Wovenware Accepted into Microsoft AI Inner Circle Partner Program

SAN JUAN, Puerto Rico — Wovenware, a nearshore provider of Artificial Intelligence (AI) and digital transformation solutions, today announced that it has been accepted into the Microsoft AI Inner Circle Partner Program. Through the program, Wovenware is partnering with Microsoft to accelerate customers’ AI initiatives in a variety of industries.

The AI Inner Circle Partner Program is offered to Microsoft partners with technical expertise in Microsoft AI tools and a proven track record of customer success. It recognizes a partner’s unique expertise and its ability to drive business transformation using the power of AI and data. AI Inner Circle partners champion Microsoft AI technologies and deliver cutting edge AI solutions for customers.

“We’re happy to join Microsoft’s AI Inner Circle Partner Program and collaborate to help customers as they embark on their AI journeys,” said Christian Gonzalez, CEO, Wovenware. “We’re looking forward to accessing and learning about new Microsoft technologies that we can use to build and deploy custom AI solutions.”

Wovenware has been a partner of Microsoft for many years. In 2016, it became one of the first software engineering services firms in Puerto Rico to be an authorized Microsoft Cloud Solution Provider (CSP). In addition it has earned Microsoft Gold Partner competency status on Application Development, Application Integration, Cloud Platform, Data Analytics, and Data Platform competencies.

Wovenware offers a bespoke model of AI solution development capabilities that address the full AI lifecycle. Its innovation workflow methodology covers business use case development; human-centric service design; training dataset preparation; building, training and validation of AI models; integration of models with production applications, and operationalization and continuous learning of models. A large portion of the company’s clients are in highly regulated industries, such as healthcare, government, and telecommunications, where security and privacy are top priorities. In addition to focusing almost exclusively on custom development of APIs and algorithms, Wovenware offers a “private crowd” for data labeling comprised of all U.S. citizens under NDA.

As a member of the Microsoft AI Inner Circle Partner Program, Wovenware has access to online resources, such as AI training materials, trial resources, sales and marketing collateral, information on upcoming events, and AI business opportunities.

Machine Learning in Insurance – Adding New Levels of Efficiency Across All Market Segments

In recent years, insurance firms have set out on their digital transformation journeys, gaining efficiencies, growing their revenue and boosting customer experience by automating operations and providing user-friendly mobile applications. And, Artificial Intelligence (AI) is further transforming the industry in unique and powerful ways we never imagined possible. New applications of machine learning in insurance make headlines every week.  Following are trending AI solutions that are transforming insurance in auto, home, and healthcare markets.

Machine Learning in Auto Insurance

The auto insurance industry has been disrupted by the rapid growth of services like Uber and Lyft, pioneers the sharing economy that created heightened demand for insurers. Yet, it will be really shaken to its core with the advent of self-driving cars. In order to better compete when driver risk is diminished, auto insurance companies are increasingly turning to to machine learning to reduce costs and boost yje customer experience.

  • Risk predictions based on driver habits- Root Insurance has a fully automated process for calculating premiums for specific drivers. Instead of relying on age and other demographic data, they track driver habits through a mobile app. After drivers take a test drive, they will combine the data with other factors and employ advanced predictive analytics to create a risk profile and provide a quote. All without a single agent.
  • Computer vision for assessing car damage- Other companies like Liberty Mutual are using computer vision algorithms to automatically assess the damage in a car from photos uploaded via their mobile app. Trained with thousands of images, the AI algorithm can automatically identify different types of damage and provide a preliminary cost assessment.
  • Customer service virtual assistants- Geiko provides virtual assistants to provide a delightful experience to customers that need guidance or have questions about policies and claims. Rather than searching through pages in a website, the virtual assistant provides a more organic and dynamic experience for the user. Customers can choose to talk to a real person at any time if the chatbot cannot answer a question.

Machine Learning in Home & Property Insurance

AI is transforming home and property insurance by providing better and more effective ways of providing services to clients: from customer support and getting quotes to fully processing claims.

  • AI-powered claims processing- Lemonade is one of the unicorns that has pioneered machine learning in the insurance claims process.  Its AI algorithms can detect fraud in claims and  manage most of the claims process without human involvement. Lemonade’s friendly virtual assistants, Jim and Maya, provide a delightful experience for users.
  • Automated property analysis via satellite imagery- Computer vision applications for insurance are on the rise.  Cape Analytics empowers insurers with AI solutions that analyze satellite imagery and automatically feed data regarding roof type, roof conditions, solar panels, and wildfire risk, among other factors. 
  • Fraud Detection- Using AI to detect fraud is common in many insurance companies around the world.  Artificial intelligence is particularly effective in detecting anomalies and there are many providers that have built specialized fraud-detection solutions in this space.

