What would you like to explore?

Discover how AI streamlines AWS migration with automation, visibility, and post-migration optimization for scalable, cost-efficient operations.

Streamline your cost-performance strategy with Lambda Power Tuning for smarter application management.

Elevate your operations with SLA Automation: set new standards for reliability and exceptional customer service.

Discover AI delivery best practices to prevent technical debt, improve scalability, and ensure reliable, long-term business impact.

A strategic approach to modernizing code At e-Core, we often partner with organizations whose software technically “works” in production, but just barely. Over time, shifting teams, competing priorities, and tight deadlines lead to codebases that are fragile, expensive to maintain, and risky to enhance. In this context, software refactoring becomes more than a technical task. It’s a business strategy that preserves continuity while unlocking future growth. Defining refactoring: Evolution without disruption Software refactoring is the disciplined restructuring of existing code, improving readability, modularity, and performance, without altering its external behavior. Unlike a full rewrite, refactoring delivers incremental value by: Accelerating feature delivery through cleaner, more modular code. Reducing operational risk by eliminating brittleness and regressions. Lowering total cost of ownership by making enhancements less labor-intensive. Why refactoring matters for business  Healthy codebases have a direct business impact: Faster Time-to-Market: Modular components support parallel development and faster releases. Greater resilience: Decoupled services isolate failures and simplify maintenance. Easier onboarding: Clear architecture and design patterns help new developers ramp up quickly. Sustainable cost savings: Less firefighting, fewer regressions, and lower maintenance overhead. What may look like a simple “cleanup” is often the first step toward digital reinvention. Refactoring techniques In enterprise environments built on Java, C#, or Python, we apply proven object-oriented refactoring techniques to increase cohesion and extensibility: Class decoupling: Split monolithic classes into focused, single-responsibility components. SOLID principles: Apply interface segregation and dependency inversion to stabilize architecture. Composition over inheritance: Build flexible, interchangeable structures. Testability enhancements : Isolate side effects and inject dependencies for better test coverage. These techniques form the foundation of our incremental software refactoring roadmaps. Beyond OO: Choosing the right tools for each paradigm Modern languages blend programming paradigms. We help clients apply the right tools to the right problems: Functional patterns in Python to create concise, side-effect-free data pipelines. Kotlin/Scala hybrids that combine OO structure with expressive functional constructs. C# LINQ and Async for declarative data queries and scalable concurrency. This multi-paradigm approach ensures that systems remain robust, flexible, and expressive. A refactoring story: From debt to differentiation Case Study: Regional logistics provider

AI customer support has moved beyond simple automation; it’s now a critical driver of efficiency, cost reduction, and customer satisfaction. However, many organizations still struggle with implementation, especially when it comes to balancing AI-driven automation with human expertise. This article brings together key insights and market observations to answer some of the most pressing questions about AI customer support, highlighting practical ways teams are using AI to enhance operations. How can AI improve efficiency in customer support? AI is transforming customer support far beyond the realm of simple chatbots. By automating repetitive tasks, optimizing workflows, and delivering actionable insights, AI enables support teams to resolve tickets faster, reduce costs, and enhance customer satisfaction. AI-driven models have already improved ticket severity assessment, escalation management, and automated first responses, allowing teams to operate more efficiently and focus on high-value interactions. What are the most impactful AI-based use cases in support? Many companies are moving beyond basic automation to adopt high-impact solutions in AI customer support. Examples include: Chatbot Agents : When integrated with knowledge bases, modern AI chatbots now reach over 85% accuracy in self-service scenarios ( Gartner, 2024 ), easing the load on support teams. Predictive Analytics : By analyzing historical interactions, predictive models help anticipate and prevent issues before they escalate, cutting resolution times by up to 30%. Sentiment Analysis : AI detects emotional tone in customer messages, enabling timely interventions and improving loyalty. Smart Ticket Distribution : Assigning tickets based on agent expertise and customer sentiment ensures faster resolutions and fewer escalations. Organizations testing AI-driven models have seen manual effort on routine tickets reduced by as much as 70–80%, demonstrating AI’s value in scaling support efficiently. How do you ensure high-quality chatbots in AI customer support? The evolution of AI chatbots has made them far more capable than their rule-based predecessors. Success with AI customer support chatbots hinges on: Up-to-date knowledge bases Seamless handoffs to humans Sentiment-aware, context-driven responses Combining chatbots with analytics and automated ticket handling can help turn them into proactive enablers of support, rather than just reactive tools. What’s the ROI of GenAI in AI customer support? Many organizations implementing AI customer support solutions are already seeing measurable ROI: reduced resolution times, lower operational costs, and increased agent productivity. GenAI-powered tools contribute to more accurate ticket handling, proactive insights, and the ability to scale operations, turning support into a strategic function that goes beyond issue resolution. Is AI replacing human agents in support? Not at all. The real story of AI customer support is empowerment, not replacement. 75% of leaders using AI in support report that their teams have taken on more strategic responsibilities ( Gartner, 2024 ). By offloading repetitive tasks, AI allows agents to focus on empathy, complex resolutions, and innovation. Why is knowledge management essential for AI customer support? A strong knowledge base is the backbone of successful AI customer support. Structured, searchable, and regularly updated repositories ensure that AI solutions, from chatbots to sentiment analysis tools, can deliver accurate and reliable assistance. Consolidating documentation, feedback, and ticket insights in a single platform enables both human agents and AI to perform at their best. 

