Europe Union

The Client.

Our client provides companies with passwordless authentication solutions. They consider passwords a significant security risk and the root cause of data breaches. Their goal was to eliminate this issue by introducing various mechanisms for user authentication without the need for passwords.


Line of business

Network Security

Founding year



100 > employees


Before engaging our services, HYPR faced challenges in finding the right talent to develop their innovative passwordless authentication solutions. The demand for skilled professionals in the United States, where HYPR is based, was high, and most candidates preferred working for large corporations rather than small companies.


To address the problem of eliminating passwords, HYPR sought to expand their engineering team. They aimed to hire skilled professionals who could contribute to the development of their authentication solutions. They approached our company to find the best talents.

Our process.

Initial talks and kickoff

HYPR approached us seeking the best talents for their project. During our discussion, it became clear that they would find those talents right here with us.

Team composition

Our team consisted of five members: full-stack developers proficient in React and Kotlin, backend developers with Kotlin expertise, an Android developer, and an iOS developer. Initially, they brought on board a portion of the team (2 individuals). Once they were satisfied with our employees’ performance and the client had more substantial financial resources, they proceeded to hire the next members of the team.

Technology overlay

HYPR primarily utilizes technologies such as React, Kotlin, Fido, and Fido2 for their passwordless authentication solutions. They also integrate with identity providers like Microsoft Azure and Google. Their product portfolio includes solutions for various platforms, including desktop, Windows, macOS, mobile applications, and server components.

Used Technologies.

Microsoft Azure

Process implementation outline.

Our team supported both cloud and on-premises installations of HYPR’s solutions. We maintained backward compatibility and ensured seamless version management for their various components. 

We focused on implementing passwordless authentication mechanisms using Fido and Fido2 standards. Additionally, we integrated with major identity providers like Microsoft Azure and Google.

Our team also worked on creating virtual USB devices to facilitate authentication, a requirement of the Fido2 protocol. Moreover we take part in maintenance stories, and each of us has some maintenance shifts (weekly shifts) while we await incidents and resolve issues.


HYPR successfully expanded their engineering team, addressing their staffing and competence gaps. Their passwordless authentication solutions were enhanced and integrated with various platforms. They continued to serve some of the largest banks in the United States.


quote icon We take great pleasure in sharing our high level of satisfaction with the DAC’digital team. We have no plans for any changes in our collaboration and are enthusiastic about the prospect of continuing to work with our exceptionally talented team members. quote icon

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Are you facing similar challenges in your quest for innovation and security solutions? If so, we invite you to join us on this journey. Contact us today to explore how we can help you achieve your goals and revolutionize your industry. Together, we can make a difference.
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Case Studies.

We are a team of engineers & problem solvers who deliver value across areas of IoT, hardware, embedded systems, big data, machine learning, DLT, DevOps, and software engineering.


The company is a leading global online travel agency focused on the hostel market. It enables travelers to experience new places and people in a fun, memorable and safe way.


The client was transforming their core product, implementing many new ideas and solutions. Due to the tight schedule, they’ve asked for help to support some features in parallel.


We’ve provided a FullStack developer supporting Social Cues as a part of the project. We developed a small microservice, a proxy, and a cache for their legacy API.

Key numbers.

These numbers represent the value we provided to the business through new enhanced functionality.

Social network effect.

50% of their bookings were made by customers who had opted into the social network (social members).


More than 80% of their social members used the features while traveling.


In the first six weeks post-acquisition (new customers acquired April – September 2022), social members were 4x more likely to be recruited via the App.


Travelers make 1.6x the number of bookings and are twice as likely to make these bookings via the App

Before our involvement.

The customer project’s goal was to develop a platform for solo travelers to socialize before and during a hostel stay. They were introducing several new features, including the “See Who’s Going” option. A visitor would receive information on other people who will stay at the same place at the same time a few days before a scheduled overnight arrival.

To accomplish this part of the project, they needed information on who was staying at the hostel and on what dates. The old implementation, however, was quite slow, and the API was incompatible.

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Team composition.

Work began immediately after one meeting with Product Owners and developers, where they explained what was needed.

The team behind the project consists of 1 Fullstack Developer. Because a client has several teams and multiple Product Owners responsible for delivering projects, a developer on our side was coordinating work with their internal developer.

Progress of the project was presented during demos to the client’s PO, but most of the work was done independently.

Used Technologies.

Process implementation.


Our part of the project was used to simplify access to the client’s API, transform responses, and cleverly cache them.


The client requested the development of a proxy that functions as an overlay capable of retrieving statistics from an endpoint. The proxy converts the data and returns it in the requested form.


Later, other microservices used it to display information about who else stays in the hostel you just ordered.


‎ ‎‎‎‎‎‎‎‎‎‎‎‎‎‎ ‎

The client was very pleased with our work and our contribution to this part of the project. The project as a whole concluded successfully, which the client wrote about on the website and in a special report. Our customer emphasized the visible effectiveness of the “See Who’s Going” feature in the preliminary results announcement.

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Case Studies.

We are a team of engineers & problem solvers who deliver value across areas of IoT, hardware, embedded systems, big data, machine learning, DLT, DevOps, and software engineering.


Amini is a purpose-built data infrastructure that addresses the lack of environmental information in Africa. In order to promote economic inclusion for farmers and supply chain resilience across Africa and beyond, they have built a comprehensive solution that collects, measures, and processes geospatial and soil parameters, such as soil moisture, air temperature, weather conditions, filtered satellite images, NDVI and NDII indexes, etc., to conclude if the land owner qualifies for insurance benefits in case of environmental factors that affected the faulty harvest or lack thereof.


Our challenge was to prepare an intelligent and scalable end-to-end system from scratch that could effectively process and integrate loads of data inputs, utilising DRF, FastAPI, and blockchain technologies in the form of a microservice for farmers and insurers.

The exponential data load posed a challenge to adjusting the architecture and fitting it to queue the data processing in the most efficient manner.


We supplement the client’s team with the missing skills and competencies based on the project’s needs (2 Backend Developers, 1 Front-end Developer and 1 DevOps Engineer) to become a major part of their core-team.

