Atlantbh

For more than 25 years, we at Atlantbh believe that progress includes giving back. As we celebrate our important milestone birthday, we want to share the story behind a tradition that brings meaning to our work and our community. Through PlayITForward, we celebrate our company birthday by Giving Back: building playgrounds and improving learning spaces across Bosnia and Herzegovina. We didn’t plan this initiative, and the first playground happened, let’s say, spontaneously. Seeing the impact on the community, we have decided to turn it into a tradition. Why Playgrounds? We believe in long-term impact. Play fuels imagination, imagination sparks learning, and learning builds a better tomorrow. A playground is where kids learn collaboration, confidence, and creativity, simply by playing together. We wanted to create spaces that bring back the joy of outdoor play and the sense of community that naturally comes with it. Finding the Right Place for Play Each year, we search for a location where a playground can truly make a difference. The ideas often come from Atlanters, our teammates from all over Bosnia and Herzegovina who know their local communities best. They suggest schools or kindergartens, and together, we listen, plan, and build spaces that keep the sound of laughter alive.  Playing Across Bosnia and Herzegovina It all started in Mostar, where we built a playground at a day-care center that supports children from socially vulnerable families, run by the NGO Altruist with the support of Save the Children. The impact of that first playground stayed with us. It showed us that we don’t need grand gestures to create real change and became the foundation of what is now our birthday tradition. As our CEO, Jasmin Velić, says: “Building playgrounds across Bosnia and Herzegovina has become something of a tradition for us at Atlantbh. It’s a small way to support the families and communities we’re part of, and if it brings even a few smiles to kids, we consider that a win.” Over the years, we’ve built playgrounds in Vareš, Gradačac, and Trnovo, each one a reminder of how meaningful it is when a simple idea brings people together. This year, for our 25th birthday, we took things a step further and built two new playgrounds in Fojnica and Pločari polje, responding to the needs of these two schools for lasting impact. For us, this project has never been just about donations or building equipment. It’s about the visits, the conversations, the planting of flowers and trees, the murals, the games, the coffee shared with teachers, and the joy of watching a once-empty space turn into a place full of color, play, and connection. Each playground has brought our people closer to the communities around us. And we’ll keep taking the steps that create meaningful connections and shape the way we work, grow, and give back.

25 years of Atlantbh history is filled with projects, teams, and results we’re proud of. If you ask any Atlanter what truly makes us special, chances are they won’t start by talking about technology. They’ll talk about how we work together. That way of working is deeply rooted in our values. They act as our compass, guiding how we collaborate, learn, and deliver excellence. Driven by high standards and a shared sense of purpose, Atlanters live Expertise, take Ownership, embrace Growth, and Collaborate. Together, these four form the foundation of one encompassing value: Excellence.   We excel by gaining and sharing Expertise Expertise at Atlantbh grows through curiosity, experimentation, and shared learning. Over 200 Atlanters bring unique experiences and perspectives, and real progress happens when knowledge circulates: from one person, to the team, to our clients. Every project, challenge, and idea becomes an opportunity to refine our craft and raise the standard for what great work looks like.   We excel by taking Ownership Ownership is trust in action. It means taking full responsibility from the initial idea to the final delivery, with a focus on quality, transparency, and accountability. Atlanters take initiative, communicate clearly, and stand behind their work. Ownership creates reliability within teams and builds confidence with our clients, ensuring that every commitment is met with integrity and consistency.   We excel through constant Growth Growth is what keeps us moving forward. At Atlantbh, we view challenges as opportunities to learn, adapt, and evolve. Atlanters lead by example, continuously improving their skills and exploring new technologies. Every sprint, every project, every reflection helps our development, individually and collectively. When one Atlanter grows, we all grow as a team, a company, and as partners to our clients.   We excel through Collaboration Collaboration is where expertise meets shared purpose. We make decisions together, communicate openly, and align expectations, both internally and with our clients. Collaboration is what turns teamwork into long-term partnerships and shared success.   Excellence is our standard Excellence is a way of working at Atlantbh. You can spot it in the details: in how we communicate, deliver, and improve every day. It’s visible in every sprint, every feature, and every interaction that makes us better as a team. One thing that hasn’t changed in 25 years is our belief that excellence is something you create together. With an excellent team, that creation looks easy. 💙

