designer, and 1 tester. As the project required, the team size was adjusted accordingly, growing when new functionalities were needed and scaling down during lower business demand.
To accommodate the complex and evolving nature of the system, we opted to work in a Time and Materials model. This approach provided the necessary flexibility and responsiveness to adapt to the project’s changing needs over time.
As we continued to enhance the system, the client recognized the need to optimize the milk delivery process further. To achieve this, our team introduced automatic route planning algorithms, which revolutionized how routes were planned for the drivers. Instead of relying on manual decision-making, the system could generate the most efficient routes based on various factors like delivery locations, vehicle capacity, and traffic conditions. This saved time, reduced fuel consumption, and improved overall operational efficiency.
Additionally, we sought to digitize and simplify the process of documenting milk quantities at each farm. To accomplish this, we developed electronic devices called MuuBox. These devices were installed in milk tankers and are responsible for uploading data in real time to the MuuMap system.
Previously, the milk collection process involved a lot of manual work for the drivers. They either had to use handwritten protocols to record the quantity of milk collected at each farm or print bulk receipts from their route, which had to be manually entered into the computer. This manual data entry was a time-consuming and labor-intensive task. For instance, entering 3000 positions manually required significant effort.
However, with the implementation of our solution, this manual process was digitized and streamlined. The collected pick ups data is now automatically aggregated in our system and allows integration with others. This automation significantly reduced the need for manual data entry, resulting in fewer errors than the previous approach.
By automating the data entry process, MuuBox significantly reduced the administrative burden on both drivers and the milk collection department. Instead of being the source of mistakes, the department can now focus on correcting errors.
For those MuuMap clients who preferred not to use the MuuBox devices, we introduced a manual entry functionality directly on their tablets. With this option, they could input the milk quantities manually. The advantage of this approach was that it eliminated the need for paperwork while ensuring accurate data recording. Just like with the MuuBox integration, the collected data from the manual entries were uploaded in real-time to the MuuMap system, streamlining the process and ensuring seamless data management.
Thanks to the application, the client became a pioneer in the market by offering a tool specifically designed for the traditional dairy industry. The unique and efficient solution attracted the first customers, who loved it and spread positive reviews. As a result, the client’s reputation grew, and more and more people started using the application. Eventually, it captured a significant portion of the Polish dairy market, securing a dominant position with a 30% market share.
Our client
Bouncing back from a plot twist
The first challenge appeared unexpectedly with the outbreak of Covid-19, forcing the company to pivot from their original idea of creating a smart anti-smog mask to something more practical for everyday use, as outdoor activities decreased significantly.
After returning to the previous concept, after easing the restrictions, the key challenge was creating a mobile application to connect with the mask and collect data from the air pollution measurement system Airly in a very short time-to-market window resulting from the first pivot.
Fast-track solution
The team’s tasks needed a speedy process and included:
The three Urban’er founders had an innovative idea to create a smart sports face mask for active urbanites. They wanted to adapt Bluetooth connectivity with a dedicated mobile app to display the duration of the activity, the equivalent of unsmoked cigarettes in air filtered by the mask.
The goal was to help those who live in urban areas with uneven air pollution landscapes to have an opportunity for comfortable and safer physical activities wherever they are. However, an unexpected twist of fate made them pivot from the original idea.
A sudden outbreak of Covid-19 in 2020 significantly limited the possibilities of outdoor activities in Poland. Thus, the company has decided to move from creating a smart sports mask to making a regular face mask with an exchangeable filter to filter out the PM 2.5 and PM 10 harmful air particles for protection during everyday activities. They wanted to focus on a modern design appealing to the public, comfort and functionality.
However, once the restrictions had been lifted, the demand for everyday protection masks dropped, allowing our client to return to their original plan. The mask used two HEPA filter layers with an FFP2 filtering standard, considered one of the most effective, that could filter out up to 95,8% of harmful particles. Moreover, they used locally-sourced materials for the design and production.
The time and budget were scarce, so the time-to-market window was narrow. They needed a team of experts to create a solution quickly and effectively.
Our team of experts comprised a full-stack developer and an assisting frontend developer. They were responsible for the app development and integration with the Airly system. Airly is crowd-sourced air pollution measuring system, and collecting data from the sensor allows insight into the current pollution level in the immediate area of the closest sensor.
Integrating the platform into the application gave the user real-time access to information on the air quality in their vicinity. The team worked on the complete solution within their short window.
The team worked independently, collaborating with the UX/UI expert on the client’s side. It allowed the creation of a coherent and stable product we then supported during the launch in the respective app stores.
Adopting React Native allowed for creating a modular and intuitive environment that can be easily modified. The app offered stable performance within the mobile operating systems. It also offered high code reusability across the platforms.
