by Huenei IT Services | Jun 4, 2024 | Artificial Intelligence
Generative AI is no longer in the experiment stage. Chief Information Officers (CIOs) are now looking to ramp up these solutions and gain a real edge in the market. However many companies are hitting roadblocks that prevent them from maximizing the potential of
Generative AI.
While the challenges organizations face often fall into common categories, the solutions must be tailored to each company’s unique needs.
Choosing the Right Path
The first step is deciding how your company will integrate these new tools. There are three main options: pre-built tools, custom models with your own data, and building your own large language models (LLMs).
Here are some key factors to consider when making this choice:
- Resources and budget: Pre-built tools are the most cost-effective option but offer less control. Integrating models with your data requires investment in infrastructure and talent. Building LLMs from scratch is the most expensive option, requiring significant resources and cutting-edge expertise.
- Specific needs and use cases: If you only need Generative AI for basic tasks, pre-built tools might suffice. However, if you require highly specialized AI for your core products or services, building custom solutions will provide a greater long-term advantage.
- Data ownership and regulations: In some industries, regulations or data privacy concerns might necessitate integrating models with your data or building solutions in-house.
- Long-term AI strategy: If AI is simply another tool in your toolbox, pre-built solutions might work. But to gain a competitive advantage through AI, you’ll need to develop unique in-house capabilities.
For example, FinanceCorp initially used pre-built Generative AI tools for tasks like writing and summarizing reports. However, these tools proved inadequate for complex financial tasks like risk analysis and contract reviews. To achieve the performance they needed, they had to switch to a custom model solution with their own data.
Taming the Generative AI Beast
One key lesson learned from pilot projects is the importance of avoiding a sprawl of platforms and tools. A recent McKinsey survey found that “too many platforms” was a major obstacle for companies trying to implement Generative AI at scale. The more complex the infrastructure, the higher the cost and difficulty of managing large-scale deployments. To achieve scale, companies need a manageable set of tools and infrastructure.
One solution is to establish a centralized, single-source enterprise Generative AI platform. While this requires initial standardization efforts, it can significantly reduce operational complexity, ongoing maintenance costs, and associated risks in the long run. It also facilitates consistent and scalable deployment of Generative AI across the organization.
A hybrid approach that combines internal and external expertise might be the most effective strategy. Partnering with a leading technology provider can provide a solid foundation for a robust Generative AI platform. However, you’ll also need to build an internal team with expertise in data science, AI engineering, and other relevant fields. This team can then customize, expand, and manage the platform to meet your specific business needs.
For instance, HSBC, after piloting solutions with seven different Generative AI vendors, faced challenges with high maintenance costs, governance issues, and integration complexities. They decided to consolidate everything on Microsoft’s platform and standardize APIs, data flows, monitoring, and other aspects. This approach helped them reduce their AI operating costs by over 60%.
Conquering the Learning Curve
Finally, there’s the ever-present learning curve. CIOs understand the technical skills needed for Generative AI, such as model fine-tuning, vector database management, and application and context engineering. However, acquiring this knowledge can be a daunting process. Building all the specialized skills in-house can be extremely slow and challenging. Even with an accelerated learning curve, it could take months for an internal team to reach the required level of expertise.
Retail giant GiganteCorp allocated a significant budget of $15 million to assemble an elite team of 50 data scientists and engineers with experience in fine-tuning cutting-edge language models, application engineering, and vector knowledge bases. However, due to the high demand for these specialists in the market, they were only able to fill 40% of the positions after a year.
The lack of prior experience and the need to master new technologies can make implementing Generative AI seem like a formidable task. However, by partnering with an experienced technology partner, companies can overcome these challenges and unlock the full potential of Generative AI to transform their operations.
After several failed attempts to develop their own Generative AI models, the legal firm BigLaw partnered with experts from Anthropic. Their guidance in best practices, benchmarking, iterative refinement, and thorough testing enabled their contract review system to achieve over 95% accuracy in less than six months, a 30% improvement over previous attempts.