As we celebrate 25 years of Atlantbh, we’re doing so in a way that reflects who we are: by investing in our Scholarship program and helping shape future engineers.  To mark this milestone, we’re awarding 25 scholarships to students across Bosnia and Herzegovina who are pursuing STEM-related fields. That’s more than double our usual program of 10 annual scholarships, because we believe that supporting education is the best way to strengthen and grow our tech community.  Through this program, selected students will also get the chance to meet our team, gain first-hand insights into the IT industry, and connect with people who’ve built their careers in tech. If you’re an IT student eager to learn and grow, we are excited to meet you. Learn more about this opportunity: Scholarship Details Twenty five scholarships will be offered to first and second-year university students enrolled in IT-related study programs. There are two scholarship categories: Category 1: 15 Scholarships for students who have demonstrated outstanding achievement in academic and non-academic activities. Category 2: 10 Scholarships for students coming from low-income households. Scholarships will be provided over a 10-month period, valued at a total of 2000 BAM for Category 1 and 3500 BAM for Category 2. Eligibility Criteria       Academic achievement during studies;       Membership in student organizations and other relevant extracurricular activities;       Participation in relevant student competitions (in IT-related fields), on both the domestic and international levels;       List of projects in which the student participated (not related to course work such as assignments and similar);       Social status (only for Category 2). For more details about the conditions, eligibility and the application process, click here.  Eligible students are encouraged to apply by the 31st of October 2025 by completing the application form available here:       Scholarships for students with outstanding achievements.       Scholarships for students coming from low-income households.  

What keeps a company growing for 25 years? We believe it’s the choices you make when no one’s watching. The everyday decisions that build trust, create impact, and shape the future. At Atlantbh, sustainability has always been part of how we work, how we grow, and how we lead. From Bosnia and Herzegovina to the global tech stage, we’ve stayed true to one belief: “Progress only matters if it creates real impact: for people, for communities, and for the future.” Jasmin Velić, CEO That belief is at the heart of our first Comprehensive Sustainability Report. It reflects the meaningful change we strive for every day. Why This Report Matters When we talk about sustainability at Atlantbh, we are talking about creating value that lasts. We are talking about building trust, setting high standards, and delivering impact across every part of our work. Our sustainability story is a part of how we hold ourselves accountable and inspire others to do the same.  This report reflects that commitment, covering the progress, initiatives, and values that guide us: Governance and Integrity Upholding transparency, ethical decision-making, and accountability in everything we do. Better Work Environment Creating a culture where our people feel supported, empowered, and valued. Going Green Reducing our environmental impact by Going Green, using digital solutions, supporting clean-ups, and promoting local tourism. Giving Back Support our communities, investing in education, creating opportunities, and investing in community through Giving Back initiatives. A Glimpse of Our Commitment in Action Sustainability is more than a statement. It’s action you can see, inside and outside our company. After 25 years of building that impact, here are just a few of the results we’re proud to share: Reduced our operational footprint by 26% in the past year  98% of our teams operate fully digital, significantly minimizing paper usage 42% of our team are women, driving progress through diversity Every new team member joins through an onboarding process that puts inclusion first Over 250 students have taken part in our internship program, and 70 received scholarships 4 children’s playgrounds built to support safe, joyful spaces for families All of this is guided by our integration of Environmental, Social, and Governance (ESG) principles and the UN Sustainable Development Goals, making sure values and business are always aligned.  Partnering with Purpose We believe success is measured by more than what people see. It’s measured by the integrity behind every decision, by the impact that lasts long after the spotlight fades and the drive to stay ahead in all areas of business.  Explore our full Sustainability Report and discover how we create value every day.

