Debugging Quarantine Fatigue and Anxiety From the Perspective of a Tech Guy

These have been strange and tough times. Each and every one of us has been affected in one way or another by the current, global pandemic and it’s during these times that we are forced to be our strongest, most resilient selves. That’s not always an easy task to accomplish, but to quote one of my favorite Beatles’ songs, “I’m going to try with a little help from my friends.” Because, when we help each other, we are capable of great things.

So, for my Wovenware family, friends, peers, and those who’ve listened and shared their experiences in these trying times, I wanted to share how I’ve debugged my quarantine fatigue and anxiety over the past seven months to help put my best foot forward each day.


Earlier this year, I tried to stop drinking coffee, not for any specific reason, probably just as a challenge to myself. I switched from coffee to all different kinds of flavors of tea. But, ultimately, it didn’t work out for me, so I won’t go much into that. I will just say that peppermint tea is amazing.

By the start of the pandemic, I was back to drinking two, quite generous, cups of coffee a day. Those magnificently balanced cups, of equal parts almond milk mixed with Puerto Rico’s most delicious coffee and a sprinkle of brown sugar, not only warmed my bones, but also gave me a burst of focused energy that I could channel into coding, debugging, designing or maybe (just maybe) even doing some compiling. It quickly became part of my daily ritual again and little did I know what was in-store for me.

The thing is, drinking that amount of coffee wasn’t an issue while working from the Wovenware campus. Since the place is so big, we get plenty of walking done daily, but now that everyone’s staying safe in lockdown, my coffee ritual started working against me. Fast-forward to a couple of months, where being cooped up in the apartment became the new normal and two cups of coffee meant having all this extra energy that I wasn’t expending. It was making me feel uneasy.

So, the first thing I thought to do was to cut back on the coffee. I didn’t eliminate it all together, but it still was quite a challenge. The concept of drinking coffee is a thing of ancestral pride here on the Island, a staple, if you will. Generally, Puerto Ricans tend to own a large moka pot, or what we colloquially call a “greca,” to prepare coffee for the family, guests or, in my case, for having a nice tall mug of “café con leche” just for myself. I always ended up having too much. Then, as if some sort of alignment of the stars occurred in my favor, I was given the perfect solution to my situation. I received a smaller-sized “greca,” compliments of Wovenware and Gustos Coffee Co. It was part of a “Lockdown Care Package” sent to us employees that included the insulated mug, Gustos Coffee grounds, and a tote bag. Now, I can limit myself to a small cup o’ brew a day without sacrificing the delicious taste and boost of energy in the morning.

Wovenware Care Package to Help Ease Quarantine Fatigue

After my cutting-back-on-coffee experiment I felt some improvement, but still felt tense most days. I was having upper back and wrist aches that eventually brought me back to feeling stressed, anxious, and uneasy. It took some time and conversations with friends and coworkers for me to realize that all this was just the quarantine fatigue and anxiety getting to me. It also is a side effect of sitting down for prolonged periods of time, adopting weird postures in my desk chair throughout the day, and not being able to move about freely. The more sedentary lifestyle also meant that I read too many news-headlines about the pandemic and politics, and worried about friends and family. I was doing less exercise and not sleeping as well as I used to. I knew then that there were a couple more things that I needed to try to get back into the swing of things.


I realized during this time that working from home is great and super convenient, but it also puts you in a weird position – Are you at home or work? You have distractions left and right, ranging from family matters, the dog, the neighbor mowing his lawn, and, not to mention, your house chores and hobbies calling out to you. You can use a pair of headphones and listen to music to block out the noise, but then you have to fight the urge to sing or dance along to your music, making you lose track of your workflow. I quickly found an effective way to remedy this, in the form of a 24/7 YouTube channel broadcast: Beats to Relax/Study to. Listening to the playlist can help me stay focused and relaxed, but when I get tired of that, I’ve found that the most soothing thing I can listen to, that also helps me absorb information, is a playlist titled, 24/7 Relaxation/Meditation Music.