A specialized Generative AI partner can and should continue to provide ongoing consulting and support services, even after initial capabilities have been implemented within the organization. Inevitably, challenges, bottlenecks, or highly specific requirements will arise as Generative AI usage is deployed and scaled. Accessing the deep expertise of these consultants can be key to resolving them effectively.
The Generative AI models deployed by the fintech company Novo initially yielded excellent results in tasks such as fraud detection and customer support. However, after eight months, performance degradations began to be observed as data patterns shifted. They had to implement continuous data retraining and recycling pipelines to maintain accuracy levels.
In conclusion, Generative AI systems are not one-time projects; they require continuous refinement and updating. Adopting a mindset of constant testing, learning, and improvement based on feedback and empirical data is crucial for maximizing the long-term value of Generative AI.
Get in Touch!
Francisco Ferrando
Business Development Representative
fferrando@huenei.com
by Huenei IT Services | Jun 4, 2024 | Infra, Software development
Imagine the frustration of a holiday shopping surge crashing your e-commerce platform. Legacy monolithic applications, while familiar, often struggle with such unpredictable spikes. Enter microservices architecture, a paradigm shift promising agility, scalability, and maintainability for modern software. But is it the right choice for you? Let’s explore the power and considerations of microservices with IT veteran Richard Diaz Pompa, Tech Manager at Huenei.
The Power of Microservices
Microservices architecture fundamentally reimagines application development. Instead of a monolithic codebase, microservices decompose the application into a collection of independent, self-contained services. Each service owns a specific business capability and interacts with others through well-defined APIs. This modular approach unlocks several key advantages.
“Imagine a monolithic application as a monolithic server. If a single functionality spikes in usage, the entire server needs to be scaled up, impacting everything else,” explains Richard; “with microservices, your application is like a collection of virtual machines. If a particular service sees a surge in activity, only that specific service needs to be scaled up.” This targeted approach optimizes resource allocation and ensures smooth performance for the entire application, even under fluctuating loads.
Another key advantage lies in improved maintainability. Traditionally, monolithic applications can be likened to complex engines. Fixing a single component often requires a deep understanding of the entire intricate system. Microservices, on the other hand, are like smaller, self-contained engines. Developers can focus on improving a specific service without needing to delve into the complexities of the entire application. This modularity not only simplifies development but also streamlines troubleshooting and debugging issues.
Conquering the Challenges: Strategies for Smooth Implementation
“While the benefits of microservices are undeniable, their implementation introduces complexities that require careful consideration,” Richard remarks, “increased service communication overhead, managing a distributed system, and ensuring data consistency across services are common hurdles that organizations must overcome.”
Organizations can leverage API gateways, service discovery mechanisms, and event-driven architectures to mitigate communication challenges. API gateways act as single-entry points for all microservices, simplifying external client access and handling tasks like authentication and authorization. Service discovery tools like Zookeeper or Consul allow services to dynamically register and find each other, reducing manual configuration headaches. Event-driven architectures, where services communicate by publishing and subscribing to events, promote loose coupling and simplify communication patterns.
Leveraging containerization technologies like Docker packages and deploys microservices in standardized, lightweight environments. This simplifies deployment and management compared to traditional methods. Microservices orchestration tools like Kubernetes can further automate deployment, scaling, and lifecycle management of microservices, reducing the operational burden on IT teams.
Furthermore, ensuring consistent data formats and interactions across services is crucial. Well-defined API contracts promote loose coupling and simplify data exchange between services. The CQRS (Command Query Responsibility Segregation) pattern separates read and write operations across different services, improving data consistency and scalability for specific use cases. In some scenarios, eventual consistency, where data eventually becomes consistent across services, might be an acceptable trade-off for improved performance and scalability.
“Successful microservices adoption requires a holistic approach that considers not only technical implementation but also strategic alignment with business objectives, risk management, and long-term digital transformation roadmaps,” cautions Richard. “Partnering with experienced microservices professionals or consulting firms can provide valuable guidance and expertise in industry best practices, emerging technologies, and proven methodologies.”