Machine Learning in Health Insurance

Proactiveness will be the core driver of most machine learning applications in health insurance. Health insurers are shifting strategies to proactively improve health outcomes instead of reactively processing claims for medical benefits. 

  • Churn prediction- Annual enrollment periods are the most stressful time of the year for health insurers. There is fierce competition among providers to attract and retain membership, while people shop around for the best deal in town. Churn prediction models aid health providers in creating proactive strategies in managing annual membership vs the traditional reactive strategies that have been employed up to this point.
  • Predicting hospital readmissions and patients at risk- Detecting patients at risk of developing chronic diseases or getting readmitted to a hospital will help providers proactively look out for their health and prevent future critical conditions. CMS issued a challenge in 2019 to search for AI solutions that can prevent adverse effects and is actively investing in machine learning for insurance.
  • Computer vision for early disease detection- IDx-DR is one of the country’s first autonomous FDA- approved clinical diagnostic tools and is powered by artificial intelligence. It uses computer vision to analyze retina images to diagnose diabetic retinopathy, a condition that can cause blindness. The exams are also eligible for reimbursement by the Centers for Medicare and Medicaid Services (CMS).

Upcoming Trends

The wave of innovations using machine learning in insurance is just getting started.  We can expect other technologies to continue to gain ground in the upcoming months and years and will likely see new business models for insurers.

  • New Data– Tapping new data generated by wearables like FitBit and genetic reports provided by companies like ancestry.com, provide a whole new realm of possibilities for health and life insurers in terms of performing analysis at an individual level. While there are many questions surrounding privacy and ethics, the potential to provide real value to both patients and providers is undeniable.
  • Personalized Benefits- As generations continue to embrace personalized recommendation systems like those offered by Netflix and Amazon, insurers will see increased pressure of finding ways to provide personalized insurance programs  to fit an individual’s particular needs.
  • Holistic Wellness- Health insurance providers will continue to expand their offerings to include holistic benefits that promote mental health, nutrition, exercise in addition to medical checkups to drive positive health outcomes.

Many organizations are implementing AI solutions for claims processing, fraud detection and virtual assistants. Machine learning in insurance is quickly becoming commonplace, but we are only seeing the tip of the iceberg in the digital transformation of a very traditional industry.

Predictive and Prescriptive AI: It’s All About Anticipating V. Controlling Decisions

AI-based predictive analytics is gaining steam across industries, helping insurance firms predict customer churn; retailers predict customer preferences; or physicians anticipate the patients that will be making a return visit. It’s becoming quite popular and helping firms root out problems even before they occur; but the problem, is, prescriptive AI has not quite kept pace with predictive AI – knowing what the problem is can be a far cry from knowing how to solve it.

I recently explored this topic in an article I wrote for the Forbes Technology Council. In it, I examined the differences between predictive and prescriptive analytics, and how prescriptive AI can actually pose an ethical dilemma when gone unchecked.

Prescriptive analytics can be tricky since it can be used to manipulate human behavior, and while it’s still a ways off before it is really feasible, social media companies, such as Facebook, have been researching and experimenting with ways to do just that. About 10 years ago Facebook conducted an experiment to see how it might influence human emotions and behavior. The site populated half of news feeds with positive information, and the other half with negative. They found that after a week, individuals whose newsfeeds had the positive slant posted more positive comments, while those with negative ones did the opposite.

While prescriptive analytics may be attractive to marketers, the industry should beware of deceptive techniques that manipulate behavior – much like subliminal advertising, which led to an FCC crack-down on that practice.

Despite the potential for prescriptive AI to be used for less than beneficial uses, it will certainly play a role for the good, helping organizations act on the data-driven intelligence it receives through predictive AI. Not only do the two forms of AI need to work together, but they also will require humans in the loop, to provide sound reason, judgement and oversight – which will never go away.