I like to joke around that meditation music is cool because “it reminds me to breathe,” like anybody needs to be reminded to breathe, right? What I mean to say is that it reminds me to be conscious of my breath. Have you ever tried to pay attention to the rhythm of your breath for more than a minute or two? it’s a bit harder than it seems. You’d be surprised how much we take breathing for granted, such a natural thing, a sort of automatic mechanism that basically runs our entire body. I started using a meditation app called Headspace that offers a free trial. With this, I learned the basics of breathing exercises.

One of the most surprising things I noticed about breathing exercises and meditation is how hard it is to concentrate on your breathing rhythm. Try it; I dare you. Just try to count 1 on the inhale and 2 on the exhale, and so on up to 10 and then restart. See how far you can get until you realize you’ve actually been thinking about that email you have to write and have completely forgotten to pay attention to your breathing. Well, the point is that whenever I get overwhelmed by messages, deadlines or requests, I can always use my breath as my token, like in the movie Inception, to ground me back and help me regain my focus. Practicing meditation has really helped me improve quarantine fatigue and anxiety.


Once the pandemic hit, it surprised me how quickly I could get used to not going out. I’ve never really been big on going to the gym and preferred playing basketball or running as ways of keeping active. And, a couple of years back, I got into swimming and fell in love with it and it’s been my go-to sport ever since, but now that pools are closed and going out is risky, I got myself a kettlebell weight and I’ve been training with it whenever I need to let off some steam. It’s quite convenient that I can get a full body and cardio workout right in my living room with two videos I found from Keith Weber: Beginner to Advanced and No time? No Problem. Note that kettlebell workouts can be dangerous and it’s very important to start-off slow.


Yoga is my favorite habit of all, simply because it’s a mix of both meditation and exercise. Learning to implement it into my routine has benefited me in so many ways. It takes a little effort to put in the time and practice, but it’s well worth the investment to fight quarantine fatigue and anxiety.

I’ve become a loyal follower of the YouTube channel Yoga With Adrienne. I can’t remember how I found Adrienne, but I know I’m grateful for her cheerfulness, positive vibes and her willingness to share her passion. She provides a good alternative to those Tony Horton 10 Minute Trainer videos I used to do back in college. She also offers an enormous list of free yoga videos specifically made for different ailments, symptoms, practices and even professions. I’ll just share a couple of routines that have helped me throughout the lockdown.

Neck and Shoulder Relief
Many times I find myself with my shoulders shrugged up almost next to my ears when I’m too focused on the task at hand. This takes a toll on me later in the day, so this little session helps alleviate that upper back tension and neck pain from the inevitable bad posture I always fall into after a couple of hours of coding.

Upper Back Love
I tend to gather a lot of stress in my upper back and this helps. I know this thanks to Wovenware’s Wellness program and the wonderful Pipe, our wellness therapist, who brought attention to this tendency of mine.

Deep Stretch
Whenever I’m feeling sore, stiff or restless, this is the routine I turn to. It’s a little on the longer side, clocking in at around 45 minutes, and includes some intermediate yoga moves, but I always feel great after taking the time to stretch out the entire body.

For Gut Health and Hips and Lower Back 

I can’t confirm that this was the cause, but at the beginning of the quarantine I was using a certain cushion on my desk chair that I think may have been messing up my lower back and causing some stomach pain. It led me to try this video that involves using the abdomen, core muscles, and focuses on breathing. It’s so easy to get caught up in the maelstrom of news and social media about the pandemic, and at the same time, there are so many other issues we are facing on a daily basis. We are bombarded with so much information that it’s easy to get overly stressed out. This video also addresses all of that.