Our Approach to The Project.

Our team translated business requirements into the architectural vision for the entire platform. As the most senior engineers, we also provide mentoring, conduct code reviews, and assist with establishing best practises. Additionally, we actively develop core elements of the ecosystem.

Our process.

Phase 1

Preparing a prototype with MVP functionality (APIs, basic blockchain architecture, microservices architecture) to prove to investors and potential clients that Amini has the capability to collect and process the base data and integrate its system with the external API, as well as lay the foundation for integration with future components.

The prototype enables the user to input the farm’s geographical coordinates and returns details such as the relevant crop calendar for that land allotment and base soil parameters, enabling the user to understand and predict future events that may affect their harvest.

Phase 2

Translating the existing features into a business solution in the form of a microservice platform by adding authentication processes and deploying the system on AWS with the use of Kubernetes according to the best standards of security and scalability. Another important part of the phase was introducing the data queuing systems as well as preparing the frontend part of the project to provide the UX/UI for the end users.

Ongoing development

As we supplemented the team with the missing competencies, our developers are currently working on the maintenance of existing features and the integration with the new components, working towards making the solution market-ready.

– Key decisions.

The architecture deployment issues – we chose AWS as the most reliable cloud vendor because we are experienced working with vast scaling opportunities, and Kubernetes because of its autoscaling capabilities, error resistance, and auto-healing.

We decided to go with ArgoWorkflows, – the innovative processing solution that is cost-effective, extendable, scalable, and streamlines the deployment process.


By integrating authentication procedures and deploying the system on AWS via Kubernetes in accordance with the best practises for security and scalability, the current features have been transformed into a business solution in the form of a microservice platform. Data queuing technologies were also included during this phase, and the frontend of the project was ready to provide a user-friendly interface and experience.

Outcome. played a crucial role in helping the customer bootstrap their engineering culture, fostering an environment that promotes collaboration, innovation, and best practices. Through our guidance and expertise, we supported the customer in establishing a strong foundation for their engineering team.

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Case Studies.

We are a team of engineers & problem solvers who deliver value across areas of IoT, hardware, embedded systems, big data, machine learning, DLT, DevOps, and software engineering.

The challenges of documentation in the maritime sector

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 

  • data manipulation, 
  • malware attacks 
  • and other cyber threats.

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.


Using blockchain for data digitisation and protection 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. – a digital notary
solution that revolutionises
security solutions

We developed the 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. 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.

Critical takeaways

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 without having the advanced technical know-how of blockchain/DLT



World-class technology, the combined expertise of 65,000 employees and operations in 56 countries have made Thales a key player in keeping the public safe and secure, guarding vital infrastructure and protecting the national security interests of countries around the globe.


Thales products comprise many components that the company buys from a large variety of partners. The goal was to develop a platform that stores all documentation of components, suppliers of individual components and replacements. The company was looking for a partner that has experience in the development of enterprise architecture. There were 3 main goals for the project implementation:

  • R&D, which was supposed to help in the choice of technology,
  • Proof of concept activities,
  • Development of Product Lifecycle Management.


The general concept of the DPF is the following: for every business process that handles product-related data, the DPF offers an access point (interface) for a human in charge of this business process. The product-centric DPF interface enables that human user to accomplish the necessary data-handling tasks if needed, reaching across the private data structures of different companies that are part of the supply network for that product. DPF provides a separate user interface for every processes or sub-process, which allows for a separation of concerns and provides a security measure. DPF architecture distinguishes between two types of companies in the supply network for a product: the OEM company, which is the company that owns the product that is the central object of the supply network, and the Manufacturer(s) companies that are the suppliers of parts for that product.


The project has been divided into four stages.

The first phase consisted of collecting the requirements, which was the responsibility of the Thales team. For this purpose, we talked with product-owners, procurement, SCM, and change management. The set of requirements was then discussed with the DAC to make the functional requirements.

Research and architecture.

The next stage is the analysis of available technologies, research and the first proof of concepts. Architecture development and selection of technological stack.

Development and testing.

In the next stage, the time came for the development and testing of a developed solution. DAC developed an architecture to integrate cloud solutions, in which PLM partner systems were embedded. One of the assumptions was to use Arrowhead Framework, currently curated by Eclipse Foundation, Industrial IoT automation, and interoperability framework.


We are currently developing a Whitepaper in collaboration with DAC scientists and engineers to promote this innovative approach. The Whitepaper should be available in late 2019 and early 2020. We will present the solution at industry 4.0 conferences and trade fairs.

Delivered value.

The main goal has been achieved: a working prototype that demonstrates the performance of Product Lifecycle Management. The development of a functional demonstrator was crucial for the further development of the project.

The DPF in Budapest at the Productive 4.0 conference related to Industry 4.0, digital production, etc., where the domain experts had the opportunity to review and give feedback on our tool.

Used Technologies.

Arrowhead 4.1

A data-space-enabled collaborative product life cycle and supply chain management. Developed in cooperation with Thales, Digital Product Footprint integrates distributed PLM systems, operated by different parties in a multi-stakeholder ecosystem, to furnish visibility of product and component dependencies across the value chain. By encompassing all data items relevant for managing all aspects of a product, DPF supports the bidding process, product configuration, and change management.

DigiTrac is a system built on the concept of Digital Product Footprint (DPF) developed by the engineers at in collaboration with THALES, Netherlands. It is based on three core viewpoints: a product-centric, a business process-centric, and an end user-centric approach to product management, i.e., it considers business processes and the people in charge of them to be the essential parts of product management.

The DPF may be seen and explored using the system. It provides a graph description of all the pieces used to build an item (for example, a car) together with all the information such as the original manufacturer, manufacturing date, maintenance, repair history, and so on. It is already being used in the transportation industry.

DigiTrac is a functioning prototype that exhibits the effectiveness of Product Lifecycle Management and can be customised for use in various industries. DigiTrac was showcased at the Productive 4.0 Industry 4.0 conference, receiving an excellent response.

State of the art.