An Agile Data Science Process Spurs Innovation

Data scientists are often tasked with uncovering the unknown, which makes it nearly impossible to create a planned schedule with hard deadlines around specific milestones. Data science projects are packed with uncertainty, and almost always involve failed experiments. Business leaders grapple with this reality because they are used to making budget decisions based on a return on investment (ROI) analysis. To get the best results out of AI projects, teams should follow an agile process that blends techniques from the scientific method, design thinking methodologies and agile software development framework. In practice, data science sprints may feel like this:

Data Science Sprints

But in reality, the employing specific habits, disciplines and techniques make the exploratory and creative process  very efficient. In this blog we explain how each of these methodologies are integrated in an agile data science process.

The Scientific Method

You cannot remove the science from data science. Like research projects in the scientific community, a data science project should have a clearly stated question or problem, a hypothesis, experiments, and observations. 

  • Defining the problem – A clearly defined problem and success metrics are critical to an AI project. Without direction, budgets may be consumed while searching for answers to the wrong problems. 
  • Researching the data– Exploratory data analysis is a foundational step in building machine learning models. Data engineers will answer the following questions: Does the data have the right level of quality? Is there enough data to address the problem? Are there any biases? Are there correlations between features and the specific outcome being evaluated? After researching and understanding the dataset, the team will be ready to choose the machine learning algorithms best suited to solve the problem.
  • Stating a hypothesis— A data science team will state a hypothesis on how to structure a Machine Learning (ML) solution around the data provided. Documenting the hypothesis and obtaining feedback from business experts reinforces the team’s focus and direction when designing experiments.
  • Conducting experiments and recording observations — Data scientists are expected to conduct multiple experiments throughout a sprint and analyze their results. Some experiments may be successful, and others may fail.  Results will not necessarily improve incrementally with every new experiment.  The first experiment may yield the best results and the last, the worst.
  • Extracting Insights– Acquiring knowledge and insights in every sprint is probably the most important step in an experimental process. If an experiment fails, what can we learn from this? Is more data needed? Different data? Better quality data? Are there biases in the dataset? 

Identifying actionable insights in each experiment will drive better results in data science projects. An agile data science sprint will include all of the same key phases performed by scientists in experimental research and development projects.

Design Thinking

The design life-cycle is very similar to the data science lifecycle in that it involves unstructured discovery and experimentation that will be improved through non-linear iterations. However, the design discovery process is driven by multidisciplinary teams with diverse skills and perspectives and is focused on the humans it is designing for.  At Wovenware, we focus on augmenting human capabilities with transformational AI innovations. The only way we can achieve this is by deeply understanding and empathizing with the people we are serving and thinking through the lens of  customer experience. The design-thinking approach provides a framework to understand if the solutions we are looking to build are desirable, technically feasible and economically viable.

Design Thinking Process

  • Empathize- Before diving deep into exploratory data analysis, our data science projects begin with a series of workshops and interviews to understand business objectives and experience and pain points of all the people that interact with the organization. We conduct interviews and talk to real people who may be impacted by the solution and place them front and center in every stage of the data science process.
  • Define- Defining user personas, challenges, and pain points helps set the stage, so that we can define a very specific problem that AI can solve and how we can measure success. The user personas will become the central focus of every iteration of the  project. In AI projects we take this a bit further and define “bot personas.” If an AI product needs to have human-like qualities to interact with real people, what should they be? For example, If it has natural language capabilities, what tone and language should it use?
  • Ideate- To spark innovation, people with diverse skills, backgrounds and roles participate in ideation sessions. New ideas that challenge assumptions and what people envisioned for the project are generated in an open and creative environment. Teams often emerge from ideation workshops with ideas they never imagined possible. 
  • Prototype- Building quick prototypes before spending large sums of money is a pivotal step for designers. In data science projects, we build prototypes and proof of concepts (POCs) to validate that an AI solution is feasible and has practical potential.  
  • Test- In the validation stage the design team will have real people interact with the prototype and record their behavior and reactions to analyze what works and what needs refining in the next design iteration. 

Integrating design thinking principles and diverse perspectives in an agile data science process will help teams create a desirable, feasible and viable design where humans and machines work seamlessly together to solve real-life problems. 

Agile Software Development

The Scrum framework was designed to help solve complex problems, and data science almost always addresses complex problems. It helps teams learn and adapt in short iterative cycles. Agile data science sprints resemble Scrum because they incorporate a lot of its artifacts and events.