e-Core We combine global expertise with emerging technologies to help companies like yours create innovative digital products, modernize technology platforms, and improve efficiency in digital operations.

Digital transformation is no longer optional—it’s necessary for businesses across all industries. In today’s fast-evolving landscape, organizations that fail to adapt their systems of work risk losing relevance and competitiveness. However, digital transformation isn’t just about adopting new technologies. It requires a continuous structural and cultural evolution that reshapes how work happens today and drives future success. Many organizations face a critical challenge: implementing technology without addressing process inefficiencies, cultural shifts, and organizational structures. This often leads to misalignment, data silos, and poor collaboration, directly impacting business outcomes. Companies need a structured, systematic, and business-aligned approach to achieve true digital transformation. This is where Atlassian’s System of Work comes in—providing an integrated set of solutions and best practices that empower organizations to streamline their transformation journey and maximize results. The four pillars of digital transformation  Digital transformation only succeeds when combined with organizational, agile, and cultural changes. Simply digitizing inefficient processes or investing in technology without preparing teams for a new way of working limits success. Atlassian’s System of Work supports this essential shift, often overlooked by organizations. These four interconnected pillars lay the foundation for long-term success: Digital transformation: leveraging digital technologies to optimize processes, enhance operations, and create new business models. Example: implementing AI and analytics to automate workflows. Organizational transformation: structural and cultural changes that improve efficiency and competitiveness. Example: breaking down silos and reorganizing teams for agility. Agile transformation: adopting agile principles to create faster, more adaptive processes. Example: implementing scalable agile frameworks across the enterprise. Cultural transformation: shifting mindsets and values to foster collaboration and innovation. Example: encouraging experimentation and continuous learning in the workplace. → Read more: six tips to create valuable automations in Jira What is Atlassian’s System of Work? Atlassian’s System of Work is a comprehensive approach to work management, built around a portfolio of powerful tools, including Jira Software, Jira Align, Confluence, Trello, Bitbucket, and Jira Service Management. This approach eliminates silos, enhances collaboration, and ensures transparency and efficiency across the organization—leading to greater predictability and agility in workflows. Why was the System of Work created? Atlassian’s System of Work was developed to address a critical business need: integrating all aspects of work so that diverse and distributed teams can collaborate effectively and transparently. It helps organizations overcome common challenges such as: Lack of alignment between strategy and execution Poor visibility into project progress Difficulty synchronizing different departments and functions Low predictability and complex change management With this structured approach, companies can align teams more effectively, eliminate inefficiencies, and accelerate value delivery to the market. Business impact: what’s in for companies? Adopting Atlassian’s System of Work drives tangible business benefits, including increased organizational scalability, data-driven decision-making, and improved operational efficiency. By reducing manual work and streamlining workflows, productivity rises while fostering a more collaborative culture. Additionally, optimized resource allocation reduces costs and accelerates time to market. Market data indicate that companies utilizing this approach experience significant benefits, including productivity increases of 25% to 30% and reductions in delivery times of up to 40%. Atlassian’s ability to seamlessly connect tools, processes, and data across teams drives this success. This interconnectedness — referred to as “network extension” in the System of Work framework — creates a more fluid and efficient digital ecosystem. How to use Atlassian’s Network Extension practically 🔹 Tool Integration: the System of Work connects tools like Jira, Confluence, and Trello into a unified platform, ensuring centralized data access. 🔹 Team Connectivity: it fosters collaboration across distributed teams, breaking down functional silos. 🔹 Automation & Workflow Optimization: Atlassian enables cross-functional workflow automation, eliminating bottlenecks and boosting efficiency. 🔹 Visibility & Transparency: from C-level executives to individual contributors, everyone has access to a single source of truth, ensuring real-time progress tracking. 🔹 Unified Communication: Platforms like Confluence and Slack integrations help centralize information, preventing data loss and fragmentation. → Read more: how Jira Service Management bridges IT and business goals How to implement Atlassian’s System of Work successfully Atlassian’s System of Work redefines organizations’ way of operating, connecting strategy, execution, and collaboration to fuel digital transformation. As a Platinum Partner, e-Core specializes in digital transformation, System of Work optimization, and Atlassian solutions implementation across enterprise environments. If your organization seeks greater strategic alignment, efficiency, productivity, and collaboration, reach out to us to accelerate your transformation journey.