Using Firebase BaaS helped to speed up the development and streamline the tasks to the frontend to ensure the fast time-to-market product release.
Integrating with Airly allowed for data collection on air pollution and displaying live updates within the app.
Providing ongoing post-launch support to ensure that all the post-launch polishes were applied as necessary.
The application was developed on time and worked precisely as intended. It also received a maximum rating in the Apple App Store. It measured the activity, average breath and the avoided pollution. It also displayed the mask battery and filter levels to indicate the necessity for charging or a replacement.
Who’s our partner?
What did they need?
“We wanted to develop further our internal product called Spark. Spark Platform allows users to automate marketing surveys. It is the most powerful, implicit user testing platform we run, and we wanted it to be intuitive and self-serviced by the users.”
Michael Schießl, CEO and co-founder
What was our proposition?
eye square looked for a partner with the highest competencies and capability of connecting technologies to make a complete and reliable solution. After the initial meetings with the UX designer, we conducted workshop sessions with the client to consult the vision and address necessary questions and issues.
Initially, we met with Matthias Rothensee – the Product Owner. However, our collaboration also involved eye square’s COO, Philipp Reiter. We discussed the specific needs and requirements to get the final blessing from the company’s CEO – Michael Schießl.
After two weeks after the initial meeting, we had a good idea of what needed to be done. An intense working session gave us room to create necessary mockups and a working MVP. The work was divided into sprints, during which we received regular feedback on what was good and what needed improvement.
The team included a business coordinator to manage all the necessary documentation and agreement details (contract, NDAs and such), a technical project manager and a scrum master to translate the eye square’s needs into the backlog used by the UX/UI and development and QA team.
Additional touchpoints included a UX and UI expert and a cross-functional development team covering the DevOps part, backend and frontend, including testing. On eye square’s end, they had a product owner who helped with necessary coordination tasks.
We had:
The project had three iterations, after which the client could use the quarterly time for any extra work:
We could design the entire platform as requested, and the client was delighted with the results. We have designed the entire platform and were able to add extra features on top of what we already agreed on. It included adding a role management system that allowed granting the correct level of access based on the user’s role.
eye square was satisfied with the results and impressed with how much we could deliver in the fixed time, scope and budget. They were also happy with the professionalism and good communication the team displayed along the way. All this resulted in them requesting another collaboration on their new project.
The Spark platform was successful, and its current state is fully developed and usable. The company has some working hours from our team every quarter that they can use for necessary maintenance and extra work.
The platform has a varied user base, including big and small companies from different business sectors like banking, insurance or consumer goods.
To this day, eye square representatives consider the platform a huge success and are eager to show it off at public events.
To know more about our partner’s endeavours, see the Memex Poland 2022 presentation on AI and art at Memex Poland 2022.
Customer.
Computer vision processing
Artificial Intelligence & Machine Learning
Mobile application development
The services are performed by DAC.digital developers chosen to form an interdisciplinary, independent team. The core areas of support were based on Data Science with Python and Image Analysis knowledge and experience and DevOps support and were aligned during the so-called “Block Planning Sessions” or prioritized and assigned to our team via email. The initial collaboration began with KickerAce mobile app development and further collaboration on Shot Analyzer software.
The customer has been provided with fully scalable and functional software, meeting the deadlines, requirements, and specifications presented towards the beginning of the project. The collaboration between DAC.digital and customers’ teams has been based on transparency, openness, and honesty resulting in solid trust. Our problem-solving approach and excellent understanding of both technology and business allowed the Sports Computing team to feel comfortable and confident in the results of our work.
Our Client
Eldro – The company carries out innovative technological projects and develops original IT solutions for managing technical infrastructure and maintenance services.
Challenge
With progressing digitization, more and more decision-making processes are data-dependent. We must be able to guarantee that this data can be trusted. Its source must be unquestionable, in-transit tamper-resistance, and the entire data flow and decision process must be auditable.
Solution
Our solution is built with intuitive user interfaces and an end-to-end chain of trust and scalability in mind. By design, this is technology agnostic. We provide a framework with the freedom to choose and deploy the most suitable stack rather than forcing a user to utilize a particular blockchain or a storage solution.
Our technology has been implemented in three POCs across three different industries, proving the technology’s flexibility.
We have a technology stack for DLT-based notarization of existing data and data ingestion streams with a clear decoupling of trust and persistence layers. Organizations can, therefore, benefit from the immutability and tamper-resistance of the distributed ledgers without data throughput limitations typical of permissionless DLTs.
The platform has a modular architecture with standardized APIs between its core services. It simplifies the process of plugging in new components that can:
It can be plugged into existing infrastructures without significant changes, limiting the time and cost of implementation.