Scrum Process

  • Sprints Length– Sprints are generally 2-4 weeks. Short experiments help avoid going down rabbit holes and keep the team focused on objectives. Short sprints also set the stage to have frequent discussions of assumptions, results and future experiments with domain experts.
  • Team Roles- Like Scrum, the data science team will receive priorities and direction from a business owner but will otherwise self-organize and be responsible for defining, planning, and executing the experiments, tasks and activities required to complete the desired results.
  • Effort Estimation- Estimating the time it will take to complete an AI project is daunting if not impossible for a data science team. There is just too much uncertainty surrounding experimentation and research.  Using story points and relative order of magnitude helps establish a baseline for planning and forecasting without stressing the team with hard deadlines.
  • Daily Scrums- Data science requires thoughtful collaboration between team members and Daily Scrums are a great way to get discussions going, not just within the data science team but also with business experts, designers, and other members of the team. It is especially useful when teams are working remotely and have limited or no physical contact. 
  • Retrospectives- Inspection and self-reflection is an important habit and discipline to promote in data science teams. Inspection goes beyond the results of the sprint and experiment and focuses on the agile data science process itself. What is working for the team? What is not? What can we do better?

 Innovation in data science does not come with a step-by-step handbook. The iterative and retrospective process should focus not only on the solution and the end-user but on the project team and how it can improve the ways to work together.

The Agile Data Science Manifesto

The following principles will help data science teams uncover better ways of working together and is inspired by the original Agile Manifesto.

  • Outcomes vs. Metrics– Building AI models often turns into a purely technical challenge of obtaining the best accuracy. Data science teams should focus on business outcomes and use model performance metrics as supporting evidence of the potential impact of the solution.
  • Multi-disciplinary Teams vs Technical Teams– While data scientists can wrangle data and create sophisticated algorithms and models, few have the business expertise to have a 360-degree understanding of the data and the problem they are solving. A holistic data science project team should include domain experts, service designers and business analysts who can engage in interdisciplinary thinking to validate assumptions on data, possible biases and interpretation of results. 
  • Simplicity vs. Sophistication- A lot of new and advanced research in artificial intelligence is being released every day, continuously increasing the potential and sophistication of AI models. To keep up with the pace of research, data scientists must learn and implement new tools, techniques, and algorithms. However, when creating industry solutions, the team should aim for simplicity and not sophistication. Do not use a neural network if a simpler linear regression solves the problem, and use the minimum amount of data needed to achieve business outcomes. 
  • Knowledge vs Product Features- The greatest value in building artificial intelligence solutions is uncovering knowledge about individuals, organizations or the world. While software solutions focus on feature development, data science solutions will focus on insights creation.
  • Balance Uncertainty vs Avoiding Risk- The process of research and experimentation is filled with uncertainty. Innovation requires creativity and boldness and should not be thwarted by risk aversion. Uncertainty should be embraced as part of the process yet contained within carefully planned sprints and introspective sessions.

Managing AI projects can be complicated, but the principles in this manifesto will help teams work effectively and obtain excellent results.

Agile Data Science at Wovenware

As a design-driven AI and software development consultancy,  Wovenware has created a proprietary agile data science process that we have coined “Innovation Sprint.” 

By combining the best practices, tools and techniques from design thinking, agile software development and data science project management we drive positive business outcomes and better customer experiences. 

Agile Data Science Process @ Wovenware

Giving Back To Our Community

At Wovenware it is important for us to help, support and serve our community, as part of our Corporate Social Responsibility commitment. In support of that goal, during the past few months we have visited San Jorge Children’s Hospital, specifically the intensive care and pediatric-oncology wards; and Casa Manuel Fernández Juncos.

Why these wards in particular at San Jorge Children’s Hospital? It is hard to see someone fighting cancer and it is even worse to see a kid dealing with it. We are putting all our efforts into doing all that we can to help children across our beautiful island understand that they are not alone as they fight cancer. We shared with the pediatric patients, gave them some gifts based in the average ages. For children between 1-8 years we had plastic dolls, coloring books, crayons, and elastic toys. For children older than 9 years old, we had playing cards, coloring books, and elastic toys as well. For every child we had balloons, because as we all know, the kids love them.

Our main goal as we visited the children’s wards was to give, not only the children, but those that care for them, a chance to smile, to think about positive things and to forget their situation – even for a moment.

Wovenware @ San Jorge Children's Hospital

I planned the Wovenware community service programs, along with Kimberly Piñero, who helps me with these activities and who is part of the Digitizers Team. I realized once again that it is not only monetary help that is so desperately needed, but it is also time and fellowship.  Being there for these kids and seeing their faces light up with laughter is what makes it all worthwhile.