Having people to hear me out, share experiences with and attack problems together, has helped me the most during these trying times. Hanging out virtually with my friends, sharing funny memes, short greetings, or having healthy competitions on who can plate the best Ribeye steak (now that we’re all YouTube-trained home-chefs). We even put together a little music project at the beginning of the pandemic by combining the efforts and excitement of a lot of busy people. Word on the street is, there may be another one of those little projects currently in-the-works, but no one really knows for sure.

I’ve been extremely lucky to have found some good activities to channel all of the emotions caused by everything going on today. Another good strategy has been to limit the amount and quality of information that I was absorbing. I uninstalled news apps and just stayed tuned to local weather, news and lock down announcements. This helped me focus on the things that directly impact me, at least for the time being. All of these activities have helped me lighten the mental load and ease quarantine fatigue and anxiety. Hopefully, some of what I’ve shared today can help you do the same.

AI Can’t Go Very Far without Good Data

The benefits of AI are all around us, but the real driver to its success is the data that fuels it. This is a topic I recently explored in the Forbes Technology Council blog. As I mentioned in it, the tendency to shortchange the data is a fairly universal problem that often results in misleading or incorrect results and poor business outcomes.

So, how can you make sure your data is going to properly fuel your AI projects? Consider the following five best practices that I shared:

  1. Go big or go home. When you are trying to solve a problem, you may not always know where you will find the answer, so try to gather as much data as possible. There’s really no such thing as too much data.
  2. Make sure the data is shipshape. After the right data is collected, it needs to be cleansed, validated and prepared to ensure that it is in good shape and ready for analysis. While this can be a time-consuming process, it can mean the difference between good and bad results.
  3. Put the data through a workout. A good way to know if the data is accurate is to test it, and find out if there is a problem before you are too far along in the process. You should divide your data into two parts and set one aside for testing and the other for feeding the algorithm.
  4. Use a data auditor. It pays to hire data auditors who can help you assess the data you have, conduct the tests and help you plan for future data training needs.
  5. Avoid biased data. It’s important that AI solutions that help organizations make important decisions operate fairly and equitably. To enable diverse AI-based decisions, the data shouldn’t focus on only one type of data source, but should encompass all scenarios. We’re all too aware of the problems encountered when AI makes decisions based on racial or gender profiling.

In the quest to be seen as a technology trailblazer, many organizations rush to deploy AI solutions to try to quickly realize their benefits. Yet, what they often don’t realize is that the best AI solution is useless without good data. By focusing on better data collection, cleansing, auditing and diversity, true AI success can be much achieved.

Chatbot Development Methodology: A Melting Pot of Diverse Teams and Frameworks

Artificial Intelligence is the driving force behind the creation of innovative products like autonomous vehicles and chatbots. Recent advancements in Natural Language Processing (NLP) have made chatbots, also referred to as virtual assistants, a great option for improving the customer experience. Answering frequently asked questions, filing claims, checking the status of an order and getting feedback from customers are among the most popular use cases for chatbots. Building a chatbot that offers a good experience to customers requires collaboration from an interdisciplinary team of business analysts, service designers, data scientists, machine learning engineers and software developers. The chatbot development methodology blends several modern frameworks and methodologies including design thinking, AI innovation sprints, and agile software development.

Defining the Chatbot’s Purpose and Managing Expectations

Like most artificial intelligence applications, the first step in developing a chatbot is clearly defining its purpose, the problem it is going to solve, and the value it is going to bring to the users. Chatbots should be designed to help users navigate through a very specific business scenario. One must avoid the trap of trying to create a jack of all trades (and master of none) chatbot. For example, some conversational applications are getting a lot of hype that can set unrealistic expectations and contribute to negative user experiences. Defining a narrow scope and a clear chatbot purpose will be key for managing the rest of the chatbot development process.

Understanding the Audience

As expected in any human-centric design approach, it’s important to understand the target audience.  Service designers will conduct interviews to gain an understanding of customers as human beings. Using design thinking techniques, we learn about their pain points, their likes and dislikes, how they communicate and what they value the most. Understanding the human needs of a customer is essential to designing a chatbot experience that delights them.