The concept of a Digital Product Footprint is the result of a problem-solving process in which the management of a product, as a primary business process for a product owner (who is not a customer/owner), is revisited because existing solutions in terms of process descriptions and underlying tools and methods have eroded and become less performant because of the following trends: Digital Transformation, Smart Industry, Smart Industry++, Realisation of Industrial Internet of Things.

As a result of adopting the view that the creation and use of a man-made product must be trackable and traceable, in all required detail, via data in the digital domain (or the virtual world of digital data, or however you want to describe it), we face the challenge of defining and implementing an all-inclusive set of digital data items that together and in part describe all aspects of a product over its entire lifecycle (i.e. from its conception to its decommissioning and destruction).

Thales products are made up of several components that the firm obtains from a wide range of suppliers. The purpose was to create a platform that keeps all component documentation, providers of individual components, and replacements. The organisation searched for a partner with experience in enterprise architecture development; this is where came in to expand the state-of-the-art.


Thales products comprise many components that the company buys from a large variety of partners. The goal was to develop a platform that stores all documentation of components, suppliers of individual components and replacements. The company was looking for a partner that has experience in the development of enterprise architecture. There were 3 main goals for the project implementation:

R&D, which was supposed to help in the choice of technology

Proof of concept activities

Development of Product Lifecycle Management

The Solution: How does it work?

The DPF’s main principle is as follows: for any business process that handles product-related data, the DPF provides an access point (interface) for a human in charge of that business process. The product-centric DPF interface lets that human user do the essential data-handling operations, reaching across the proprietary data structures of multiple organisations that are part of the product’s supply network. DPF offers a different user interface for each process or sub-process, allowing for concern separation and security.

Personalised product interface for Data Analytic.

DigiTrac, facilitated by Data Space, enables a collaborative product life cycle and supply chain management. It connects distributed PLM systems run by many parties in a multi-stakeholder ecosystem to provide visibility of product and component interdependence across the value chain. DPF helps the bidding process, product configuration, and change management by incorporating all data items necessary to control all elements of a product.

DigiTrac provides access to an anonymised representation of an existing complex product involving a multi-stakeholder supply network and spanning as many lifecycle phases as possible, supplemented with other data items to cover another business process involved in DPF management, namely the Logistics process. As a result, the DPF will enable access to a wide range of data items, including design data (software, hardware, and mechanical parts), manufacturing data, supplier data, supply and logistics process and network data, and data on the product’s operating performance, maintenance, and support. This product’s change processes will comprise a variety of small and significant effect events, such as a mid-life update, ownership change, export limitations, obsolescence management, and supply network changes.

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The general concept of the DPF is the following: for every business process that handles product-related data, the DPF offers an access point (interface) for a human in charge of this business process. The product-centric DPF interface enables that human user to accomplish the necessary data-handling tasks if needed, reaching across the private data structures of different companies that are part of the supply network for that product. DPF provides a separate user interface for every processes or sub-process, which allows for a separation of concerns and provides a security measure. DPF architecture distinguishes between two types of companies in the supply network for a product: the OEM company, which is the company that owns the product that is the central object of the supply network, and the Manufacturer(s) companies that are the suppliers of parts for that product

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Case Studies.

About the client

  • Name: Swiftly 
  • Line of business: Automated Recruiting and Unbiased Recruitment tools
  • Founding year: 2020
  • Country: Sweden

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:

  • Web Scraping Tool: We developed a sophisticated web scraping tool to extract precise keywords from job listings, enhancing categorization accuracy.
  • SOTA Presentation: We created a visionary state-of-the-art (SOTA) presentation, demonstrating automated field auto-fill capabilities to streamline the application process.

Applied technologies:

  • Python, Selenium and FastAPI were used to implement a service able to scrap form fields from a given website, and to fill automatically the forms once the data are provided
  • Neo4J and PostgreSQL were databases used for storing graph data describing relations between job offers, job seekers and other data which can be used to look for mutual associations, as well as more general and structured metadata of job offers.
  • Sklearn was used to implement a recommendation engine looking for best matches between job seekers and job offers.

Pre-existing Challenges:  

Before implementing the SOTA and POC solutions, Swiftly faced several challenges:
  • Inaccurate Categorization: Swiftly encountered difficulties in accurately categorizing job listings, causing mismatched job offers and candidates.
  • Manual Application Process: Manual application processes consumed time and resources, impeding scalability.
  • Insufficient Automation: The absence of automated keyword extraction led to imprecise job listing categorization.
  • Scaling Issues: Manual processes and categorization limitations hindered scalability.
  • Lacking Technological Strategy: Swiftly lacked a comprehensive technology-based strategy to enhance categorization accuracy and streamline processes.

Implementation Approach.

Our implementation strategy followed these steps:
Initial Talks and Kickoff

Collaborative discussions between Swiftly’s leadership and’s technical team laid the groundwork for a productive partnership, aligning expectations and goals.

Team Composition

An 8-person team, comprising ML Engineers, Embedded Systems Engineers, Data Scientists, and Fullstack Developers, came together to tackle the project.

Agile Collaboration

Daily stand-up meetings and ongoing communication facilitated iterative development and enhancements.

Results and Impact:

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.


Swiftly’s collaboration with 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

  • Project Duration: Successfully completed within 16 days!

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Our Client

  • Name: MuuMap
  • Line of business: Software for the dairying industry
  • Country: Poland




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’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.

Which technologies have we applied:

The team and our approach to the project.  

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.

The begin of the journey

  • It all started with a navigation tool for the drivers. The client noticed that new drivers faced challenges navigating through the 3000 dairy farms. They needed details about accessing area premises, locating milk tanks, and gate openings. Typically, new drivers spent two months riding along with experienced drivers to learn the routes, and even then, they would call dispatchers or other drivers for directions to specific farms.
  • To address this, we collaborated with the client and developed a solution. We placed location pins on the map for each farm’s milk cooling station and, if needed, “drew” a new road. This helped drivers get precise route plans, accurate directions, and essential information about the yards they visited.
  • The success of this application reduced driver training time from two months to just 2-3 days.
  • Later, the Manager module was created, serving as a massive CRM system. It holds vast amounts of information about farmers, their milk deliveries, drivers, license expiration dates, available fleet, subcontractors, destination points, and daily production demand. This comprehensive tool provides an all-encompassing overview of the entire dairy operation.
Review Quote
We’ve been collaborating with for many years, and truth be told, we co-created MuuMap together. The success we’ve achieved is undoubtedly a result of this partnership. is a trusted partner and our number one choice.
Adam Strużyński
Product Manager of MuuMap

MuuBox – an answer to how to optimize a delivery process


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.