The Final Verdict: A Well-Considered Choice
“IT leaders must carefully evaluate their organization’s needs, resources, and readiness for adopting a microservices architecture.” Richard highlights “while the benefits are substantial, the increased complexity and operational overhead might not be suitable for every project. A thorough assessment of the potential advantages and challenges, coupled with a well-defined implementation strategy, is essential for successful adoption.”
As enterprises navigate the complexities of the digital landscape, microservices architecture presents a compelling path forward. “By carefully considering their unique requirements and seeking guidance from experienced professionals, CIOs can make informed decisions about whether and how to leverage this architectural approach. This ensures their software systems remain not only scalable and maintainable but also agile enough to thrive in the ever-evolving digital world,” he concludes.
Get in Touch!
Francisco Ferrando
Business Development Representative
fferrando@huenei.com
by Huenei IT Services | Jun 3, 2024 | Artificial Intelligence, UX & UI Design
Progressive Web Apps (PWAs) are revolutionizing the way businesses deliver web experiences. By merging the best aspects of traditional websites and native mobile apps, PWAs offer a seamless, app-like user experience accessible through any web browser.
The best of two worlds
PWAs work for every user, regardless of the browser they’re using. They provide an enhanced experience for modern browsers that support the latest web standards, while still functioning as a traditional website on older browsers. Can work offline or with poor network connectivity by leveraging service workers, a script that acts as a client-side proxy, and caching app resources and data for offline use.
Also, they are searchable and discoverable through search engines, just like regular websites, providing a wider reach compared to native apps in app stores.
Progressive Web Apps eliminate the need to develop and maintain separate native apps for different platforms (iOS, Android, etc.). A single codebase can target multiple platforms, reducing development and maintenance costs. Unlike native apps, PWAs do not require installation from app stores, making them accessible to anyone with a web browser. They can be updated seamlessly without user intervention, ensuring users always have access to the latest version. This eliminates the need for manual app updates, reducing overhead and ensuring a consistent experience across users.
PWAs can leverage existing web infrastructure and APIs, making it easier to integrate with existing systems and processes within the organization. This can reduce the need for extensive refactoring or migration efforts. Additionally, they can be built using a modular architecture, allowing different components or features to be developed and deployed independently. This can aid in scalability and enable large businesses to incrementally roll out new features or updates.
Overall, PWAs offer businesses a cost-effective, scalable, and user-friendly solution for delivering engaging web experiences across multiple platforms, while leveraging existing web infrastructure and technologies. This can lead to improved user engagement, reduced development and maintenance costs, and better compliance with security and privacy standards.
The AI obsession
Progressive Web Apps can integrate AI technology to provide enhanced functionality and user experiences.
These new applications can leverage NLP to enable voice commands, chatbots, or virtual assistants. This allows users to interact with the app using natural language, enhancing accessibility, and providing a more intuitive user experience.
Also, machine learning algorithms can be integrated into PWAs for various purposes, such as:
- Personalization: Analyzing user behavior and preferences to provide personalized recommendations, content, or experiences.
- Predictive analytics: Predicting user actions, needs, or preferences based on historical data and patterns.
- Image/object recognition: Identifying objects, faces, or features in images or videos within the PWA.
AI can be used to assist users in filling out forms by automatically populating fields based on user inputs or previous data, reducing friction and improving the user experience. Can also analyze user behavior, preferences, and context to deliver highly relevant and personalized notifications at the right time, improving engagement with the PWA.
With the help of technologies like TensorFlow.js, AI models can be integrated into PWAs and run directly in the user’s browser, enabling intelligent features even when the device is offline.
What about data privacy?
Developing an AI-powered Progressive Web App (PWA) that meets stringent privacy standards and complies with certifications like ISO 27001 demands a comprehensive approach. Companies must embrace a “Privacy by Design” mindset from the outset, weaving data protection principles into every phase of development.
