Amtex has formed long-lasting relationships with numerous IT giants like Salesforce, AWS, Azure and TIBCO. It has curated a multitude of successful products like Securra Health, Huut and CONTINYOU

Amtex Systems partners with CYGNVSâ„¢ to provide Enhanced Cyber Resilience solutions in India

The partnership has been established by ContinYou, which provides a range of programmes to support children’s and young people’s learning with its focus on improving life chances and tackling the effects of child poverty.The councils are Dudley ...

COUNCILS AND CHARITY TO PROVIDE CHILDREN’S CENTRES IN PARTNERSHIP

ContinYou

continyou

No, You Don't Need a Highly Specialized Team to Build Your SaaS Application 

Cybercrimes are increasing at an accelerated rate. To put things into perspective, by 2025, the cost of cybercrime is predicted to hit a whopping $10.5 trillion annually. Statistics like these demand a significant need to enhance the data security posture in organizations across the globe. However, several obstacles affect the adoption of a robust data security framework, such as multi-cloud complexities, data and AI laws, and the limited capabilities of existing data detection and response, data loss prevention, and similar tools. Data Security Posture Management (DSPM) is a critical discipline that turns an organization’s focus from an infrastructure-first to a data-first approach to cyber security. This blog will discuss DSPM and the key capabilities and factors to consider when buying a solution for your data security needs. What is Data Security Posture Management (DSPM)? DSPM stands for Data Security Posture Management. Gartner coined the term DSPM a few years back in its Hype Cycle report. Gartner highlighted the need for enhanced data security measures or approaches to help prevent the growing instances of data breaches and overcome the complexities of multi-cloud that lead to those breaches. As mentioned earlier, DSPM is a data-centric approach to data security, be it for on-premises data stores, SaaS services, or hybrid multi-cloud environments. The solutions provide detailed insights into the existence and location of sensitive data across the environment, who accesses it or how it is accessed, and the security posture of the application or data stores where it resides. Core Capabilities of DSPM Organizations seeking the right DSPM solution to reinforce their data security strategy must recognize that not all solutions are created equal. In 2024, GigaOm released its first-ever assessment of DSPM in its Radar report. The research firm highlighted some key capabilities that must be fundamentally available in a robust DSPM solution. Let’s take a look at those capabilities. Data Discovery Data security starts with gaining visibility of all data assets across an organization, including its on-premise data stores, SaaS services, and other cloud environments, leaving no stone unturned. The DSPM solution helps an organization do that by automatically discovering its structured and unstructured data assets. Data Classification DSPM solutions automatically scan environments to identify and classify sensitive data based on its business value, regulatory requirements, and other industry-specific policies upon discovering data assets. Data classification is a critical process that helps organizations set appropriate controls and prioritize their most sensitive data. Data Flow Data movement across an organization's various systems, data stores, and applications is dynamic. Teams must visualize and understand the data flow across systems to evaluate its privacy and security controls. DSPM helps visually illustrate the data movement, enabling teams to trace the transformation of data and the associated risks. Risk Assessment Another critical feature of a DSPM solution is that it gives teams insights into the risks through a thorough assessment. DSPM continuously monitors the environments for various vulnerabilities, assigns risk ratings, and helps teams prioritize remediation. Data Access Governance One critical threat to sensitive data is excessive privileges that lead to unauthorized access or inadvertent exposure. DSPM leverages access insights and IAM integrations to help organizations gain visibility of data access and risks and set up appropriate access policies and controls based on users, roles, and permissions. It further paves the way to the principle of least privilege (PoLP). Security Controls Apart from access governance, DSPM further enables organizations to place appropriate security controls based on the classification of data and its relevant risk scores. Such controls may include data encryption at rest or in motion, dynamic data masking for secure sharing practices, etc. DSPM also helps automate the security controls across the environments to minimize the risk of human error. Data Lineage From data ingestion to retention, data transformation occurs continuously and rapidly. Tracking such transformation across its lifecycle becomes difficult when handling data at the petabyte scale. DSPM allows organizations to overcome this challenge by tracking the changes over time. Breach Response DSPM solutions are well-equipped to respond to data breaches as soon as they are detected. The tool leverages breach impact