A Chatbot Design Experience is a great tool to have to guide the team in defining all the key components of a virtual assistant.

Defining the Chatbot’s Personality

Techniques to define user personas should be adapted to create a “chatbot persona”, including its age group, interests, how it acts, how it communicates, its sense of humor and its limitations. Is the chatbot an advisor, an assistant, or a friend? A poorly designed chatbot personality is one of the top reasons why many chatbots fail. A chatbot with personality and empathy toward the customers can drive user engagement and create meaningful experiences. Robotic chatbots, however, have the completely opposite effect.

Designing the User Journey and Conversation

With user persona and chatbot personalities created, a service designer next should define in greater detail the human + machine interaction. He/she must design the user journey and script the actual conversations. What questions might a user answer? If the chatbot does not know an answer, do they redirect the query to a support representative? What information does the chatbot need to know in order to answer a question? The conversational and human-in-the-loop design are the most challenging parts of designing an optimal customer experience.

Developing the Chatbot

Once the chatbot design is complete, the development team can map out the features and create a technical design. Identifying if the chatbot requires artificial intelligence (AI) to fulfill its purpose will be important in defining the chatbot development methodology. Not all chatbots need AI to be fully functional, and if this is the case, the team will likely implement agile development methodologies in the traditional software development lifecycle (SDLC). Beyond building a rule-based chatbot, the software engineering team should define what background tasks and fallback mechanisms need to be carried out in order to properly integrate all the system’s components. Development can be completed through incremental deliveries, adapting design with feedback obtained from the end-users.

If a chatbot requires Natural Language Processing (NLP) components, agile software development and machine learning will need to be blended in a chatbot development methodology. The machine learning process is inherently different from rule-based systems. It requires exploration and experimentation when performing data collection, data preparation (cleaning, wrangling, and merging), feature engineering, model training, and model evaluation. The project team must account for the experimentation that will take part in the chatbot development process. Wovenware has developed a proprietary methodology, the Innovation Sprint, to integrate innovation and discipline in a goal-oriented experimentation process.

Integrating Natural Language Processing

The NLP components in chatbots are mainly used to recognize user intent and extract entities. Each intent can represent a task to be performed. A conversation flow will vary depending on the detected intent. How the chatbot handles intents will define its relationship with the user. Optional components, like sentiment analyzers and language translators, can add value and enhance chatbot experience. Sentiment analysis can help chatbots respond to users in a personalized manner by understanding what makes them happy, while language translation components can be used to give chatbots multilingual capabilities. Data scientists will optimize NLP components in order to provide information about intent, meaning and the context in which the chatbot needs to appropriately respond to a user’s query. Natural Language Processing is constantly evolving so these components must be updated regularly to keep the virtual assistant functioning in optimal conditions.


Once each system component has been designed and implemented, testing and evaluation should be carried out. For middleware software, unit testing should be performed to test individual components. Machine learning components need to be evaluated differently. Testing data is used to evaluate the trained models and success metrics must be clearly defined in order to measure model performance. Usability testing must be done before and after deployment. These tests will help to understand if the chatbot follows the expected conversational flows and error handling strategies by monitoring user interactions.

From Chatbot Rookie to MVP

Successful chatbots are designed to learn, making maintenance an integral part of the chatbot development methodology. Once the chatbot is interacting with real users it is important to analyze user feedback and sentiment, along with other insights the interactions may produce. Insight analysis may give us an understanding of possible usability issues or areas of improvement, but it also may provide us with possible market opportunities to implement more data-driven solutions. Analytics can help us understand bot performance, user engagement, sentiment and demographics. Fixing bugs and other software defects should be part of the software maintenance strategy, but with evolving customer expectations and technology advancements it is important to also keep the system updated and sustainable. Metrics for the machine learning components should always be monitored in order to address a possible decrease in performance. In the case of chatbots, the ever-evolving nature of natural language may cause the NLP components to worsen over time. Constant retraining, through active learning and evaluation of those components is vital during the maintenance and development process.