Data minimization is key, collecting only essential user information for the AI functionality while providing transparent communication about data usage. Robust data handling measures, including encryption, secure protocols, and fortified storage, safeguard user privacy.
Empowering users with clear consent mechanisms and control over their data fosters trust. Rigorous auditing, logging, and periodic risk assessments maintain accountability and enable swift identification of potential issues.
Adhering to privacy regulations like GDPR and implementing secure AI model training processes further reinforce compliance. Ethical AI principles, such as transparency, fairness, and explainability, underpin the system’s responsible operation.
By integrating privacy and security measures holistically throughout the lifecycle, companies can deliver innovative AI-powered PWAs that prioritize user trust and data protection, setting new standards for responsible technology.
Too good to be true?
Considering the breadth of skills required, it may be challenging for a single team or organization to possess all the necessary expertise. In such cases, finding an experienced partner or consulting firm that specializes in AI-powered development can be a viable option.
An experienced partner can provide:
- Proven expertise and a skilled team with the required technical capabilities
- Established best practices, methodologies, and tools for PWA and AI development
- Experience in navigating regulatory and compliance requirements
- Access to specialized resources and infrastructure
- Ability to scale resources as needed and provide ongoing support and maintenance
PWAs equipped with AI capabilities represent a powerful tool for businesses seeking to deliver a superior user experience, reduce costs, and gain a competitive edge. By partnering with an experienced software development firm, you can leverage this technology while ensuring the highest security and privacy standards are met.
Get in Touch!
Isabel Rivas
Business Development Representative
irivas@huenei.com
by Huenei IT Services | May 6, 2024 | Cybersecurity, Process & Management
Why Partnering with an ISO 27001 Certified Developer Matters
In the rapidly evolving digital landscape, ensuring the security and reliability of software applications is paramount. As cyber threats continue to escalate, organizations must prioritize implementing robust security measures throughout the software development life cycle (SDLC).
Choosing the right development partner is crucial. This is where certifications like ISO 27001 come in.
The Power of ISO 27001 for Secure Development
This cerfitication provides a comprehensive set of controls and best practices for establishing, implementing, maintaining, and continually improving an Information Security Management System (ISMS). By adhering to this standard, software development organizations can embed security measures into every phase of the SDLC, from requirements gathering and design to coding, testing, and deployment.
“ISO 27001 emphasizes identifying information security risks throughout the entire organization,” explains Jorge Attaguile, COO of Huenei IT Services. “This applies to the Secure Development Life Cycle (SDLC) as well. By implementing a risk-based approach, we can identify and mitigate potential vulnerabilities in the software itself, development processes, and data handling.”
Proper documentation is a cornerstone, and the SDLC is no exception. Documented procedures for secure development cover aspects like security requirements, coding standards, and testing procedures. Additionally, continuous improvement is a core principle. This translates to regularly reviewing and updating SDLC security practices, potentially involving security tools, penetration testing, and adapting to new threats, as Attaguile highlights: “ISO 27001 offers a comprehensive set of controls that can be adapted to specific SDLC and security needs.”
“ISO 27001 requires organizations to establish and apply security rules throughout the SDLC,” explains Dr. Rebecca Herold, a renowned information security expert and CEO of NIST Risk Management. “This can involve secure coding practices, access controls, vulnerability management, and robust testing procedures.”
Complementing ISO 27001 with ITIL processes can further enhance the efficiency and effectiveness of secure software development. ITIL provides a framework for standardizing IT service management practices, focusing on delivering high-quality IT services while aligning with business needs.
“ITIL processes already touch on various security aspects, such as change management, incident management, and problem management,” notes Stuart Rance, an ITIL expert and author of several ITIL publications. “By integrating ISO 27001 controls into these processes, organizations can ensure that security is embedded within their IT service management practices.”
By integrating security controls into existing ITIL workflows, a software development company can achieve a more efficient and secure development process.