When deciding which product to buy or service to subscribe to, we implicitly go through two stages of comparison. The first stage is more objective, and tends to focus on what the product or service offers in a binary sense. A product either offers something or it doesn’t. A given streaming service either has a specific piece of content (such as the complete Star Wars catalogue) or feature (such as supporting offline viewing) or it doesn’t. This is often an eliminatory stage, as options which don’t provide required features are removed from the comparison. The second stage however, is when we focus on the “hows” . This is when we compare the subjective experience of using the product or engaging with the service. This stage is where quality tends to shine. Most streaming services will probably have an app for your phone, but some of them run a lot smoother than others. It’s somewhat curious that whenever we make recommendations, it’s often in regards to how well a given product or service does something, and rarely about the actual objective functionalities it offers. Funny enough, we frequently leverage adverbs to stress those recommendations: efficiently, seamlessly, effortlessly, smoothly, etc. While actual functionalities are objective , one either has a given feature or doesn’t, it’s often the subjective traits that stick with us the most, and allow us to distinguish between an average product or service and an amazing one. In a similar fashion, we, as customers, tend to use the same mindset when requesting or ordering things. We often prioritize requesting objective features over subjective traits. What this means is that while it is simple and straightforward to request for additional Star Wars movies in a streaming catalogue, it is often confusing to phrase a request for a better overall experience on the existing app. Objective feature requests are also much simpler to implement, so they become easier targets for prioritization, while subjective capabilities are often locked behind extensive debate over how they can be achieved and how to measure the actual benefits of their rollout. Being harder to formulate can also lead to subjective requests being either misunderstood or outright skipped as being too generic or unclear. Alright, so it is somewhat tricky for customers to directly provide us with valuable insights regarding things we’re doing well and ones we can improve. How can we obtain these insights then? The answer is through knowledge and understanding of our own offerings and customers . Why context is the missing piece It should come as no surprise that the more we understand a given problem, the better the solution we can implement for it. This often translates directly to products and services, with greater knowledge of a product and its users, comes a greater ability to evolve said product in a meaningful way that retains customers and satisfies their needs. To this understanding of the reality surrounding a product, feature, service or issue, we give the name of context. Context is something of a group puzzle. What this means is that beyond just managing to put all the pieces in the right places, the challenge is that we, alone, seldom have all the pieces. We therefore need to identify which people can provide the missing pieces so we can complete the puzzle. When it comes to context, a very important information to keep in mind is that it tends to grow in silos. Normally each team, area or department will have a silo of information of its own. This is completely natural, and happens across all organizations to varying degrees. It is not necessarily something we want to avoid by definition. A member of the legal team would potentially find little benefit in having a deep understanding of the exact implementation of the software tool being maintained by the company. There could be benefits? Yes, of course there could. But the effort necessary to share such deep knowledge across such distinct areas might be more trouble than it is worth. In most cases, a shallow understanding of the bottom line of each of these silos is often enough for the other silos. There are however cases in which a deeper understanding between different areas can generate great benefits. Specifically when talking about tech companies, there is a very special case, and that is the formation of silos between product teams and support teams. On one side we have product development teams, which often hold the most in-depth knowledge about product capabilities and features. On the other side we have support teams, which tend to hold an extensive knowledge regarding how the product is used by customers, whether it be as intended or not. The remainder of this article goes through manners in which leadership can help enable this dissemination of knowledge across the teams, to mitigate the challenge provided by the natural formation of silos of knowledge. We’ve drawn the suggestions below from a forum conversation regarding this effort of better connecting product and support teams in order to enable the transmission of this deep knowledge across the borders of their silos. Creating space for sharing “interesting” observations One of the key components of brainstorming is the notion of bringing ideas and thoughts forward. This comes from the understanding that ideation is more of a process than a single event in time, and ideas often shift and transform, as they interact with other people and other ideas, before reaching their mature form. A powerful resource in this sense are the observations and comments from customers about your product or service. Through their extensive contact with customers, it is often support teams that have the largest knowledge base of these observations. This