Chatbots move from rookie to MVP level through the collaboration of business leaders, service designers, data scientists, machine learning engineers and software developers. Each member of the team needs to stay at the top of his/her game to implement modern frameworks and novel technologies to continue to improve the customer experience. The team will always be on a journey to understand the user, to evolve the application around the things that matter to them the most and make the technology as human as possible, but this is what makes for great chatbots.

An Overview of Large-Scale Monitoring of Infrastructure Using Deep Learning for Satellite Imagery

In the past half-decade in Puerto Rico, we have had our fair share of catastrophic events such as hurricanes and earthquakes. These events have left significant parts of the population without power for extended periods of time, often ranging from days to weeks, or even months.

As society becomes ever more connected and reliant on power and utilities, the more increasingly disruptive these catastrophic phenomena become to essential services for millions of people. This is one of the driving reasons behind the growing adoption of self-sustainable technologies such as solar power generation.

As we continue to develop as a country, we’re learning from these experiences and can appreciate the need to consider every option that may allow us to receive vital services in the event of another catastrophe. One of those options is the use of solar farms.

By implementing deep learning for satellite imagery, programmed in a location-independent, distributed platform not affected by regional events, we can design a solution that quickly analyzes infrastructure over large regions.

This approach enables us to put in place a disaster-resilient pipeline that could provide us with insights into the status of critical infrastructure, even after significant catastrophic events. We believe that such a system can potentially help mitigate the impacts of prolonged service interruption by providing resilient solutions that reside outside the scope of these events.

As an example, in this blog, we’ll go over the high-level process through which you could create a potential solution to solar power installation assessment, using deep learning models for high-resolution satellite imagery. This system could be ideally utilized during and after potentially catastrophic or disruptive events, to assess if the infrastructure suffered any large-scale damage, such as a loss of some of its solar panels.

Why the Use of Very High-Resolution Satellite Imagery?

With the continuous advancements to satellite imagery technologies, the amount and complexity of plausible analysis that can be performed also continues to increase. At the time of this writing, Maxar’s Worldview3 and Worldview4 satellites provide 30 cm per pixel images, the highest resolution for satellite imagery commercially available. There are many considerations when it comes to satellite image data acquisition, resolution is of extreme importance in the context of small objects such as solar panels. As you can imagine, there are clear benefits to working with 30 cm including the ability to create more accurate models and an easier data annotation process, given that the objects are more clearly delineated. Therefore, it is a good idea to utilize the highest resolution available.

50 cm/px vs 30 cm/px Satellite Imagery Resolutions

Note: Both images were originally 512 x 512, but minor cropping has been performed to illustrate the effect of resolution

Deep Learning for Satellite Imagery 50 cm/px resolution

50 cm/px resolution

Deep Learning for Satellite Imagery 30 cm/px resolution

30 cm/px resolution

The satellite imagery with 50 cm/px resolution:

  • Although at first glance the image appears bigger, what this really means is that each pixel averages the value of a larger area, therefore it contains less information about specific details providing a lower resolution.
  • If you were to zoom the image in order to observe the solar panels these would quickly get pixelated.

The satellite imagery with 30 cm/px resolution:

  • Although at first glance the image appears smaller, what this really means is that each pixel averages the value of a smaller area, therefore it contains finer detail of the area yielding in turn a higher resolution.
  • Even at the current zoom level you can observe what seem to be discernible features in the solar panels.

For reference, the average area of solar panels is between 936 square centimeters in residential installations and 1528.8 square centimeters in commercial installations. This translates to approximately between 31 pixels (residential) and 51 pixels (commercial) at 30 cm resolution, and between 19 pixels (residential) and 31 pixels (commercial) at 50 cm resolution. As you can see, the amount and quality of information that is available for models to learn from is variable depending on the resolution of the data and the effects of other necessary processes inherent to satellite imagery data, such as orthorectification.