insights, such as the volume of data impacted, the affected identities, and the relevant regulatory requirements. These insights help the tool to automatically isolate the impacted systems, revoke access permissions on compromised data, and initiate the notification process. Enterprise Stack Integration DSPM shouldn't be just another siloed integration among other tools. In fact, it should offer a unified approach to data security by seamlessly integrating with the existing security stack. This key operational factor ensures that workflows remain seamless and the organization can maximize the security stack's value. Secure AI Like many other technologies, DSPM tools had to respond to AI's growing adoption and its unprecedented risks. The OWASP Top 10 for LLM Applications feature the top-most critical risks that could compromise the AI systems development lifecycle, resulting in legal fines, compliance violations, and reputational loss. Hence, advanced DSPM solutions must offer much-needed capabilities like data sanitization and redaction or LLM firewalls to help organizations safely adopt AI. Important Factors to Consider While keeping in mind the aforementioned capabilities, organizations should also consider the following factors when looking for a robust DSPM solution. Agentless Discovery It is imperative to ensure that the solution offers agentless data discovery across the organization's environment. This capability is necessary to enable fast data discovery and mapping while minimizing deployment complexities. Centralized Dashboard The tool should provide a centralized dashboard that aggregates various metrics and reports, allowing teams to simplify monitoring and provide real-time insights to stakeholders. Continuous Detection and Prioritization of Critical Data Exposure The solution should identify the most critical threats to the organization's security posture and implement immediate remediation controls. Data Lineage Mapping The tool should further offer data lineage capabilities, providing organizations with visibility into the transformation of their sensitive data over time. By tracking the data's lineage across its lifecycle, security teams can better understand the security gaps and thus establish optimized controls and policies. Automated Remediation DSPM tools should allow security teams to respond automatically to security incidents when they occur. This capability is essential in ensuring the integrity and confidentiality of the data. Scalability and Performance Lastly, organizations with hyper-scale environments should look for a DSPM tool that scales to accommodate the needs of their rapidly growing data. Cybercrimes are increasing at an accelerated rate. To put things into perspective, by 2025, the cost of cybercrime is predicted to hit a whopping $10.5 trillion annually. Statistics like these demand a significant need to enhance the data security posture in organizations across the globe. Cybercrimes are increasing at an accelerated rate. To put things into perspective, by 2025 , the cost of cybercrime is predicted to hit a whopping $10.5 trillion annually. Statistics like these demand a significant need to enhance the data security posture in organizations across the globe. 2025 2025 However, several obstacles affect the adoption of a robust data security framework, such as multi-cloud complexities, data and AI laws, and the limited capabilities of existing data detection and response, data loss prevention, and similar tools. However, several obstacles affect the adoption of a robust data security framework, such as multi-cloud complexities, data and AI laws, and the limited capabilities of existing data detection and response, data loss prevention, and similar tools. Data Security Posture Management ( DSPM ) is a critical discipline that turns an organization’s focus from an infrastructure-first to a data-first approach to cyber security. DSPM DSPM This blog will discuss DSPM and the key capabilities and factors to consider when buying a solution for your data security needs. What is Data Security Posture Management (DSPM)? DSPM stands for Data Security Posture Management . Gartner coined the term DSPM a few years back in its Hype Cycle report. Gartner highlighted the need for enhanced data security measures or approaches to help prevent the growing instances of data breaches and overcome the complexities of multi-cloud that lead to those breaches. Data Security Posture Management As mentioned earlier, DSPM is a data-centric approach to data security, be it for on-premises data stores , SaaS services, or hybrid multi-cloud environments. The solutions provide detailed insights into the existence and location of sensitive data across the environment, who accesses it or how it is accessed, and the security posture of the application or data stores where it resides. data stores data stores Core Capabilities of DSPM Organizations seeking the right DSPM solution to reinforce their data security strategy must recognize that not all solutions are created equal. In 2024, GigaOm released its first-ever assessment of DSPM in its Radar report . The research firm highlighted some key capabilities that must be fundamentally available in a robust DSPM solution. Let’s take a look at those capabilities. GigaOm released