Today we are super excited to share the news that we launched our Phantom Stock for All Program. Unlike in the US or the UK there is no legal framework for employee stock options in Austria. Therefore we had to construct a model that mirrors the behavior of a stock option plan adjusted to the Austrian legal system. The result is our MOSTLY AI Phantom Stock Option Plan (PSOP).

Why did we launch a Phantom Stock for All Program?

Our founders, management team, and investors believe that we need a world class team to achieve our mission of enabling organizations to thrive ethically and responsibly with smart and safe synthetic data and that employee ownership pulls in top talent. Employees are looking for more than just work that pays the bill. They are looking for purpose and a way to change the world and they want to participate in the value they create. Phantom stock can help us along the whole journey of being a member of the MOSTLY AI team.

Hiring Top Talent

A great tool to attract top talent across the globe. While in some regions employees expect stock options, others might not even be aware of the concept. We want to reward every MOSTLY in a meaningful and fair way across the globe. As a remote first company, it is especially critical to create shared interest and to put all our people on the same track, no matter which part of the world they live in. The Phantom Stock for All Program creates a shared space where a global mission becomes a reality. 

Keeping People on Board

We know that our talents are among the best in their respective fields and in high demand on the market. Therefore, we want to retain them and let them benefit from their hard work in the long run.

Motivation

Every MOSTLY has a stake in the company and understands and benefits from going the extra mile when needed. Yearly reviews and significant refreshers also reward individual and team performance.

Aligning Goals across Teams

We all share a common goal - the overall, long term company success. This unites the teams and pulls everyone in the same direction. MOSTLIES are fearless (one of our core values) - we explore uncharted territories and if we succeed, we succeed together. 

How does MOSTLY AI's Phantom Stock for All Program work?

Granting

We have granted every team member phantom options and will include phantom stock options in the offers for new joiners. This means that all our employees receive rights which entitle them to certain bonus payments by the company calculated on the basis of Phantom Shares in the case of a liquidity event, which can either be an IPO or an acquisition of the company.

Vesting

There are different types of vesting schedules possible, such as backloaded vesting or annual vesting. We opted for the employee-friendly “monthly-vesting” option with a standard one year cliff period. While many programs terminate vesting in case of long term absence (longer than 30 days) we decided to continue vesting up to a 3 months absence - and in case of family leave even up to 12 months.

Exercising

Austrian Tax Law (as many other European laws, for example in Germany) doesn´t know a “Fair Market Valuation” and therefore many companies offer strike prices based on the latest fundraising valuations. We also do that, but offer a considerable discount to make the program even more attractive for all of our team members.

Benefitting from the Option

We are all working towards a high valuation in case of a liquidity event so that everyone will benefit from their hard work and contribution. It’s so simple that we wonder why this isn’t how things are done everywhere. 

Big Kudos to the team from Index Ventures for pulling together a comprehensive guide for European startups. When designing our PSOP system, we also took inspiration from Balderton Capital’s guide to employee equity.

Here at MOSTLY AI, we are big fans of open-source software. We are leveraging more than 90 open-source software packages for our synthetic data generator. It is safe to say that without open-source software libraries, it would have been impossible to get where we are today so quickly. We sometimes get asked by prospects why they should choose MOSTLY AI’s Synthetic Data Platform over freely available open-source solutions, like MIT’s Synthetic Data Vault, to synthesize data. This blog post provides an answer.

Update: SDV changed their license model in 2023, and is NOT open-source anymore.

SDV vs MOSTLY AI: Synthetic data quality

The answer is multifaceted, but the main point is the quality of the synthetic data you can generate. We pride ourselves on delivering synthetic data that is so close to the real data that it can be used as a drop-in replacement without sacrificing any meaningful quality. And, of course, all while guaranteeing full privacy.

Already two years ago, we looked at the quality of synthetic data generated with two popular open-source models: CTGAN and TVAE. Back then, we showed how MOSTLY AI’s synthetic data had higher accuracy on multiple dimensions. This time we look more broadly at the open-source software library developed by MIT,  the Synthetic Data Vault (SDV). It was initially released in 2018 based on research work led by Kalyan Veeramachaneni. SDV is a Python library that supports three types of data: single table data, relational data, and time series data. In addition, SDV provides an evaluation and benchmarking framework, SDGym, and comes with 100+ datasets that can be used to explore the functionality.