Image of Panel Resolutions

Deep Learning For Satellite Imagery: Resolution

50 cm/px resolution

Deep Learning For Satellite Imagery: Resolution

30 cm/px resolution


  • 50 cm/px resolution: When zoomed in, individual panels become blurred.
  • 30 cm/px resolution: Even when zoomed in you can still spot the separation between panels.

Why Utilize Deep Learning?

The role of object detection is to extract information from images of what objects exist within them and where they are located. Although many advanced machine learning and computer vision strategies exist for object detection, none have proven to be as efficient as Deep Learning. Deep Learning can help by minimizing the time and the possibility of error when analyzing high dimensional satellite imagery data when detecting objects of interest, in this case solar panels. Deep Learning models obtained from the Single Shot Detector (SSD) architecture are capable of very accurately detecting, classifying, locating and approximately bounding the areas in an image containing solar panels with the added benefit of being relatively quick.We have chosen this model for the task, but many others could be used in its place. During the rest of this article whenever we refer to the ” model” or “detector,” we mean this very specific implementation of an SSD.

Single Shot Multi-Box Detector

images describing the complex SSD architecture and the overall training process this model utilizes to detect objects. Images taken from the Single Shot Multibox Detector paper.

So, Let’s Create This System

First, we would select our regions of interest and acquire the data. When doing this we should take care to select appropriate data that has similar features to the ones we are interested in detecting, they should also have sufficient quality and resolution in order for the model to be able to learn from them and for us to be able to properly annotate the required features. Then, utilizing specialized software, such as QGIS, we would go about carefully and cleanly annotating all of the solar panels that we could find.

The Annotation Process

QGIS: An open source software that allows for the annotation of georeferenced images.

QGIS: An open source software that allows for the annotation of georeferenced images.

Annotations: All solar panels in image have been annotated by drawing a polygon over their area.

Annotations: All solar panels in image have been annotated by drawing a polygon over their area.

Second, once the annotation process has been finished and we have reached a satisfactory amount of annotations, comes the arduous task of creating, debugging, training, assessing and tweaking a model. For the purpose of this blog, and as mentioned above, we will be using a Single Shot Detector because it has certain characteristics that are desirable for the task at hand. Satellite imagery usually comes in very large TIFF files that need to be cut into smaller more manageable pieces for performance reasons, therefore some preprocessing is needed. With all of this done, we are finally ready to train the SSD model. Depending on the amount of data and the configuration used for this training process the amount of time it will take to train may vary.

Finally, once training is done, we need to assess the results and make any necessary tweaks. Assessing the quality of the model is a very problem-specific and subjective process. In general, by looking at the output from the validation phase and comparing the resulting metrics to the ones of similar projects you can decide when your model is ready for production.

Deep Learning for Satellite Imagery: Model Predictions

Inferences made by the model

The inferences made by the model (the green squares on top of houses), correctly predict all solar panels in rooftops in a region adjacent to the one it was trained in.

Data, Model, Now… What?

The last action in the development of this monitoring system is to incorporate the model inside a bigger solution which feeds the model information, runs the predictions and provides the results in a smooth streamlined fashion. At this point we would develop a group of additional modules for handling each of the pre- and post-processing tasks, and then deploy our solution.

Congratulations! You now know the high-level process of creating a deep learning model for satellite imagery. This solution can be utilized at any point by acquiring the desired satellite imagery at the corresponding resolution and running it through the data acquisition, inference, and analysis pipeline developed. It should provide essential and timely insights into the target infrastructure, in this case solar panels, when they’re most needed, such as during or after a catastrophic event.

We at Wovenware currently have solutions in place and the expertise to leverage deep learning for satellite imagery to provide solutions to these challenges, in this and other scenarios. While we described some of the challenges of working with satellite imagery, every problem/project comes with its own challenges, so we continue to work hard, solving these challenges whenever they arise. Feel free to contact us with any questions about this project or any of your own image detection or advanced software development needs.  You can reach us here or at 877-249-0090.