its first-ever assessment of DSPM in its Radar report Data Discovery Data security starts with gaining visibility of all data assets across an organization, including its on-premise data stores, SaaS services, and other cloud environments, leaving no stone unturned. The DSPM solution helps an organization do that by automatically discovering its structured and unstructured data assets. Data Classification DSPM solutions automatically scan environments to identify and classify sensitive data based on its business value, regulatory requirements, and other industry-specific policies upon discovering data assets. Data classification is a critical process that helps organizations set appropriate controls and prioritize their most sensitive data. Data Flow Data movement across an organization's various systems, data stores, and applications is dynamic. Teams must visualize and understand the data flow across systems to evaluate its privacy and security controls. DSPM helps visually illustrate the data movement, enabling teams to trace the transformation of data and the associated risks. Risk Assessment Another critical feature of a DSPM solution is that it gives teams insights into the risks through a thorough assessment. DSPM continuously monitors the environments for various vulnerabilities, assigns risk ratings, and helps teams prioritize remediation. Data Access Governance One critical threat to sensitive data is excessive privileges that lead to unauthorized access or inadvertent exposure. DSPM leverages access insights and IAM integrations to help organizations gain visibility of data access and risks and set up appropriate access policies and controls based on users, roles, and permissions. It further paves the way to the principle of least privilege (PoLP). Security Controls Apart from access governance, DSPM further enables organizations to place appropriate security controls based on the classification of data and its relevant risk scores. Such controls may include data encryption at rest or in motion, dynamic data masking for secure sharing practices, etc. DSPM also helps automate the security controls across the environments to minimize the risk of human error. Data Lineage From data ingestion to retention, data transformation occurs continuously and rapidly. Tracking such transformation across its lifecycle becomes difficult when handling data at the petabyte scale. DSPM allows organizations to overcome this challenge by tracking the changes over time. Breach Response DSPM solutions are well-equipped to respond to data breaches as soon as they are detected. The tool leverages breach impact insights, such as the volume of data impacted, the affected identities, and the relevant regulatory requirements. These insights help the tool to automatically isolate the impacted systems, revoke access permissions on compromised data, and initiate the notification process. Enterprise Stack Integration DSPM shouldn't be just another siloed integration among other tools. In fact, it should offer a unified approach to data security by seamlessly integrating with the existing security stack. This key operational factor ensures that workflows remain seamless and the organization can maximize the security stack's value. Secure AI Like many other technologies, DSPM tools had to respond to AI's growing adoption and its unprecedented risks. The OWASP Top 10 for LLM Applications feature the top-most critical risks that could compromise the AI systems development lifecycle, resulting in legal fines, compliance violations, and reputational loss. Hence, advanced DSPM solutions must offer much-needed capabilities like data sanitization and redaction or LLM firewalls to help organizations safely adopt AI. Important Factors to Consider While keeping in mind the aforementioned capabilities, organizations should also consider the following factors when looking for a robust DSPM solution. Agentless Discovery It is imperative to ensure that the solution offers agentless data discovery across the organization's environment. This capability is necessary to enable fast data discovery and mapping while minimizing deployment complexities. Centralized Dashboard The tool should provide a centralized dashboard that aggregates various metrics and reports, allowing teams to simplify monitoring and provide real-time insights to stakeholders. Continuous Detection and Prioritization of Critical Data Exposure The solution should identify the most critical threats to the organization's security posture and implement immediate remediation controls. Data Lineage Mapping The tool should further offer data lineage capabilities, providing organizations with visibility into the transformation of their sensitive data over time. By tracking the data's lineage across its lifecycle, security teams can better understand the security gaps and thus establish optimized controls and policies. Automated Remediation DSPM tools should allow security teams to respond automatically to security incidents when they occur. This capability is essential in ensuring the integrity and confidentiality of the data. Scalability and Performance Lastly, organizations with hyper-scale environments should look for a DSPM tool that scales to accommodate the needs of their rapidly growing data.