See how we adapted IoT in the MuuMap system.

Visit the website

We are aware of different customer needs

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.

Review Quote
The product quality is phenomenal, and all of my expectations have been met.
Adam Strużyński
Product Manager of MuuMap

From manual to autonomous milk reception

  • The latest module created is for milk reception at the plant. During inspections, officials present a purchased product with a QR code, requesting documentation for that product. That’s when a manual paperwork process begins.
  • The quality control department employee needs to search for the delivery from that day and then look for the specific delivery to that particular tank. Only then can they find the precise time the milk from different routes was collected in that tank. This process can be time-consuming and prone to errors due to the manual nature of the documentation.
  • Now, with the newest module, MuuMap continues to be involved in the process beyond milk delivery to the gate. The customer supports weighing the truck upon entry, documenting laboratory tests, and recording the destination tank where the milk is pumped or stored.
  • After the truck leaves the plant, it is weighed again, providing valuable information on the actual milk quantity received compared to the declared amount. Based on this data, MuuMap’s application generates a digitized route report, allowing easy traceability of the milk’s journey, including the specific day, routes, and suppliers contributing to each tank.

The results of the dairy revolution

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.


27600 Farmers

677 Road Tankers

30 Dairy Plants

1175 Drivers

650 Devices

Over 5 billion liters

34,30% in Poland
3,48 % in Europe

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Our client

  • Name: Urban’er (Smartwear Sp. z o.o.)
  • Line of business: Smart wearable technology
  • Founding year: 2019
  • Size: 1-50
  • Country: Poland (Gdańsk)


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:

  • Designing the application
    • Backend and integrations, parts of the frontend
  • Enabling the application to collect activity and filtered air data from the mask
  • Implementing the integration with Airly – a crowd-sourced system for collecting air pollution data from dedicated sensors in the vicinity

Our tech stack

  • React Native
  • Firebase BaaS
  • Apple iOS and Google Play implementation
  • Airly integration

A clever idea with unexpected turns of events


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.

air pollution

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.

Check out the article on top tech trends in the coming years.

See the trends

Our small but effective team 

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.

See how we helped create a health wearable for dogs.

View the case study

Implementing the process to accommodate a small time-to-market window    


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 creation process:


  1. Our team adapted React Native to create a stable application for iOS and Android devices.
  2. Quick streamlining of the tasks to the frontend via Firebase BaaS allowed us to accelerate the development process to accommodate the narrow time-to-market window.
  3. We ensured smooth integration with Airly to enable smooth data collection on pollution levels.
  4. Our team proceeded to a smooth launch in the app stores.
  5. Post-launch, the team was at the client’s disposal for six months to iron out any errors and inconsistencies as necessary.

How it turned out

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.

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Line of business



In e-commerce, companies acknowledge the need to enhance communication between the company and its clients to improve response speed.

Solution implemented a proof of concept (PoC) for an AI chatbot on its website. The primary objective was to assess how the chatbot could enhance communication, improve response times, and effectively handle customer queries and concerns.

Which technologies have we applied:

  • Python3,
  • LangChain,
  • Figma,
  • React,
  • Firebase,
  • FastAPI,
  • Kubernetes.
  • OpenAI API (LLM, ChatGPT)
chatbot technologies used

The role of chatbots in company challenges.

The companies face challenges in effectively addressing customer queries, concerns, and feedback in a timely manner. Many users ask the same questions repeatedly, resulting in a need for a solution to handle such repetitive inquiries. 

Implementing a chatbot was seen as a way to address these issues effectively. Currently, numerous employees are dedicated to answering live chat questions. Automating this process through a chatbot would reduce the need for human intervention and free up employees for more strategic tasks.

Moreover, training employees to handle the extensive information required for live chat responses is time-consuming. On the other hand, training a chatbot is much faster and more efficient, allowing for quicker implementation.

Due to the implemented chatbot, companies can work towards achieving complete automation of their customer support process. By utilizing a chatbot, they can streamline communication, reduce reliance on human resources, and provide rapid and accurate responses to customer inquiries.

chatbot customer service

The people and tech behind our project.  

Our team was composed of a diverse set of experts, including two UX/UI specialists, one front-end developer, one DevOps specialist, and four specialists in Natural Language Processing and backend development.

In the project, we employed a range of technologies for different aspects:

For design, we used Figma. React was utilized for frontend development. The backend consisted of Firebase as well as Python 3 with LangChain. FastAPI was implemented to handle requests efficiently. For service management, we employed Kubernetes. 

Enhancing our chatbot’s performance.

We have started with research on the feasibility of using various LLMs as a backbone of our chatbot, and ultimately chose OpenAI to power our chatbot. 

We carefully designed the chatbot, ensuring it aligned perfectly with our brandbook. Once the design was complete, we developed the backend. In just two weeks, we performed the integration with the frontend successfully.

Having a successful chatbot heavily depended on the data it received, as wrong context could lead to poor performance. So, we carefully curated the ideal dataset for fine-tuning to ensure its optimal functionality.

After successful deployment, we collected internal feedback to evaluate the chatbot’s performance in handling both simple and complex questions, and also run a set of automated tests to evaluate its performance.

chatbot DACdigital

The impact of our chatbot’s PoC on the entertainment industry

  • We successfully created the entire Proof of Concept (PoC), demonstrating that the chatbot is capable of understanding user queries and providing relevant answers based on the fine-tuned model. 
  • The project was completed within the allocated time frame and budget, which spanned two months and encompassed design, backend and frontend development, as well as machine learning implementation. 
  •’s team considers additional features for future implementation, such as sentiment analysis of the inquires, support for multilingual operations, further enhancement of the contextual understanding and integration with external systems.
chatbot healthcare banking

Cross-industry efficiency: the impact of chatbots in diverse sectors.