In essence, Attaguile continues, “ITIL provides a foundation for efficient service delivery, while ISO 27001 ensures security is built into those processes. Integrating them allows a software development company to achieve both efficient and secure development.”
The Client Advantage
The benefits of partnering with a developer certified are numerous. By implementing ISO 27001 controls within ITIL processes, the development process becomes more secure, resulting in a final product with fewer vulnerabilities and a lower risk of security breaches. Your data and the software itself are better protected.
Furthermore, ITIL’s focus on high-quality IT services, when combined with ISO 27001, ensures security best practices are ingrained within the development lifecycle. This leads to a more robust and reliable final product with fewer bugs and security issues. Additionally, streamlining processes through ITIL and integrating security controls can lead to faster development cycles and potentially lower costs, translating to faster delivery times or more competitive pricing.
As Attaguile concludes, “Knowing a development company adheres to both ISO 27001 and ITIL standards demonstrates a commitment to quality and security. This fosters trust and confidence in our ability to deliver a secure and reliable product that meets your business needs.”
Partnering for Success
By choosing a development partner certified in ISO 27001 and ITIL standards, you gain a significant advantage. You receive a more secure, reliable product delivered efficiently by a trustworthy provider. This translates to peace of mind and a successful software development project.
Get in Touch!
Isabel Rivas
Business Development Representative
irivas@huenei.com
by Huenei IT Services | May 6, 2024 | Software development
Building secure and reliable software is paramount for businesses today, especially when adhering to strict security frameworks like ISO 27001. Traditional approaches to development can be time-consuming, prone to errors, and make it challenging to maintain compliance. Here’s where Generative AI (Gen AI) steps in as a revolutionary force.
Revolutionizing Development with AI-Powered Insights
Traditional development processes often involve manual code reviews and testing, leading to bottlenecks and potential vulnerabilities that can derail ISO 27001 compliance efforts. Gen AI, on the other hand, learns from vast code repositories, identifying patterns, best practices, and potential security weaknesses.
“Generative AI can analyze mountains of code in real-time, highlighting areas that might conflict with ISO 27001 controls and suggesting improvements to align with the standard,” explains Gustavo Commisso, CEO of Huenei IT Services. “This empowers developers to write cleaner, more secure code from the start, reducing the risk of vulnerabilities and simplifying compliance audits.”
AI Streamlines Development Workflows and Maintains Compliance
Beyond security, Gen AI can automate repetitive tasks such as unit testing and code generation, tasks crucial for ISO 27001 compliance. This frees up developers’ time to focus on higher-level problem-solving and innovation, while ensuring adherence to security best practices.
“AI can automatically generate boilerplate code, unit tests, and documentation that meet ISO 27001 requirements,” says Commisso. “This significantly reduces development time and effort while demonstrably strengthening your software’s security posture for audits.”
Continuous Learning: The AI Advantage
A key strength of Gen AI is its ability to continuously learn and adapt. As new code and security threats are encountered, AI models refine themselves, becoming more efficient and effective.
Commisso highlights, “Gen AI aligns perfectly with Agile development methodologies. By continuously learning from new data, AI systems can adapt to changing requirements and security landscapes, ensuring your software remains robust, secure, and compliant with ISO 27001 standards.”
Challenges and Considerations
While the benefits of Gen AI are undeniable, careful planning is crucial. Data quality, model interpretability, and developer training are essential for responsible and effective AI implementation.
“We must remember that AI is a powerful tool, not a silver bullet,” cautions Commisso. “Successful adoption requires a well-defined strategy, collaboration between developers and AI specialists, and ongoing monitoring of the AI models to ensure continued effectiveness and alignment with ISO 27001 controls.”
The future of software development is brimming with possibilities thanks to Gen AI. By integrating it with established development practices and ISO 27001 compliance frameworks, companies can achieve significant gains in efficiency, security, and innovation. This powerful combination empowers developers to build better, more secure software faster, all while demonstrably maintaining compliance with rigorous security standards.
Get in Touch!
Isabel Rivas
Business Development Representative
irivas@huenei.com