We abstract the technical complexities and nuances of the DLT ecosystem to help with the onboarding of the rest of the world. Our solution is built with intuitive user interfaces and an end-to-end chain of trust and scalability in mind. By design, this is technology agnostic. We provide a framework with the freedom to choose and deploy the most suitable stack rather than forcing a user to utilize a particular blockchain or a storage solution.
Work Evidence Reporting – Maintenance crews can send images to document the servicing job with their mobile devices. The images are sent directly to the infrastructure operator, and their hash is notarized on the blockchain. In the case of a dispute, all involved parties will validate the attachments.
Insurance Evidence Authentication – When signing an insurance policy with new clients, insurance agents must document the vehicle’s condition. We provide a blockchain-based, secure, and tamper-proof platform that enables the clients to provide digital evidence to the brokers using their smartphones.
Space Imagery Authentication – With Distributed Ledger Technology, we add a trust layer on top of satellite imagery, serving as input data for the earth analytics platform. Thanks to the DLT implementation, platform users will always verify if the information provided is generated from a sound and trustworthy source.
N-SAAW is a one-of-a-kind Deep Neural Network (DNN)-based system for monitoring farm animal health and wellbeing. This method was created specifically to examine the milk protein to fat ratio, encompassing extremes associated with malnutrition. This type of detection aids in lowering the danger of ketosis or acidosis induced by starvation. N-SAAW has already been deployed and evaluated in real-world agricultural settings, where it has been shown to identify malnutrition about 3.5 times sooner than current analytical approaches. Furthermore, because of the pre-processing, there was no possibility of the DNN missing starvation.
N-SAAW may be effortlessly linked with farm sensors and provides a way for autonomous monitoring to collect data that is then analyzed to provide insights to farmers. It also includes a visualization and reporting tool, allowing the provenance to be disclosed farther down the supply chain.
The solution developed and demonstrated was a tool to support the monitoring and analysis of milk cows’ bio parameters (temperature, pH). The data on temperature and pH are gathered from a ruminal probe.
The tool’s core functionality is based on a Recurrent Neural Network trained to predict the possible health deterioration of a specimen. In case of a predicted threat, an alert is triggered to inform the farm manager about the malnutrition of an animal. The triggering value has been set to a pH = 5.8, sustaining over a period of a few hours. Figure 1 presents the tool’s user interface, showing measurements and predictions tables, alerts, and pH values over time.
DAC conducted an initial validation of the health deterioration (malnutrition) prediction model. The results of the validation are considered satisfactory. As shown in the confusion matrix below (Figure N), the accuracy of “bad” and “good” predictions is reliable, producing an insignificant number of false positives. The mismatch between “warning” and “good” can be reduced with an auxiliary analytical algorithm.
Figure 2: Confusion matrix of the Recurrent Neural Network (RNN) trained to predict possible health deterioration resulting from malnutrition.
Our Client
Rohlig-Suus – a Polish-German logistics company with a global reach.
Challenge
Based on surveys and case studies the direct cost related to loss and theft of Returnable Transport Items (RTIs) ranges between 10-30% of the pool of registered items. In total, plastic pallet and container loss within the U.S. is estimated to be between $800M and $1.5B annually, based on surveys in America. In the rest of the world, the percentage of losses will not deviate much.
Solution
Our goal was to provide an automated RTIs circulation ecosystem allowing participants of the supply chain to exchange information about their returnable packaging (e.g. pallets) balance without sharing the sensitive information with others (e.g. who are the suppliers, how much goods each company buys/sells, etc.). Taking into consideration the number of participants in the supply chain involved in RTIs circulation within one ecosystem, the immutability of the data in the system is important. We designed a system meeting all challenges thanks to blockchain technology.
Limiting the number of kilometers driven by trucks to collect and transport empty RTIs withing one logistics ecosystem.
Providing the privacy of the sensitive business data within the supply chain during the RTI circulation process.
Abstracting the DLT complexities and providing a technology-agnostic system for non-DLT users.
The operator of the returnable packaging exchange manages DLT.
Shippers and hauliers register electronic pallet receipts on a common DLT chain.
Producers and chains register PULL and PUSH transactions and place orders for new pallets and returnable packaging.
Producers of returnable packaging and pallets provide new elements to the system, reacting to information about damaged and providing new ones with the growing demand
automated contract execution,
autonomous dispute resolution,
fewer paper documents,
improvement of freight monitoring,
continuous and trusted data exchange between partners,
real-time access to confirmed information about RTI balance.
Blockchain reading performance in high velocity, large volume context.
Using of off-chain indexing services to handle read operations for the UI.
Migration part from private to public ledgers.
Hiding the blockchain layer from the less tech-verse users.
BlockIS storage enabler application analysis and plan.