This realization also was felt as we visited Casa de Niños Manuel Fernández Juncos, a non-profit corporation that provides reeducation and treatment services to the problems associated with the abuse, abandonment and neglect of children, adolescents, and young people in our Island.  We collected first necessity goods such as: bottle waters, cookies, toilet paper, toothbrush, rice, baby wipes, toys, cleaning products, canned food, and juices.

Wovenware @ Hogar Manuel Fdz Juncos

We are proud of our employees’ commitment to serving our community and the time they dedicate to our CSR initiatives. We are committed to continuing to make a difference in children’s lives, raising much-needed funds, giving our time and talents, and supporting education across Puerto Rico to make a brighter future for our children.

My First Year as a Software Developer

From the start of my journey toward a bachelor’s degree in Computer Science, I understood that it was going to be a challenging career in which I would need to keep studying for the rest of my life. I understood that I would have to be self-disciplined and dedicate endless hours to continue my education beyond the classroom. This discipline has been key to my career as a software developer.

So you may ask, how was my first year as a software developer at Wovenware? In one word: complicated. The work environment was very different from college. No longer was it a question of failing an exam or an assignment, but now I could be failing a client and in turn the company I work for. After you graduate, responsibilities increase and every action counts, for better or for worse.

First Impressions

When entering “the real world,” I started seeing code for big and complex projects, written by many other more experienced developers and that intimidated me. Participating in status meetings with peers, quickened my heart. I was afraid of saying the wrong thing or admitting I was stuck on tasks. While I’ve always been on the quiet side, I knew that I had to work on my communication skills to succeed. I completed processes of uploading code to the user acceptance testing (UAT) or production environment while shaking out of fear of messing up. Over time all of these feelings changed. I became more familiar with my colleagues and gained confidence when speaking. Little by little, I was learning more about the system and tasks were completed in less time.

My First Task

My first assignment at Wovenware was the real test for me. Like a complete beginner, I began to attack the problem without first analyzing whether a solution already existed. By wanting to look good and complete the task as soon as possible, I did not first analyze it based on my current knowledge of the system. I quickly learned that there was another more appropriate solution. My lesson learned is that before writing a code, it’s important to intently look at the problem and if you still don’t understand it, ask. Once you identify the problem, investigate if a solution already exists. In this way, little by little, you will learn from the environment.


Meetings were always my weakness. Regardless of whether it was with the group or the client, they made me anxious. I’ve learned, however, that your voice is like a promise; you must be totally honest and sincere at all costs. It’s also very important to listen to colleagues and understand what they do and their contributions. Together you can help one another find solutions to problems.


I am fortunate to say that the Wovenware team I am a part of is truly supportive of one another, always transparent, and ready to help. The company has given me great mentors, for which I am very grateful.

One way that I began to give back to the team during my first year is, when I finished my tasks, I would go to them and ask  how they were doing or if they needed any help. Often, while they were telling me about a specific problem, a solution would come to them or to me. This interaction has always helped me feel integrated with the team instead of feeling like a burden. It is very important to realize that we are not a burden to others and that every day we should look for ways to continue supporting our colleagues. After all, our actions affect everyone.

Fulfilling One Year

After having carried out many tasks and gaining confidence in my solutions and social interactions, I began to worry about my code. Does it meet the company’s standards? Can my colleagues read my code? Can I improve my code? All of these questions were answered throughout the year. Between the code reviews by my colleagues as well as studying the code of others, I learned more about the importance of writing well. Basically, it is our signature, our reputation, and our legacy in the project. Therefore, it is very important that the code can be understood and readable for the good of the team in addition to other future programmers.


  • Be humble and accept your mistakes.
  • Stay ahead of the game. If you know that you will be working with a tool that you’re not familiar with, orient yourself and find out about it in advance.
  • If you have any doubts, do not hesitate to ask. Do not start solving things without understanding the problem.
  • Be self-taught and master problems. You can’t wait for knowledge to come to you. Motivate yourself to learn more and more.
  • Do not hesitate to train for certifications. You are giving value to yourself as a professional, as well as the company.
  • When a project manager asks you about deliverables, try to give a specific date for completing the task. Do not be frustrated if at first you cannot comply with the deadline, it is a skill that you acquire over time.

Future Work

The key is to keep learning. As for me, I plan on continuing to improve my coding skills, delve into mobile applications, design patterns and many other technologies such as cloud computing. I hope to continue to grow as a professional and someday maybe even share my knowledge through teaching.

The world of programming is huge and there is a lot to explore along the way.