DSPM Buyer’s Guide: Key Factors to Consider

AI is evolving rapidly, and its influence in enterprise environments is undeniable. We’ve seen the rise of AI agents capable of handling specific tasks, from automating data entry to providing customer support. But with this explosion of specialized agents comes a new challenge: how to manage their collective actions and ensure they work together seamlessly. Enter the orchestrator agent, an emerging concept poised to shape the future of enterprise AI. These agents don’t replace task-specific bots, they elevate them, coordinating workflows, integrating systems, and ensuring collaboration between humans and machines. Let’s explore why orchestrator agents are essential and how they might transform the way businesses leverage AI in the years to come. Why Orchestrator Agents Are Emerging Now The rapid adoption of AI agents has brought incredible opportunities but also introduced new complexities. Today, most AI systems operate in silos, focusing narrowly on their tasks without understanding the broader business context. This fragmentation creates inefficiencies and limits their potential. As companies deploy more AI-driven solutions, the need for a higher-level system to manage and harmonize these agents becomes apparent. Orchestrator agents are emerging as the answer to this challenge. They act as meta-managers, ensuring that individual agents collaborate effectively while adapting dynamically to the needs of the enterprise. https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true What Orchestrator Agents Could Bring to the Table Although orchestrator agents are still in their infancy, their potential impact is enormous. Here are some of the capabilities they might offer as they develop: 1. Intelligent Workflow Management In the future, orchestrator agents will enable businesses to design and execute complex workflows that involve multiple AI agents and human operators. They will understand the sequence of tasks needed to achieve a goal and ensure that each agent contributes effectively. For example, an orchestrator agent might: Assign a customer inquiry to a chatbot for initial triage.
Escalate the case to a human representative if the chatbot cannot resolve the issue.
Follow up with a satisfaction survey sent by another specialized agent. This seamless handoff between agents and humans will optimize operations while maintaining a high level of service. 2. Advanced Integration Across Systems As AI ecosystems grow, integration will become even more critical. Orchestrator agents will serve as the central hub, connecting disparate systems, agents, and data sources. They’ll ensure that no agent operates in isolation and that all tools contribute to a unified strategy. Future orchestrator agents could: Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise.
Enable real-time data sharing between agents, preventing duplication and enhancing decision-making.
Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. 3. Enhanced Reasoning and Decision-Making Unlike task-specific agents, orchestrator agents will rely heavily on reasoning capabilities. They’ll use advanced AI models to simulate scenarios, weigh options, and make informed decisions. This ability to "think" at a higher level will allow them to adapt to changing circumstances, ensuring workflows remain efficient even as variables shift. For instance, if an AI agent encounters an unexpected error, the orchestrator could assess the situation, identify an alternative path, and trigger the next best step. 4. Human-AI Collaboration Orchestrator agents won’t just automate workflows; they’ll also make them more transparent and collaborative. By providing intuitive interfaces, these agents will allow employees to: Visualize the progress of tasks.
Understand how decisions are being made.
Provide feedback to improve the system over time. In the future, orchestrator agents might also enable natural language interactions, letting employees adjust workflows or intervene directly through simple commands. The Road Ahead While orchestrator agents hold immense promise, they are still in the early stages of development. Many companies are experimenting with basic versions of these systems, but the full realization of their potential lies ahead. Here’s what the future could look like: Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems.
The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Conclusion The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Orchestrator agents represent more than just a solution, they symbolize the next step in how humans and AI systems collaborate. By ensuring that agents, systems, and people work together harmoniously, these meta-managers could redefine efficiency and innovation across industries. The road ahead is filled with questions: How will these agents evolve? What challenges will emerge as they scale? And, most importantly, how can enterprises prepare to harness their full potential? One thing is certain: orchestrator agents are a glimpse into the future of enterprise AI, and the journey has only just begun. AI is evolving rapidly, and its influence in enterprise environments is undeniable. We’ve seen the rise of AI agents capable of handling specific tasks, from automating data entry to providing customer support. But with this explosion of specialized agents comes a new challenge: how to manage their collective actions and ensure they work together seamlessly. Enter the orchestrator agent, an emerging concept poised to shape the future of enterprise AI. These agents