Mainframe Modernization in the Era of Digital Transformation

Digital transformation is the disruption of business through digital technologies, changing how a business operates and delivers value to customers.

For the past few years, digital transformation has been a key item on the agendas of CxOs everywhere. We’ve been explaining to our clients what digital transformation means and its implications in business, budget, culture, technology and competitiveness. The question that we get asked the most is: “Does digital transformation push innovation?” And, the answer is a resounding yes.

The next question we always get is what technologies should we focus on to achieve digital transformation. There are many solutions, but the key is to treat technology as a business asset, like Uber and Airbnb do. According to Forrester, however, companies that are looking to push the envelope can innovate by implementing emerging technologies such as augmented and virtual reality (AR, VR), robotic process automation (RPA), artificial intelligence (AI) and intelligent agents and chatbots.

The real challenge comes when a bank or telco has a 40-year-old mainframe in its basement, that serves as the host system for millions of customers that are served monthly. Unplugging, or ripping and replacing the system, is simply not an option. But can you still transform the business and implement emerging technologies? Once again, the answer is a resounding yes.

Mainframe modernization does not necessarily imply replacing migrating old legacy systems to newer cloud applications. Legacy systems can be adapted and integrated with modern applications that improve the customer experience. For example, it’s quite possible to implement chatbots and AI in the cloud, while connecting the trusty old mainframe to the data lake that resides in the cloud and feeds it new applications that impact processes, culture and business value.

Banks and insurance companies are masters of this hybrid world, where mainframes and modern applications blend seamlessly together. One of our largest clients has benefited from our work integrating new technology with its legacy system. Through this integration, the firm has been able to create new channels for servicing customers, where self-service chatbots enhance customer service and convert leads to sales. All activity is captured and monitored by real time dashboards which can influence strategies and sale promotions. These new assets are deployed in the cloud. Our enterprise client can make fast decisions based on data, and it can scale as needed without much hassle. At the end, every service order is processed in its time-tested mainframe.

Other enterprise clients are working toward a long-term mainframe modernization strategy that does involve replacing legacy systems. It is part of a broader digital transformation strategy the looks beyond technology and impacts the business operations, employee culture and product offerings. It’s not easy to do away with legacy systems. They take years to build, cost lots of money overtime and let’s face it, in many cases they continue to do the job they were tasked with. But unfortunately, their worth just may be decreasing over time, and they will most certainly reach obsolescence at some point. A strategic approach to digital transformation can help companies leverage what they have, while transitioning to the future, where innovative technologies can give you new ways to interact with customers, increase productivity, save costs and find better ways of working.

Multinational enterprises with big investments and dependencies in legacy systems have been successful in mainframe modernization because they have partnered with experts who understand their business and who also understand that change takes time and a long-term approach. Wovenware has supported the growth and evolution of organizations in highly regulated industries for the past 17 years through the implementation of technologies that expand human capabilities. While technology enthusiasts may have a hard time using the words “mainframe” and “innovation” in the same sentence, mainframes are essential to the 71 percent of Fortune 500 companies that still use them.

I still remember a conversation I had with Carlos, our COO, more than a decade ago. We were attending one of the last JavaOne conferences in SF hosted by Sun Microsystems and were discussing the value of integration to legacy systems as we had a long walk in a foggy afternoon. Little did we know that the conversation – as well as the systems – are still very relevant today.

Incorporating emerging technologies, empowering teams to experiment, and find ways to do daily work in a better way is what innovation is all about. It can infuse the enterprise with new energy and creativity that shatters stagnant ways of doing things, and it’s how strategic transformation is borne. But this type of digital transformation – the kind that is most successful — doesn’t happen overnight. When tried and true legacy systems that continue to provide value, live in harmony with innovative technologies, the ultimate benefits of mainframe modernization are realized. Yet, this type of change doesn’t come easy, but requires a unique digital transformation partner that can transcend the old and the new to create a perfect balance of each.