Chatbots are a great solution that go beyond e-commerce, demonstrating their efficacy in a variety of industries like banking and healthcare. Users frequently grow impatient while waiting for simple question answers, and they eagerly look forward to prompt responses.

Chatbots in the banking industry provide a helpful solution to this frequent problem. Customers can get prompt answers to their questions by utilizing chatbot technology, which will reduce their dissatisfaction and improve their overall experience.

By giving doctors helpful support, chatbots can also have a big impact in the field of medicine. These intelligent systems can talk to patients, learn about their symptoms, and then transmit the information instantly to doctors.

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  • Name:Zarząd Transportu Miejskiego (ZTM) w Gdańsku (Public Transport Authority in Gdansk)
  • Business object:
    Organization and management of public urban passenger transport
  • Founding year: 2005
  • Country: Poland


The client’s goal was to provide the generated data to developers in an open format. The openness of the data allows for the creation of various applications according to the expectations of each demanding user. Thanks to this, public transport passengers organized by ZTM in Gdansk could check the timetable in multiple ways, along with information about any delays of individual ZTM vehicles in Gdansk.


  • Using the CKAN platform to store open static data (which changes relatively infrequently).
  • Building a custom service that allows for sharing data that changes frequently, such as the GPS position of trams and buses.

Which technologies have we applied:

  • Java,
  • Spring-Boot,
  • REST,
  • SOAP,
  • SqlServer,
  • Kubernetes.

Exploring the limitations of traditional traffic management system.

  • Between 2012-2015, the Integrated Traffic Management System TRISTAR was launched in the Tricity (Gdansk, Sopot, Gdynia). Its goal was to improve the quality of transportation, which includes, among other things, collecting information about vehicle traffic in the Tricity. As part of the system, the hardware and software of ZTM in Gdansk were modernized.
  • The technologies used in TRISTAR were built traditionally. This made it difficult to use the system to directly share data about current timetables and GPS positions of buses and trams in a manner that would maintain its operation. ZTM Gdansk needed help preparing an automatic data export from the TRISTAR system and building an architecture allowing for secure sharing. That’s when came to help.

Cooperation begins: and ZTM in Gdańsk.

In 2016, we participated in a tender to create the “Open Data System of ZTM in Gdańsk” and were selected by ZTM in Gdańsk. Our domain knowledge and experience in backend architecture were crucial for the client.

Four specialists from worked on the project: the Chief Solution Architect, the Architect/DevOps Engineer, and two Senior Mobile Developers who provided the necessary expertise and experience in delivering the project. We worked as an independent team in close cooperation with the customer.

The challenges of sharing complex data structures.  

The first step was to decide where to place the open data. Fortunately, the client already had access to the CKAN platform, a data management system for public organizations. For this reason, we chose CKAN as the place to share “Open Data ZTM in Gdańsk”.

To make the information we wanted to share consumable by planners such as Google Maps, Jakdojade, BusLive or Time4BUS we had to prepare it properly. We worked on the data structure related to the timetable, which proved to be quite complicated due to its distribution across various databases and microservices.

We also had to consider the limited resources of the source system. We had to strike a balance between data collection speed and the load on the databases, which are simultaneously used by other ZTM Gdansk applications.

Sharing rapidly changing data (e.g., departure estimates from stops or vehicle positions) required a different approach due to CKAN system limitations. We needed to be able to update the data frequently enough to meet the quality requirements. We shared this data directly from our servers, and CKAN remained the place for its documentation.

Review Quote, with whom we have been cooperating for many years, deserves to be called a reliable and professional business partner on whom one can rely.
Agnieszka Rzeźnikowska
Chief Specialist for Passenger Information System Support Public Transport Authority in Gdańsk

The impact of’s partnership with ZTM in Gdańsk

  • As a result, the team and ZTM in Gdańsk successfully developed applications that collect and share data so planners can easily consume them from other providers, such as Google Maps or Jakdojadę.
  • The entire set of “Open ZTM data in Gdańsk” can be found here:
  • To present the shared data, we created their visualization through an application on the website:  in both desktop and mobile versions.
Mobile application for public transport

We continue to work on open data for ZTM in Gdańsk, ensuring their timeliness and carefully controlling the downloading of data from the TRISTAR system.


In 2022, we handled 406,639,355 requests for open data in Gdańsk. 

Dynamic data open data

On the other hand, queries for static data (updated once a day) occur hundreds of thousands of times a month. 

Open data statistics
Review Quote
The solutions created by the team were executed according to our expectations, and our business goal was achieved.
Agnieszka Rzeźnikowska
Chief Specialist for Passenger Information System Support Public Transport Authority in Gdańsk

In the case of the map, which was made public less than two years ago, we recorded 3,827,046 visits. The promotional campaign and regularly introduced improvements contribute to the increasing interest in the product, as evidenced by the growing number of visits.

Mobile public application statistics

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  • Name: TB Auctions
  • Line of business: Ecommerce
  • Founding year: 2018
  • Size: Up to 500 employees in the company’s workforce.
  • Country: The Netherlands.


As a result of acquiring several ecommerce companies, TB Auctions needed to integrate all systems into a unified platform, while also incorporating new functionalities to enhance the overall user experience. 


To produce high-quality code and satisfy the customer’s requirements, we put together a team of Kotlin experts who were solely focused on working on this project.

Creating a unified ecommerce platform: technology stack

  • Kotlin,
  • Ktor,
  • JetBrains/Exposed,
  • Azure Service Bus,
  • MS SQL,
  • Redis,
  • MongoDB,
  • Grafana,
  • Loki.
Technology stack for ecommerce platform

Simplifying ecommerce with TB Auctions.