is mostly simple and straightforward information, things that frequently even sound somewhat obvious when said out loud. Another key trait is that these are not the usual complaints about problems. Those obviously need to be addressed in accordance, but focusing only on individual problems often means paying less attention to the wider scope. The form this can take will obviously vary based on your organization, but a simple Confluence page or shared Wiki-style page can go a long way in preserving and disseminating this type of information . This way, whenever the product team starts ideating over features or priorities, they can leverage these observations to expand their brainstorming process. Remember, these might not represent much individually, but once combined with other ideas and suggestions, they can quickly point towards effective and beneficial traits for your bottom-line. End-of-Day/Week Support Summaries Another interesting resource that can be used to connect product and support teams are support summaries. Whether this is a daily, weekly or even a monthly effort would depend on the size of the organization and the amount of customers. But the idea remains the same, at each given interval, a lead figure of the support team can compile a brief summary of what took place during that interval. Which is then filed and documented alongside the reports for other intervals. This type of report can provide great insight into trends and peaks of certain types of issues or requests, which in turn can be used to glimpse into existing points of friction or zones for potential improvement . Finding unexpected use cases The ability to recognize and identify emergent usage in a given space can be a very powerful tool to drive decisions about product backlog. The word emergent in this case is used to describe usage that is developing in a somewhat unexpected or unintended way. Identifying early on that your product is being used with a specific purpose that differs from the original design can allow for companies to leverage this emergent usage and benefit from what customers are actually leaning towards , or viewing your product as exceptional at. This can sometimes open up broad new horizons of demands your customers have but were never able to express. To serve as an example of what emergent usage can be, a product that helps people perform a given activity, such as track expenses might end up being used by parents to teach their children how to track and control their spending. This discovery enables a team to provide additional features or tailor the user experience to welcome this emergent use and incentivize its adoption. In a similar fashion, adjacent use cases can be perceived and explored upon, such as Duolingo including Math and Music lessons besides its traditional catalogue of language courses. Creating a framework for capturing context Often when running insight research over data and reports from user interactions, one of the main challenges is glimpsing at the complete context of the actual conversations they relate to. Documenting the actual complaints or requests is obviously fundamental, but by documenting additional contextual details, support teams can enable a much faster extraction of product insights. A thorough report can go a long way towards providing insight, but the thoroughness itself can present challenges. The goal is then always to find a balance between what is too much information and what is too little. Naturally there is no silver bullet or magic formula here, the key parts of the context to be captured will vary based not only on the product or service being worked on, but also by the maturity level of it, as well as its customers. Regardless of what exactly is being documented, it’s very important that these resources are forwarded to the product and research teams so the actual insights can be extracted. Validating research through support conversations While earlier we talked about the value of documenting the context of customer exchanges in order to facilitate research, there is an additional way in which the two can intertwine. By quickly running findings and features by support teams , it allows us to leverage their extensive context of the topic for validation. The support team’s positive reception of a proposal, be it a fix, an enhancement or an entirely new feature, can work as a form of green flag that the proposal is sound and viable . Likewise, hesitation and concern might serve as a signal that a new feature might generate confusion or clash with other existing flows, and might need further workshopping before it can be rolled out. This can also go far beyond a simple approval signal, and support teams can be included to a degree in the process of ideation for new features , enabling them to contribute with their extensive knowledge of customer behavior in order to guide the decision process. Closing thoughts As we’ve explored earlier, it is often challenging for customers to talk directly about the more subjective elements of products and services, even though these are often the driving force behind our preferences and experiences. In order to be able to identify what customers are not explicitly requesting , we need to understand them. Connecting the deep technical understanding of the product teams with the extensive knowledge of user behaviors and needs of the support teams , while no simple feat to accomplish, presents itself as a powerful opportunity for driving the development of better products or services.  A key notion to keep in mind is that although our short-term objectives might differ across teams, in the end, our objective is often one, which is to provide the best experience we can to our end-customers.