This March, we celebrate a major milestone for Atlantbh: 25 years of tech excellence, growth, and impact.  What began as a small team with big ambitions in the heart of Europe has evolved into a company that delivers quality software and invests deeply in people—its engineers, partners, and the local community. Lasting Impact Since 2000, we've helped our global clients stay ahead with high-quality, reliable solutions while creating opportunities for local engineers to work on challenging, long-term projects.  We have navigated the evolution of technology, adapting to new tools, methodologies, and challenges. Growing with our clients, partners, and team, we've seen firsthand the power of collaboration and the drive of young talent. Our culture of ownership, continuous learning, and excellence defines who we are today and will continue to shape our future. Beyond business, we have remained committed to nurturing talent, supporting education, and strengthening our community. Our core mission has remained the same through every transformation: to deliver excellence while building an environment where people and ideas thrive. Celebrating with Purpose Rather than hosting big celebrations, we are marking our 25th anniversary in a way that best aligns with our core values. We will increase our investments in future engineers and give back to the community. As part of our ongoing commitment to education, we will award 25 scholarships to students across Bosnia and Herzegovina pursuing STEM-related fields by the end of the year. These scholarships will support exceptional students who have demonstrated academic excellence, motivation, and a passion for technology. By providing resources and opportunities, we aim to support the next wave of innovators—individuals who will shape a better future with their tech expertise. At Atlantbh, we believe that investing in education is the most powerful way to build a stronger, more innovative tech community. Looking Ahead As we celebrate this milestone, we see it not as the culmination of our journey but as the foundation for what comes next. Moving forward we will continue to do the work towards our commitments and lasting impact by Creating Opportunities – working on long-term projects and partnerships that shape industries Investing in Education – supporting young talents with knowledge, skills, and mentorship Strengthening Our Culture – Cultivating an environment where Atlanters thrive, lead, and make an impact Championing Sustainability – Supporting our community and environment with care and responsibility. To our clients, partners, and Atlanters—thank you for being part of this incredible journey.

On October 17, Atlantbh will attend an international AI event: the Applied Artificial Intelligence Conference (AAIC) 2024 in Vienna. This conference will gather AI professionals from over 70 nations and focus on AI’s real-world impact and business applications. With over seven years of hands-on experience in this rapidly evolving field, it’s great to reflect on our journey in incorporating AI into products and how we implemented it back then within existing and new projects. Now, a few years later, working with clients from multiple industries has enabled us to gain huge insights into how incorporating AI can accommodate every business need and translate problems into solutions. At AAIC, we are looking forward to showcasing our expertise in custom tech solutions powered by AI, meeting like-minded professionals and organizations, and learning about existing trends. If you are also attending the conference, we would love to connect with you and explore how our tailored AI solutions can be customized to meet your specific business needs, and how we can collaborate to drive innovation and success in your industry.

A new academic year comes with new scholarship opportunities at Atlantbh! For the seventh year in a row, we’re excited to open up our scholarship program to IT students across Bosnia and Herzegovina.  Along with financial support, the program provides the chance to connect with our experts and learn about the IT industry, rewarding your hard work and fostering both academic and professional growth.  If you're an IT student eager to learn and grow, we are excited to meet you. Learn more about this opportunity: Scholarships offered Ten scholarships will be offered to first and second-year university students enrolled in IT-related study programs. There are two scholarship categories: Category 1: Students who have demonstrated outstanding achievement in academic and non-academic activities. Category 2: Students coming from low-income households. Scholarships will be provided over a 10-month period, valued at a total of 2000 BAM for Category 1 and 3500 BAM for Category 2. Eligibility criteria Academic achievement during studies; Membership in student organizations and other relevant extracurricular activities; Participation in relevant student competitions (in IT-related fields), on both the domestic and international levels; List of projects in which the student participated (not related to course work such as assignments and similar); Social status (only for Category 2). For more details about the conditions, eligibility and the application process, click here. Eligible students are encouraged to apply by the 25th of October 2024 by completing the application form available here: Scholarships for students with outstanding achievements.  Scholarships for students coming from low-income households.   