don’t replace task-specific bots, they elevate them, coordinating workflows, integrating systems, and ensuring collaboration between humans and machines. Let’s explore why orchestrator agents are essential and how they might transform the way businesses leverage AI in the years to come. Why Orchestrator Agents Are Emerging Now Why Orchestrator Agents Are Emerging Now The rapid adoption of AI agents has brought incredible opportunities but also introduced new complexities. Today, most AI systems operate in silos, focusing narrowly on their tasks without understanding the broader business context. This fragmentation creates inefficiencies and limits their potential. As companies deploy more AI-driven solutions, the need for a higher-level system to manage and harmonize these agents becomes apparent. Orchestrator agents are emerging as the answer to this challenge. They act as meta-managers, ensuring that individual agents collaborate effectively while adapting dynamically to the needs of the enterprise. https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true What Orchestrator Agents Could Bring to the Table What Orchestrator Agents Could Bring to the Table Although orchestrator agents are still in their infancy, their potential impact is enormous. Here are some of the capabilities they might offer as they develop: 1. Intelligent Workflow Management In the future, orchestrator agents will enable businesses to design and execute complex workflows that involve multiple AI agents and human operators. They will understand the sequence of tasks needed to achieve a goal and ensure that each agent contributes effectively. For example, an orchestrator agent might: Assign a customer inquiry to a chatbot for initial triage. Escalate the case to a human representative if the chatbot cannot resolve the issue. Follow up with a satisfaction survey sent by another specialized agent. Assign a customer inquiry to a chatbot for initial triage. Escalate the case to a human representative if the chatbot cannot resolve the issue. Follow up with a satisfaction survey sent by another specialized agent. This seamless handoff between agents and humans will optimize operations while maintaining a high level of service. 2. Advanced Integration Across Systems 2. Advanced Integration Across Systems As AI ecosystems grow, integration will become even more critical. Orchestrator agents will serve as the central hub, connecting disparate systems, agents, and data sources. They’ll ensure that no agent operates in isolation and that all tools contribute to a unified strategy. Future orchestrator agents could: Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise. Enable real-time data sharing between agents, preventing duplication and enhancing decision-making. Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise. Enable real-time data sharing between agents, preventing duplication and enhancing decision-making. Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. 3. Enhanced Reasoning and Decision-Making 3. Enhanced Reasoning and Decision-Making Unlike task-specific agents, orchestrator agents will rely heavily on reasoning capabilities. They’ll use advanced AI models to simulate scenarios, weigh options, and make informed decisions. This ability to "think" at a higher level will allow them to adapt to changing circumstances, ensuring workflows remain efficient even as variables shift. For instance, if an AI agent encounters an unexpected error, the orchestrator could assess the situation, identify an alternative path, and trigger the next best step. 4. Human-AI Collaboration 4. Human-AI Collaboration Orchestrator agents won’t just automate workflows; they’ll also make them more transparent and collaborative. By providing intuitive interfaces, these agents will allow employees to: Visualize the progress of tasks. Understand how decisions are being made. Provide feedback to improve the system over time. Visualize the progress of tasks. Understand how decisions are being made. Provide feedback to improve the system over time. In the future, orchestrator agents might also enable natural language interactions, letting employees adjust workflows or intervene directly through simple commands. The Road Ahead The Road Ahead While orchestrator agents hold immense promise, they are still in the early stages of development. Many companies are experimenting with basic versions of these systems, but the full realization of their potential lies ahead. Here’s what the future could look like: Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Greater Autonomy: Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Deeper Learning: Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems.
The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems. The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems. Wider Adoption: The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Conclusion The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Orchestrator agents represent more than just a solution, they symbolize the next step in how humans and AI systems collaborate. By ensuring that agents, systems, and people work together harmoniously, these meta-managers could redefine efficiency and innovation across industries. The road ahead is filled with questions: How will these agents evolve? What challenges will emerge as they scale? And, most importantly, how can enterprises prepare to harness their full potential? One thing is certain: orchestrator agents are a glimpse into the future of enterprise AI, and the journey has only just begun.