Artificial Intelligence solutions are increasingly integrated into business operations, transforming decision-making and process optimization. However, the adoption of AI data preparation still faces significant challenges. According to a global Precisely survey from September 2024, 66% of organizations say that poor data quality and lack of data governance are the main obstacles to the success of AI . This data highlights a critical point: the success of AI projects depends essentially on the quality of the data. For Artificial Intelligence to be effective, it is essential to rigorously collect, organize and prepare the data, guaranteeing the accuracy and reliability of the models . Without a reliable foundation, even the most advanced models fail to deliver real value. This raises a fundamental question: Is your company really prepared to turn data into a competitive advantage? Or are you still accumulating information without a clear purpose? Find out how to structure your AI data preparation process and unleash the full potential of AI to drive your business forward. The 3 essential pillars for a successful data journey An organization’s data journey is a dynamic and continuous process of evolution, where data centralization, quality, and governance become intertwined as the company matures. These three pillars are not isolated stages, but rather mutually reinforcing dimensions . Let’s see how they relate to each other and influence the evolution of data maturity in an organization: 1. Unifying your data: centralization and integration One of the first challenges in the data journey is overcoming the dispersion of information. Many companies find themselves with valuable data spread across several different systems , departments and formats, a scenario known as “data silos”. For example, imagine having customer data in a CRM, sales information in spreadsheets and stock data in legacy software. This fragmentation prevents a unified view of the business. The solution? Unify this data in a centralized repository, such as a data lake, data warehouse or cloud solution. This centralization ensures that everyone in the organization has access to a consistent and complete view of the data, allowing for more accurate analysis and informed decisions. Centralized access is one of the first steps toward a scalable and sustainable AI data preparation process . 2. Ensuring accuracy: data quality and reliability After unifying your data, the next step is to ensure its quality and reliability. In large volumes, data can easily become disorganized , inconsistent or inaccurate . This process of refinement is vital, as poor quality data inevitably leads to inconsistent AI models and, consequently, wrong business decisions. High-quality, reliable data is the foundation of any successful AI initiative. Machine Learning models, for example, are trained based on this data, so if the data is compromised, so is the model’s performance. By investing in data quality, your company guarantees that analysis and predictions will be accurate, and that the decisions made based on these insights will be more effective and strategic, reducing operating costs by avoiding errors and rework. When building your AI data preparation foundation, quality is not optional, it’s essential to enabling trust and value. 3. Protecting and managing: data governance and security With your data centralized and its quality assured, the next step is to establish a solid governance and security structure. Data governance ensures that information is used ethically, responsibly and in compliance with laws and regulations. It defines clear policies on who can access what data, how it can be used and how long it should be stored. At the same time, data security protects the company’s most valuable asset from unauthorized access, loss or damage. This involves implementing technical measures such as encryption, firewalls and intrusion detection systems, as well as organizational measures such as security policies and employee training. Effective data governance and security protect the company from legal and financial risks, and promote trust among customers and partners. By demonstrating a commitment to data protection, the company builds a solid reputation and opens doors to new business opportunities. Good governance is what turns a well-intentioned AI data preparation initiative into a secure, scalable, and sustainable strategy. Business benefits A well-structured data journey is not just a technical requirement, but a strategic differentiator that generates real impacts, such as: Faster and more accurate decisions: reliable data makes analysis more agile and strategic. Reduced operating costs: automation based on data optimizes processes and eliminates inefficiencies. Personalized experiences: data analysis makes it possible to create interactions that increase customer loyalty. These benefits reinforce the strategic role of data in the competitive positioning of companies , highlighting the importance of investing in its organization and preparation through a clear AI data preparation approach. Conclusion The data journey is more than a technical process; it is a strategic differentiator for companies that want to thrive in an AI-driven market. By centralizing, qualifying and governing their data efficiently, organizations are positioned to turn challenges into opportunities, driving innovation and generating tangible results. AI data preparation is the first step toward unlocking the full potential of Artificial Intelligence in the business world.  At e-Core, we help companies turn data into powerful insights, preparing them for the future of AI. Start your data journey today.

In recent years, artificial intelligence has shifted from a futuristic promise to an essential ally in the day-to-day work of development teams. AI for developers, such as code assistants, automated bug analysis, and intelligent test generation, are transforming the way we write software. Reducing repetitive tasks, accelerating delivery, and freeing developers to focus on what really matters, using technology to solve business problems. According to the report The Economic Potential of Generative AI by McKinsey (2023) by McKinsey (2023) , the use of generative AI in software development can reduce time spent on activities like coding, reviewing, and debugging by up to 45%. That’s a significant productivity leap, especially in roles like software engineering, where those tasks take up a large part of the daily routine.  PwC (PricewaterhouseCoopers) also highlights a notable trend: industries with higher AI exposure have seen productivity growth nearly five times greater.