Atlantbh is attending Europe's thought-leading insurtech conference ITC DIA Europe - Amsterdam 2024.  With over two decades of experience, Atlantbh has led digital transformation for numerous high-profile clients worldwide, including multiple Fortune 500 companies. For the past seven years, we have assisted industry leaders in enhancing efficiency and refining operations by leveraging data and AI technologies.  Knowing when to implement AI is essential. Our expertise lies in identifying manual processes that can't scale and pinpointing areas where personalization and automation can enhance customer experiences. This is demonstrated through our recent successful collaboration implementing AI (Machine Learning) to transform customer service for a client, where we developed an AI-assisted solution to accurately categorize customer inquiries, issues, or complaints, increasing responsiveness and efficiency. Our expertise spans multiple industries, with six years dedicated to developing tech solutions for the insurance sector. The collaborative partnership has resulted in high levels of innovation in software solutions delivered to the client and continuous improvement in the quality of services offered by Atlantbh. The main challenges and solutions of this collaboration are detailed in our case study, Unified Processing of Small and Big Data Sets. We are looking forward to the opportunity to contribute to the digital transformation in the insurtech industry, particularly with the increasing use of AI.  If you are attending ITC DIA Europe, contact us to schedule a meeting with our representatives in Amsterdam.

Lately, we have seen a significant number of discussions regarding the potential of AI and strategies to incorporate it into the product development process. Specifically, how can we enhance, innovate, and scale in ways we were not able to do before? In this interview, we asked Samra Tanovic, our VP of Analytics and Services, to share her experience and insights on this topic. Q: Could you tell us about your journey in incorporating AI into products?  A: Several years ago, when the need for AI functionalities arose within one of our projects, we were faced with a series of questions: How do we enable our teams and build the expertise needed for such tasks? How long will it take? How do we spot opportunities for the use of AI faster? What are the costs? The key takeaway from this experience is that it’s essential to start with an explicit, well-defined goal that aligns with business and user needs and then to consider how AI could assist in achieving this goal. It’s important to recognize that not all problems require an AI solution. There’s a risk of wasting significant time and resources trying to utilize AI just for the sake of using it. Instead, we learned to carefully examine existing challenges and ask: Can AI solve this problem more efficiently? Is there potential for scalability? Does the benefit of using AI outweigh the cost? Our journey also taught us that data science projects, unlike standard software engineering, involve a series of experiments that may or may not lead to discoveries or entirely new lines of research. The inherent uncertainties in this process make it difficult to define and control, which is why a unique management approach is required. Another key aspect we realized is the importance of the data that AI uses and produces. While the size of the training data does impact the model’s performance, data quality often plays a considerably more influential role. Investing in clean, relevant, and diverse data preprocessing turned out to be essential for accurate AI outcomes, making it an investment worth prioritizing. Finally, in order to build expertise and prepare our company for the rapidly evolving AI landscape, we set up a team dedicated to the research, development, and implementation of cutting-edge AI solutions within existing and new projects. This step allowed us to continue innovating and incorporating AI where it made sense, enhancing both our products and our ability to adapt to future technological advancements. Q: Could you provide examples where your teams used AI to enhance problem-solving efficiency? A: The main criterion is to consider processes that are excessively manual, do not scale well with product growth, or areas where customer experiences could be improved through personalization or automation. ML is mostly required in cases when the targeted behavior cannot be accurately expressed in software logic without relying on external data sources. A few years back, we used AI to analyze text and extract common business attributes, such as business category, address and operating hours from an unstructured text, a process that was previously time-consuming for humans. The efficiency of the validation of the business attributes, which was our main use case, was increased by 80%. An interesting and simple example involved removing a common UI element, a dropdown menu, and using classification to determine the correct option automatically. Previously, selecting the wrong option led to downstream problems and complicated user experience. However, by leveraging ML to accurately determine the appropriate dropdown option based on the user’s input, we managed to reduce mistakes by 30%.  Moreover, an intriguing area to consider is threshold replacements. Traditional business rules with set thresholds are often used to trigger specific actions. However, these thresholds can be too rigid and may not adapt well to changing circumstances. AI models, in contrast, can learn from data patterns and make more flexible, dynamic decisions.  Q: Lastly, drawing from your experience, what are some obstacles that businesses might encounter while implementing AI, and what strategies can they employ to overcome them? A: One of the biggest challenges is the lack of trust in AI-powered solutions. Many businesses are hesitant to rely on AI for critical decisions because they don't fully understand how algorithms work or the resulting accuracy is not meeting their expectations.  AI systems are designed to learn but can make errors or provide inaccurate results based on various factors, such as the quality of the training data, complexity of the task, and limitations of the algorithms used.  In scenarios where explicit and deterministic results are critical, businesses should carefully assess the suitability of AI models. Hybrid approaches that pair AI with deterministic methods should be considered, along with models with high interpretability, to maintain control and build trust. It is also useful to be transparent about limitations and potential biases.  Speaking of biases, if not designed and trained with fairness and ethical considerations in mind, AI systems have the potential to perpetuate biases and discrimination. Ensuring compliance with regulations, regular audits, testing for bias and seeking legal advice can help. Q: Thank you. It was a pleasure discussing this topic. A: Thank you. I'm excited to see how AI continues to shape and enhance the field of product development. With over 6 years in the AI field, Atlantbh can help you utilize the potential of AI to optimize existing business operations and products. Find out how we can help, contact us.