Orchestrator Agents: The Next Evolution in Enterprise AI

What's Next for AI: Interpreting Anthropic CEO's Vision

Què hi ha a continuació per a la IA: interpretar la visió del CEO antròpic

Co bude dál pro AI: Interpretace vize generálního ředitele společnosti Anthropic

Τι ακολουθεί για την AI: Ερμηνεύοντας το όραμα του CEO της Anthropic

¿Qué viene después en el mundo de la IA?: interpretación de la visión del director ejecutivo de Anthropic

AIの今後: Anthropic CEOのビジョンを解釈する

Nini ekoya sima pona AI: Ko interpréter Vision ya CEO Anthropic

AI-ийн дараагийн зүйл: Anthropic CEO-ийн алсын харааг тайлбарлах нь

Seo se Latelago bakeng sa AI: Go Hlatholla Pono ya CEO ya Anthropic

Leswi landzelaka eka AI: Ku hlamusela Xivono xa CEO wa Anthropic

Що далі для ШІ: інтерпретація бачення генерального директора Anthropic

AI uchun navbatdagi narsa: Antropik bosh direktorning qarashlarini talqin qilish

人工智能的下一步：解读 Anthropic 首席执行官的愿景

How I Created a Social Recommendation Network in Brazil

SeaTunnel-Powered Data Integration: How 58 Group Handles Over 500 Billion+ Data Points Daily

From Resistance to Resilience: Change Management for First-timers in Digital Transformation