The client started replacing their systems with Kotlin some time ago, and as they grew, they realized they needed more experienced Kotlin experts. After they bought several ecommerce businesses and made the decision to combine all of their systems into one platform, this became especially urgent. Additionally, TB Auctions focused on creating new features that would simplify the purchasing process for users.
The referral and successful small project

The client contacted us through a referral from our trusted partner – Angry Nerds. At the outset, TB Auctions chose to assign us a smaller project in order to assess and confirm our technical expertise.

From one Senior Fullstack Developer to a complete team

We’ve started with one person (Senior Fullstack Developer) working on the project on our side, whose remarkable technical skills were highly appreciated by the customer. That’s why the client requested adding 25 extra individuals, comprising mostly regular, mid and senior Kotlin specialists, a team of juniors and frontend experts (Angular and REACT), as well as DevOps, .NET and QA automation specialists. Our team worked as part of the customer’s larger team.

Optimizing efficiency with multiple teams

To ensure optimal efficiency, we formed multiple teams, each with a maximum of six members as the bigger groups made cooperation challenging. Our team shared innovative ideas with the customer and recommended strategic modifications to optimize their operations.

Review Quote
And yeah, really professional. So that’s what you need, of course. Cause it’s a big investment, but it’s also a big trust you give to another company. So yeah, that was key for me. I need to have a trusting relationship, the same one I have with my people.
CTO of TB Auctions
dedicated Kotlin team for ecommerce integration

Need a dedicated Kotlin team for your next project? We’ve got you covered.

Kotlin dedicated teams

Which technologies and expertise have we applied?

  • The system is built on Microservices architecture. 
  • K8s manages containerized deployments and scaling applications stored on the Azure Cloud platform. It is an event-based application using Azure Service Bus as a message broker. 
  • Kotlin is the primary back-end language, with Kotest as a testing module. Kotlin programming language has numerous benefits and advantages. It can reduce the boilerplate code required to perform tasks, resulting in less clutter in backend solutions. Faster execution of the written code and quicker loading times are produced by less code and a shorter compile time. In order to make the code shorter and less verbose, it also eliminates unnecessary vocabulary. Kotlin provides several features that enhance code safety, such as null safety and type inference. These features help minimize the risk of runtime errors and simplify the process of writing error-free code.
Review Quote
Kotlin stands apart as a distinct language with notable advantages, particularly in the realm of functional programming. When we started using Kotlin with functional programming techniques, our team got really excited about it! It made it easier to bring in new people who were genuinely interested in what we were doing.
CTO of TB Auctions
  • Ktor is used to support Kotlin with building asynchronous Servers and Clients. 
  • JetBrains Exposed framework connect Kotlin with SQL and provides DSL features to work with it. MS SQL is the leading SQL Engine for most of the services databases. 
  • Redis is used as a fast cache database. MongoDB provides more complicated aggregation and analytics of data. 
  • The whole system is monitored and observed by Grafana for display data, Prometheus for scrapping metrics data, and Loki for log aggregations.


Achieving results through comprehensive ecommerce integration efforts.

The team put forth a lot of effort to make sure that every system was fully integrated and operating without a hitch, ensuring that users would have a simple and hassle-free experience when browsing and making purchases. Due to the changes, customers were able to purchase products from a single source, eliminating the need to navigate through multiple sites. 
Millions of products
are auctioned by TB Auctions every year, providing a valuable service to customers around the world.
of the client’s technology department was made up of our IT team.
Within 12 days
from the initial conversation with the customer, the project had begun.

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    • Name: eye  square GmbH
    • Line of business: market research, human experience
    • Founding year: 1999
    • Size: 50-200 employees
    • Country: Germany (Berlin)


    After a successful collaboration on the Spark surveying platform, the client came back to us with a new project for deepening the research with a more hands-on experience, adopting gaze estimation to track the user’s attention while browsing content on their smartphones. The key issue is that such an approach is still highly innovative and uncharted territory.


    • Collecting training and test data using crowd-sourcing platforms and a dedicated web app
    • Creating AI-powered computer vision processing pipeline to detect the point of gaze 
    • Developing a full-stack framework consisting of a set of web services, backend infrastructure, front-end SDK and web app
    • Continuous research & development of algorithms for accuracy improvement

    Technology stack

    • Python
    • PyTorch
    • OpenCV
    • MediaPipe
    • JavaScript
    • FastAPI
    • AWS
    • Docker
    • ClearML
    • DVC
    Web Eye Tracking project

    The challenges of the innovation in computer vision

    The collaboration on the web eye-tracking project evolved organically from the previous project we worked on – the Spark platform for market research. Given the successful results of that project, eye square requested to start working on a new solution that would allow them to deepen the research even more. They wanted to create a solution to track the customer’s attention while browsing the content on their smartphones for more accurate marketing and experience research. They needed an accurate system for tracking the user’s gaze over the surface of the phone’s screen. The project’s key challenge was that the eye-tracking technology for mobile phones was an innovative concept without a practical market application yet. Therefore, they needed competent experts and engineers to create something entirely from scratch. Moreover, the created environment had to be executable in a real-life context without any specific research environment. The aim was to create something that would work in the daily setting and regular phone use.
    Setting the right course for the product

    Our initial communication involved the company’s COO, Phillip Reiter, technical project managers – Garrit Güldenpfennig, Frederic Neitzel and Olaf Briese, and the CFO, Friedrich Jakobi. They outlined their expectations and needs for the project. 


    The company previously used  several third-party solutions for laptop-related use cases, some of them required external HW. The main goal this time was to create a new solution that would be suitable for mobile phones. The initial agreements took approximately two weeks, after which the team started working on the Proof of Concept to ensure the visions were aligned before starting the subsequent phases of such a complex R&D project.

    Building a team of gaze estimation and computer vision experts

    Our team comprises a Senior Computer Vision Engineer, Machine Learning Engineers, DevOps specialists, frontend developers and a project manager. 


    eye square supported us with three technical Project Managers (one of whom has become a project coordinator), the CFO and a developer  to provide extra help. Moreover, an essential part of their contribution was coordinating the crowd-sourcing platform to acquire testers and data sets. The technical project managers Garrit, Frederic and Olaf also provided their expertise and help whenever needed.