Make it work, then make it fast Atlantbh had the opportunity to implement a back-end software solution for an insurance company. The client required a data processing engine able to transform different sizes of data varying from a few kilobytes to dozens of gigabytes, using the same mechanism. Having a unified engine which would have the same transformation logic both for the files containing 2 records (small data sets) and 20 million records (big data sets) was important to achieve simplicity. From the functional point of view, following requirements had to be addressed: To start a processing flow, user needs to provide the input data, along with the configuration data set. Both are provided using the predefined schema and are validated by the system. Once triggered, each processing flow contains multiple logical steps, each of them transforming the data. Processing flow is dynamic and driven by the configuration. Complexity is very different for each step (from simple operations to application of very extensive business rules). Files given as input and produced as output in each step, often need to be in a different format and sometimes the number of files cannot be predicted in advance. There was also set of non-functional requirements: Performance requirements are given for both small and big data sets and defined as highly important aspect for business. For example, small data sets such as files up to 10k records need to be processed in a range of minutes and should never be blocked by the big ones. There is also a minimum number of processes that should run in parallel, without affecting performance, etc. Different customers should be able to use a system and have full control over their data. They shouldn’t be affected by other users by any means, and access to their data should be secured. It should be possible to control priorities for the different processing flow types. Faced with the combination of business and technical constraints, we needed to come up with a right architecture that will be both easy to extend and maintain. On top of this, a working solution was needed within a short timeframe, in order to enable testing and verification of complex business rules. Challenges Challenge 1: Technology which will support required functionalities needs to be chosen Solution: The choice of technology to be used for data processing and transformation was already made by the client. They decided on Apache Spark, considering that it can support the scalability required by the system. This way the development team is limited by the technology they can use, but are free to use it in a way that will support all business cases – known at the time and possibly needed in the future. Challenge 2: Define how Apache Spark will be used to support all business cases. Possibilities and Solution: To make an adequate decision on how to design the architecture and implement certain functionalities using Spark in a way that would support all business cases, two approaches were considered: usage of Thrift Server, and writing custom Spark jobs using Scala, submitted for processing via custom Process Manager. 1) The usage of Thrift server seamed simpler for implementation but had limitations that would complicate things in the long run With Thrift Server, the main focus would be primarily on describing how the actual data transformations would be performed, as all the responsibilities related to the actual technical usage of the Spark cluster would have been shifted to the Thrift Server. One processing context is used and reused. Jobs would be performed over the same context in case of a small data set. Ensuring that any unnecessary overhead for preparation of processing was removed. On the other hand, the thrift server available inside the used platform seriously limited the flexibility. Only SQL language for transformations was allowed - seriously limiting the transformation capabilities for more complex business cases.  Data IO was limited to the same data format and no option of fine-grained control over resources or parallelism configuration, which is integral for fine-tuning the transformation processing for different sizes of data. 2) Writing custom Spark jobs using Scala was a more complex solution The benefit was that it gave the development team the needed flexibility and freedom in designing the jobs. It required more expertise on the low level of abstraction and would shift the responsibility of resource allocation for specific jobs on the development team. But this way, each processing job would be independent and more flexibility could be achieved, since Scala does not have as many limits as SQL and it can be combined with other languages this way. Considering both options but having in mind the requirements given in the introduction, it was decided that we would go with the second solution. More flexible solution was chosen to ensure that new