What if your digital product could not only look amazing but fuel your startup's success? At Meliorar, we believe that there’s more to design than meets the eye; there’s growth, a lot of growth! We use a combination of research-backed design methodology and user psychology to create intuitive digital experiences. If you want to keep users active for a longer period of time, increase the rate of collecting conversions, or simply design a product that will be adored, we work to fulfill the aims. The Meliorar Difference: Growth Hacking for UX At Meliorar, we don’t just design beautiful interfaces; we ensure growth in them. We analyze user behavior, conduct A/B testing, and track key metrics to ensure every design decision drives measurable results. Our growth hacking process involves User Research, Data Analysis,  UX Strategy, and UI Design. This iterative approach allows us to optimize for conversions and user engagement continuously. However, we differentiate ourselves with our global understanding and appreciation for cultural diversity. We practice cultural customization: creating designs, that appeal to a particular audience in Europe, the MENA area, and further. Meliorar: From Pixels to Performance and Everything In-Between Meliorar was born out of a desire to help startups harness the true power of design. Like many, we began with a simple idea and a lot of passion. We believe that design could be a powerful tool for growth, and we were determined to help other startups unlock that potential. At the very beginning, we remember concentrating on UI design only creating graphically appealing designs. We quickly understood though that looking good was not everything. There are also concepts like user behavior, data, and most importantly conversions. This is where the growth hacking ideology came into the picture and the transformation of Meliorar from just another UI/UX design to construction of beautiful yet effective designs happened. We've faced challenges along the way, learning valuable lessons about the importance of clear communication, agile methodologies, and building strong client partnerships. But through it all, we've remained committed to our core values: user-centered design, data-driven decision-making, and a relentless pursuit of growth. Milestones and achievements Since launching Meliorar in December 2020, we've been on a mission to help businesses unlock growth through user-centered design. And we've hit some pretty awesome milestones along the way: Global expansion: We've worked with incredible clients across Europe, the MENA region, North Africa, and even Canada. Fintech frenzy: We helped a fintech solution generate 1500 subscribers on their opening night! E-commerce magic: We teamed up with an e-commerce company to reduce their cart abandonment rate by 12% Conclusion We're grateful to Hacker Noon for this opportunity to share our story and connect with the startup community. Being nominated for Startup of the Year is a huge honor, and we're excited to be recognized alongside so many other incredible companies. But we need your help!  If you believe in the power of user-centered design and want to support a passionate team of growth hackers, cast your vote for Meliorar today! We believe that design has the power to transform businesses, and we're dedicated to empowering startups with the tools they need to achieve their goals. Every vote counts, and we appreciate your support. Vote for us ! Thank you for being part of our journey! What if your digital product could not only look amazing but fuel your startup's success? What if your digital product could not only look amazing but fuel your startup's success? At Meliorar, we believe that there’s more to design than meets the eye; there’s growth, a lot of growth! We use a combination of research-backed design methodology and user psychology to create intuitive digital experiences. If you want to keep users active for a longer period of time, increase the rate of collecting conversions, or simply design a product that will be adored, we work to fulfill the aims. The Meliorar Difference: Growth Hacking for UX At Meliorar, we don’t just design beautiful interfaces; we ensure growth in them. We analyze user behavior, conduct A/B testing, and track key metrics to ensure every design decision drives measurable results. Our growth hacking process involves User Research, Data Analysis,  UX Strategy, and UI Design. This iterative approach allows us to optimize for conversions and user engagement continuously. User Research, Data Analysis,  UX Strategy, and UI Design. However, we differentiate ourselves with our global understanding and appreciation for cultural diversity. We practice cultural customization: creating designs, that appeal to a particular audience in Europe, the MENA area, and further. Meliorar: From Pixels to Performance and Everything In-Between Meliorar was born out of a desire to help startups harness the true power of design. Like many, we began with a simple idea and a lot of passion. We believe that design could be a powerful tool for growth, and we were determined to help other startups unlock that potential. We believe that design could be a powerful tool for growth, and we were determined to help other startups unlock that potential. At the very beginning, we remember concentrating on UI design only creating graphically appealing designs. We quickly understood though that looking good was not everything. There are also concepts like user behavior, data, and most importantly conversions. This is where the growth hacking ideology came into the picture and the transformation of Meliorar from just another UI/UX design to construction of beautiful yet effective designs happened. We've faced challenges along the way, learning valuable lessons about the importance of clear communication, agile methodologies, and building strong client partnerships. But through it all, we've remained committed to our core values: user-centered design, data-driven decision-making, and a relentless pursuit of growth. Milestones and achievements Since launching Meliorar in December 2020, we've been on a mission to help businesses unlock growth through user-centered design. And we've hit some pretty awesome milestones along the way: Global expansion: We've worked with incredible clients across Europe, the MENA region, North Africa, and even Canada. Global expansion: Fintech frenzy: We helped a fintech solution generate 1500 subscribers on their opening night! Fintech frenzy: E-commerce magic: We teamed up with an e-commerce company to reduce their cart abandonment rate by 12% E-commerce magic: Conclusion We're grateful to Hacker Noon for this opportunity to share our story and connect with the startup community. Being nominated for Startup of the Year is a huge honor, and we're excited to be recognized alongside so many other incredible companies. But we need your help!  If you believe in the power of user-centered design and want to support a passionate team of growth hackers, cast your vote for Meliorar today! We believe that design has the power to transform businesses, and we're dedicated to empowering startups with the tools they need to achieve their goals. Every vote counts, and we appreciate your support. Vote for us ! Vote for us ! Vote for us Thank you for being part of our journey! Thank you for being part of our journey!

SOTY 2024 Nominee: Meliorar, More than just a Design Agency

Is Bias in AI Quantifiable?

From Centralized to Federated: Evolving Data Governance Operating Model

Stories, News & Mentions about CONTINYOU

Continyou’s Stories on HackerNoon

Continyou's News & Mentions