The global computer vision market is experiencing explosive growth, propelled by the urgent need for QA and analytics automation across several industrial and enterprise sectors. Valued at over $19.8 billion in 2024, the market is projected to grow significantly, reaching $58.29 billion by 2030, with a compound annual growth rate (CAGR) of approximately 19.8%. This rapid expansion is driven by the demand for sophisticated applications in quality control, security, and autonomous systems. Continue reading →

Over the past few years, AI has evolved from an experimental initiative to a strategic growth engine driving digital transformation across enterprises. The investment trends reflect this shift, propelling the AI technology market toward an estimated $1.8 trillion by 2030. Companies are no longer asking if they should invest in AI, but the question is now “how much does AI development cost?” and what ROI it can bring, as well as how to build a financial roadmap.

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In a marketplace defined by infinite choice and limited attention, visibility is no longer accidental but engineered. Today, 44% of online shoppers start their product search with search engines, not marketplaces or brand websites. [Source: Klarna Insights] This means the battle for growth begins long before a customer reaches your product page.

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For years, WordPress has dominated the web‑development space, powering millions of sites globally with its flexibility and ease of use. However, as demand for faster and more efficient development grows, traditional workflows have begun facing limitations. They are often slow, resource-intensive, and show signs of strain as businesses require quicker turnaround times and intelligent features to retain customers. But thanks to AI and vibe coding, this is changing.

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Artificial Intelligence has transitioned from being a powerful experimental tool to becoming the backbone of business infrastructure and digital transformation. Across industries, AI is no longer a technology change but an operational necessity. From autonomous Agents to multimodal intelligence, 2025 is the moment when businesses move from experimenting with AI to relying on it completely. Integrating AI into business operations is a complex process that needs significant investment and expertise. The key challenges experienced are data privacy, ethical standards, and technical skills. Despite these challenges, the use of AI technologies has doubled since 2023, with nearly 65% of companies already adopting them.

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More than 40% of websites are still powered by WordPress; hence, it is not only a digital pillar but also a prime target of any cyberattack. By 2026, advances in WordPress security will already be a board-level issue, rather than a technical maintenance issue. The attackers are also using automation, AI, and zero-day exploits to compromise unprotected sites quickly than traditional defenses can respond. The first line of plugin-based defense, which was used as a means of protection, has been rendered ineffective in providing security on the enterprise level.

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Software Development Lifecycle (SDLC) is the backbone of every enterprise technology stack. However, despite the automation offered by DevOps, CI/CD pipelines, and agile frameworks, organizations still experience the same bottlenecks: delays in planning, test duplication, conflicts during the integration process, and friction during deployment. Continue reading →

Mobile app development in 2026 will fundamentally redefine how enterprises design, deploy, and scale digital experiences. The era of static, monolithic applications is giving way to cloud-independent architectures, AI-native frameworks, and privacy-first ecosystems built to operate seamlessly at the edge. In this new paradigm, mobile apps are no longer standalone utilities; they are intelligent service layers that connect users, data, and business systems in real-time. Continue reading →

AI is no longer a nice-to-have for delivering remarkable customer support; it is mission-critical. In fact,
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When ChatGPT launched in November 2022, foundational models and generative AI triggered an enterprise gold rush. Companies across every sector scrambled to integrate AI into their operations, driven by board-level pressure and fear of competitive disadvantage. Budgets materialized overnight, and pilot programs launched at unprecedented speed. Fast forward to today, and the results tell a different story: over 90% of generative AI pilots are failing to reach production.

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