problems and constraints will not emerge along the way and the team had the expertise to face the challenge. Challenge 3: Meet performance requirements despite the different challenges Moving further, the chosen solution showed certain fallbacks related to the performance. Spark was running on the Hadoop PaaS platform, which used YARN as a resource manager. It meant that each Spark job required the following steps to be executed (List 1*): job acceptance, resource allocation, spinning up of a new context, read resource(s) from HDFS, execution, write output resource(s) to HDFS and shut down used context. This brought unnecessary overhead to each job, which was prominently visible in case of small data sets, where supporting steps could have taken more time than the main execution step. Solution: Due to challenges faced and time constraints given, we agreed with the client to adopt the approach: make it work, then make it fast. We enabled the client to perform business rules validation, while the team worked on the optimizations to make performances better. Optimizations were a multipart process, but we would emphasize three parts that made the most impact. 1) Overhead reduced by chaining smaller jobs into large one Initially, each step was implemented separately, emphasizing the issue with the YARN process manager and stage overheads - going through the same stages (List 1*) for each part of the transformation process. Therefore, we decided to chain all jobs (executions) into one large job, where input and output operations were performed just once at the beginning and the end of the encompassing job instead of each individual step. This required complex framework outside of the jobs to configure, correctly orchestrate and take the burden of reading/writing operations instead of Spark jobs. 2) “Fast-lane” introduced for the small data sets On a larger scale, outside of a single data transformation flow, there were performance overheads with running small data sets on a full-blown actual Spark cluster. The main issue was that, the overheads coming from Spark resource scheduling, contact initialization and driver - executors communication were not justifiable for relatively small job executions which came with smaller datasets. Change has been made to run small data sets in existing pre-configured local Spark contexts, without changing or separating implementation of the business rules. With the introduction of a “fast-lane” for running smaller spark jobs on an already pre-existing context, the overhead from the usual spark application spin-up has been removed - effectively reducing the processing time of small jobs to only the time taken to actually execute the business logic. Large datasets continued to be executed on the Spark cluster, while small jobs would either go to the Spark Local Context or the cluster, depending on client requirements. 3) Maximized throughput with resource utilization configurations For scenarios where both large and small jobs are running on the cluster, there is the natural problem of properly utilizing the available resources to maximize the average throughput between job sizes. Therefore, each job’s resource utilization was dynamically configured based on the dataset size that needed to be processed. A queuing mechanism has been established, with proper job size and resource consumption configurations defined, based on the underlying cluster. Additional factor which we needed to take care of is to allow parallel executions of small and large jobs, but making sure that large jobs would not block the cluster by using all available resources. In the end, these Spark optimizations played a large role in achieving our final goal, meeting customer performance requirements! Conclusion When faced with a short deadline, it may seem more desirable to use technologies, tools or frameworks which require no or limited deep-dive knowledge about how they work under the surface. Nevertheless, we were aware that in this case it wouldn’t be the right approach. We considered not just the actual requirements which were sufficiently complex, but the possibility of them being significantly extended in the future which is very common for the type of the system we worked on. Therefore, we have decided to go for something more complex and flexible, which in the short run may have seemed like a bad decision, but in the long run paid large dividends. Two important factors influenced choosing this path. First, we had the personnel and expertise to be able to quickly adopt all the necessary knowledge. Secondly, we were able to identify with our client what needs to be done to make it ‘just work on time’ and then focused on the ‘make it fast’ part. This case study was written by Atlanters: Nejra Suljić, Irma Smajić, Dino Prašo, and Ridvan Appa Bugis. If you enjoyed it, share it with your friends and colleagues - or read more blogs about Big Data!