    Technological aspects of the gaze estimation project

    To create a solution that exceeds the state-of-the-art technology, we had to use the available resources to the maximum. The crucial part of the process was to create a stable foundation that would allow the creation and development of new features that would bring it closer to the final project and what it should look like.

    • We applied Python, PyTorch and OpenCV, among other libraries, to create the base algorithm. We later based the development on testing data from early tests and larger data gathered via the ClickWorker crowd-sourcing platform. 
    • JavaScript was used to develop WETSDK, Training Data Collection App (TDCA) and an example web application illustrating the production use case. 
    • FastAPI was used to develop a communication interface between the end user – the web app and the algorithm running on the backend server 
    • AWS allowed us to store the training and validation data in the cloud
    • Docker made it easier to encapsulate the algorithm in self-contained SW images that can run in the cloud

    Due to its complexity and innovation, the project must be divided into multiple stages and requires extensive research, including the “trial and error” approach. The biggest challenges involve the dynamic environment, as we wanted to create a solution that would work “in the wild”, without the need for any specific HW and with minimum prerequisites from the user.

    This raised several obstacles, including the complexity of calibrating the phone camera. Phone screens & cameras differ from model to model. Therefore, it’s hard to find a generic estimation method, especially since phone manufacturers don’t disclose the physical dimension of devices. 

    Since the user needs to have complete freedom to use their phone, there’s the challenge of making sure that the gaze estimation algorithm can be auto-calibrated in different models of smartphones. It is a difficult task given different angles, distances, and face detection capabilities. Our neural networks are trained on different faces and angles to get a result similar to regular use. 

    Due to the vast crowd-sourced data from the ClickWorker platform, the team needs to evaluate the data quality.  An automatic framework was developed to filter out recordings that don’t satisfy basic quality metrics, like proper lighting, lack of blurring, etc.

    Learn more about Python and its uses

    Read the article
    Web Eye Tracking project

    First steps towards a groundbreaking eye-tracking solution

    • Milestones 1-3: developing a PoC
      • Milestone 1 – achieving a certain level of algorithm accuracy (06.06-31.07.2022)
      • Milestone 2 – creating SDK and an example of application design (05.08-30.09.2022)
      • Milestone 3 – web eye tracking service and further improvement of the algorithm accuracy to meet the acceptance criteria (01.10-31.10.2022)
    • Milestone 4: continuous research, improving the algorithm and preparing the application to collect a large amount of data (TDCA – Test Data Collection Application) via ClickWorker – a crowd-sourcing platform
    • Milestone 5: processing the data from ClickWorker, adding TDCA features, and further working on the algorithm accuracy
    1. The project started as a proof of concept. Initially, we aimed to achieve the required minimum algorithm accuracy.
    2. Upon establishing the essential accuracy, our team worked on the SDK environment and application design ready for testing and data collection.
    3. After establishing these features, we created a web eye-tracking service for further testing.
    4. The next step involved continuous research in improving the algorithm and preparing the application for collecting more considerable amounts of data from the ClickWorker crowd-sourcing platform
    5. Currently, we are working on the next round of testing to improve the algorithm’s accuracy to the maximum of 1 cm average point of gaze estimation error on a wide range of test subjects.

    Listen to Karol Duzinkiewicz talk about Gaze Estimation.

    Watch the video

    What were the key metrics of our journey?

    • 80% – the target accuracy for the next phase
    • <1cm – the target margin of gaze point detection error

    Outcomes and further steps towards reliable eye-tracking experience research

    Even though the innovation threshold is set high for that project, the results are satisfying on both sides. The initial aim was to prepare the Proof of Concept. However, our partner keeps extending our work, as the results are good and the prospects promising.

    We are gathering and processing more training data to improve the algorithm’s accuracy. We already exceeded the technological state-of-the-art and are continuing to work on achieving better accuracy and taking the next steps towards creating a working product.

    Several elements of the processing pipeline developed by are currently considered for patent submission.

    Learn more about the Spark market research platform.

    View the case study

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    Who’s our partner?

    • Name: eye  square GmbH
    • Line of business: market research, human experience
    • Founding year: 1999
    • Size: 50-200 employees
    • Country: Germany (Berlin)

    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?

    • Frontend
    • Backend
    • Role management system
    • UX design

    Listen to Florian Passlick present Spark.

    Watch the video

    What was the tech stack?

    • React
    • Java Spring
    • API integrations
    • AWS S3
    • AWS Cognito
    • PostgreSQL
    • Integrations: Tivian (formerly Questback), Dynata
    Spark example

    What did eye square need to create?

    eye square needed to find a way to automate the users’ input so that some of the manual work could be done on the company clients’ end, allowing them to tailor their research and surveys to their needs as necessary in an easy, automated way, with the use of only one self-service platform. Before that, they had multiple tools that needed extra work. The first software house they hired couldn’t work on their project, and they ultimately came to us. They needed a platform to automate the research-creation process for their clients made from scratch. It would include the software development of each element of the Spark platform – frontend and backend. The platform needed to allow users to define their profile and group and access their panel. Automating and combining multiple tools into one user-friendly program would allow the company’s clients to manage their resources independently and more effectively.
    How did we set our collaboration on the right course?

    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.

    Who were the experts behind the project?

    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:

    • DevOps engineer and solution architect
    • Junior DevOps engineer
    • Backend engineer
    • UX designer

    Learn more about eye square in our interview with Michael Schießl and Friedrich Jakobi

    Check it out

    Which technologies and expertise have we applied?

    • Java (with Spring) for backend work
    • React for frontend
    • AWS Cognito helped automate new user registration by sending verification emails
    • AWS S3 service to automate uploading and storing user files in respective files connected with a specific user
    • PostgreSQL for database purposes
    • Tivian (formerly Questback) integration for survey templates and creation
    • Dynata integration for delivering the surveys to the correct type of respondents needed for the survey
    • Additional UX design work
    Spark overlay
    Review Clutch
    First of all, we are very happy with the perfect timing of the project and delivery of the solution we needed. perfectly fit into the time frame proposed and also delivered a set of extra functionalities on top of what was agreed upon before the start. The quality of the code and work, including UX/UI, was spot on.
    Philipp Reiter
    Partner & COO of eye square GmbH

    What were the key metrics of our journey?