Wovenware More Than an AI-Software Development Company

Let me start with a little background on myself. I am an industrial engineer, and I have focused my career on project management. I have worked in multinational companies such as Hewlett Packard, Ericsson, Johnson & Johnson, and Telefónica. Each position has been full of learning experiences, challenges, exposure to top management, and marquis companies to add to my resume. But none compare with my experience at Wovenware.

I’ve been a project manager at Wovenware for the past three-and-a-half years. The day I decided to join the team, I had two offers in my hands – the other one was at Medtronic. It was a very stressful day to say the least. I spent the whole day thinking about the pros and cons of each opportunity. I also had a 7-month baby at the time, so I was also considering the work-life balance I wanted. I had 24 hours to make my decision and I do not regret choosing Wovenware one bit.

Four months into my new career venture, Hurricane María hit our beautiful island of Puerto Rico. The whole island was without power. I lived in an apartment that did not have a power generator nor cistern for water. With a one year old, and milk still being the primary source of food, I could not stay there without a refrigerator to maintain the milk. We had an opportunity to get on a plane (which was very difficult at that time) and ended up in Florida. We spent three months living with friends and family who graciously opened their doors to help us. During this time, I continued working remotely. Wovenware never hesitated to be supportive of my decision to leave the island. Senior managers kept saying, “we are also parents, we understand what you are going through.” This made me feel much better with the guilt I had. I did not want it to appear that I was taking advantage of the flexibility they were giving me nor the feeling of abandonment I had. Life was extremely difficult in PR during those times, and I had accessibility to all the “luxuries” that we lost during the hurricane. By “luxuries” I mean water, electricity, open supermarkets, air conditioning, etc. I was able to return to the island three months later when electricity was restored at our apartment.

Today, we are going through another difficult time. This COVID-19 pandemic had brought new challenges, as I imagine it has been the same for everyone. Again, Wovenware’s leaders have gone above and beyond to make sure we are all okay at home.

We all have the flexibility to work from home, which has been a challenge in and of itself, but at least I feel safe in my bubble. Our HR department spent the first three months coordinating the most creative and cool things I have ever seen at a company. These remote activities have included things like yoga nights, disco parties, music videos made by our own employees, collages with all employees wearing our Wovenware polos or T-shirts, tie day, crazy socks day, pimp your desk day, playing Pictionary and Charades, taking ergonomic exercise online breaks, and even a care package with Gustos Coffee, a Puerto Rican Coffee, and a Greca for everyone to make a little bit of our office coffee at home. The list goes on and on.

Since the pandemic began, top and middle management meet every morning for a quick status call. Lately, we have all been asked to bring an interesting, non-business question to ask our co-workers so that we can get to know each other better. We have talked about what food we would eat for the rest of our lives if we had to choose only one; if we were able to be an expert at something, what it would be; and other very interesting questions. Last Friday, however, the activity prepared by our HR department topped all of them. The department contacted our families and made a surprise video. The families all said how much they loved us, how proud they were of us, and how we continue to be strong and successful during these difficult times, always caring about others and making sure everyone is okay. I think we all cried, but they were happy tears.

This is not a company; this is a family! It is the first time in my career that I felt I am not another employee number. I am Aixa and my opinions and ideas are valued. I am part of a great company that is growing at an exponential rate. I am part of a family that cares for me, that makes me laugh, and that I consider as true friends. How many of you can say that about your companies? I wanted to write this blog post to express my gratitude and appreciation for the company I am with today, and where I plan to be for a long time. Helping my working family grow each day, sharing innovative ideas and most of all, having fun at work. Thank you Wovenware for taking care of us, for worrying about our physical and emotional state during these difficult times, for always listening, and for making sure we continue to grow together as a family.