    • 874h – total project estimate
    • 3 months – the span of the solution development (March to June 2021)
    Spark filing example

    What were the necessary steps of implementation?

    The project had three iterations, after which the client could use the quarterly time for any extra work:

    1. Creating the platform that allowed the eye square admins to create surveys on behalf of their clients
    2. As an extra feature, our team created the file submitting and storage system
    3. After approximately nine months, the aim was to give the survey creation to the company’s clients so that the users with correct access privileges could create their requests and accept the costs, which were then accepted for them by a project manager on eye square’s end
    4. Adding more survey templates for a more customised and accurate experience
    5. After completion, the client has a pool of 24 hours per quarter for maintenance work or the development of additional features

    What was the outcome of the collaboration?

    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.

    Review Clutch
    Communication and cross-technology and cross-vertical experience and depth of the team that delivered the solution were impressive. They are not only service providers but, first of all — deliver added value on top of what they were asked for, with fresh ideas and suggestions. The process management was impressive. We felt like everything ran smoothly, and finished the project on time, which is also not common!
    Philipp Reiter
    Partner & COO of eye square GmbH

    To know more about our partner’s endeavours, see the Memex Poland 2022 presentation on AI and art at Memex Poland 2022.

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    Bitbond is a leading software service provider offering bank-grade tokenization and digital asset technology. The company has conducted Europe’s first security token offering. In 2019 it was approved by BaFin, the German financial regulator. Since then, Bitbond has offered asset tokenization technology and services to financial institutions. The company is based in Berlin, Germany.
    Business domain/experience we shared .
    Mobile application development Prod eng and UI/UX design
    Web3 wallet integration

    Problem & Solution

    • Short timeframe to deliver the MVP,
    • Looking for a partner who will take over the project end-to-end,
    • Strong need for a smooth handover process after development.
    • Providing the team capable of delivering the end-to-end solution,
    • Adjusting the workflow to a short timeframe,
    • Keeping the Client close in the loop to make sure we are meeting the expectations.



    Due to a short timeframe, we’ve immediately established an efficient a quick communication channel with the client.

    The client has received the pricing estimation one week after the first conversation,

    Our team has started with UX/UI based on the results of the workshop with the designer and the client.

    We’ve introduced daily alignments with the client over Slack.

    Our team worked in two weeks sprints.

    We’ve run a dynamic multistage development and implementation to meet the deadline; the product went live as soon as possible and subsequent iterations were built atop of the MVP.

    Delivered value.

    • The MVP was delivered one month after signing the contract,
    • The high quality of the delivered solution was maintained despite a concise timeframe,
    • The Client has met their internal deadline and business goals,
    Review Quote
    Your team is doing a great job, working fast, and always seems to understand what we need and deliver high quality. I’m happy that we managed to deploy the create token functionality to production on time. It was an important milestone.
    I feel like all people you brought on to the project are competent and know what they are doing. I like the quality that is being delivered
    You put a lot of attention on us, which I appreciate. I’m very optimistic about our cooperation 🙂
    Radko Albrecht
    Founder, and CEO at Bitbond

    Find out more about our collaboration with Bitbond

    Watch the interview with Bitbond’s founder and CEO – Radoslav Albrecht
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    Used Technologies.

    EVM compatible networks: Ethereum, Polygon, Avalanche, and Binance Smart Chain
    Wallets: Metamask, WalletConnect, Coinbase Wallet

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    Enelion is a manufacturer of electric car chargers and ecosystem management software for electromobility. The company has been designing electronics and manufacturing equipment in Poland since 2016 and has delivered several thousand chargers to customers at home and abroad. Enelion is also developing charger network management software to provide operator and charging service providers. In addition to foreign customers, Enelion cooperates in the Polish market with PGE, Tauron, Energa, Polenergia, and Greenway.
    Experience we shared
    Efficient Systems Data management
    Software integration

    Customers’ business goals

    The simple provision of chargers to tenants and billing of energy consumed in the administration system.

    Optimal use of available power in the building.

    Protection against network overload in an office building or parking lot.


    A search for users optimization

    Here we have used PostgreSQL algorithms (ltree) for representing labels of data stored in a hierarchical tree-like structure.

    Closest stations search optimization

    We have employed a PostGIS, a spatial database extender for PostgreSQL object-relational database. It supports geographic objects allowing location queries to be run in SQL.

    Communication between applications and queuing

    We have deployed an open-source message broker, RabbitMQ, which can be deployed in distributed and federated configurations to meet high-scale, high-availability requirements.

    Hardware management integration between apps

    The goal was to provide remote access to the status of the charger but also to allow users and end-users to control the charger, e.g., switch it on/off remotely. Both groups are using different apps to perform these activities. Our team accessed the chargers software backend and conducted the integration from that level.

    Process software development team has been working together with the client’s team. The work has been aligned with the scrum methodology.

    Delivered value:

    • A search of charging stations,
    • Chargers booking,
    • Payments monitoring,
    • Integration of the platform with end users’ mobile app,
    • Remote control over the charger stations (start/end, status),
    • Users division (Operators & Charging Service Providers) and access level control.

    The system allows dividing the network into smaller operators, who will only have access to their devices. Charging Service Providers can check the status of the charging station. Thanks to the connection with the Enelink system, most of the maintenance activities will be performed remotely. Another feature was setting up a charging plan that will limit the station’s power at the right time if the Service provider chooses several Operators. All charging stations can be labeled. This makes it easier for Service Provider to manage the stations from a given label. Then, information about the stations’ availability can be easily shared with end-users in a few clicks.

    Dynamic Tariff solution gives an attractive offer for each end-user, encourages them to charge in specific places, and introduces discount coupons and VIP programs. Entering tariffs helps Service Providers optimize earnings at charging stations.

    Used Technologies:

    rabbit mq
    Flask Framework

    Are you interested in solutions for electromobility?

    Just leave your email address and we’ll be in touch soon
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