For this benchmarking exercise, we picked five of the 19 provided single table datasets to get a good variety of data in terms of size and structure:

Currently, SDV offers five different models for synthesizing single table data: Tabular Preset (FAST_ML), GaussianCopula, CTGAN, CopulaGAN, and TVAE. To get a proper overview of the state of the art of open-source data synthesis, we spun up some virtual machines and synthesized all five datasets with all available models. And of course, we used the latest release of the MOSTLY AI Synthetic Data Platform to synthesize these datasets to compare. For the record – we used the standard configurations of all models and of our platform. We did not specifically try to tune any dataset. In total, we created more than 5 million rows of synthetic data or 300 million synthetic data points.

The big picture of quality includes the functionality of the synthetic data

Since we wanted to check out SDV more broadly, we also had a look at the functionality to evaluate the quality of generated synthetic data. SDV’s Evaluation Framework takes a real and a synthetic dataset as input and then calculates up to 29 different metrics comparing these two. It returns the average of the scores of the individual metrics, which results in an overall score from 0 to 1, with 0 being the worst and 1 being the best (= the synthetic data is really close to the real data).

For our benchmark, we picked three metrics that worked without any further configuration (LogisticDetection, CSTest, and KSTest) and had SDV report the aggregate score. CSTest (Chi-Squared test) and KSTest (two-sample Kolmogorov–Smirnov test) are statistical metrics that compare the tables by running different statistical tests. LogisticDetection is part of the detection metrics, which evaluate how hard it is to distinguish the synthetic data from the real data by using an ML model (in this case a LogisticRegression classifier).

The results are summarized in the chart below:

* Please note that no synthetic data for covtype could be created with CopulaGAN due to time-out issues, even on a VM with 224 vCPUs

In short: MOSTLY AI beat every single open-source model for every single dataset. Unsurprisingly the less compute intense FAST_ML, and GaussianCopula models cannot create highly realistic synthetic data with average scores of 0.68 and 0.63, respectively. From the more sophisticated models, TVAE performs best with an average score of 0.82, followed by CopulaGAN (0.78) and CTGAN (0.74). MOSTLY AI’s average score is 0.97.

SDV vs MOSTLY AI: Beyond the hard metrics & further evaluations on synthetic data generation

In practice, you will want to evaluate synthetic data on more dimensions than statistical and detection metrics. High-level metrics give you a first assessment of the quality of the created synthetic data, but the real deal is when synthetic data is actually evaluated by performing the exact same downstream tasks you would have performed using the real data.

Again and again, these analyses confirm what we already know: MOSTLY AI’s Synthetic Data Platform delivers the most accurate synthetic data consistently. But don’t take my word for it: you can find all the created synthetic datasets as a download here to perform whatever kind of analysis you wish.

The heart of our synthetic data platform is where we do not rely on open source but instead have developed our own proprietary IP. The approach and the deep learning architecture used to train a generative model. We have done so because this is what really matters when it comes to achievable synthetic data quality.

There are other reasons to consider when choosing a synthetic data generator. In addition to unmatched synthetic data quality, some of the reasons for choosing MOSTLY AI’s Synthetic Data Platform include:

• Ease of use: Our platform is so simple to use, practically anyone can synthesize data. You do not have to write a single line of code. You do not have to worry about choosing the generative model or about hyperparameter tuning. Just provide the data you want to synthesize, and the platform takes care of the rest – making sure to deliver the highest quality synthetic data.
• Speed: Training sophisticated generative models is compute-intense, and takes time and money. We waited several hours for some CTAN and TVAE models to train on strong VMs. With our platform on a lightweight VM, the synthesizations never took longer than an hour. In production, this can lead to significant cost savings.
• Flexible data ingestion: Instead of being limited to ingesting CSV files, you can connect directly to various data sources, including cloud buckets (e.g., AWS S3) or relational databases like Oracle, MySQL, and PostgreSQL. This saves time and pre- and post-processing steps that are otherwise necessary.
• Privacy guarantees: Synthetic data is not, per default, fully anonymous. For example, you need to make sure to handle outliers and extreme values properly. MOSTLY AI’s synthetic data generator takes care of all that automatically.
• Dedicated support: We have been working in this space for more than five years and know synthetic data inside out. If you encounter any issues, we are there to provide support – for our enterprise clients with guaranteed SLAs.

SDV vs MOSTLY AI: In conclusion

In conclusion, at MOSTLY AI, we are enthusiastic supporters of open-source software and recognize its significant contribution to our synthetic data generator. Our rapid progress and success can be attributed to leveraging over 90 open-source software packages. However, when prospects inquire about why they should choose our Synthetic Data Platform over freely available open-source solutions like Synthetic Data Vault (SDV), we have compelling reasons to offer.

The key factor that sets us apart is the exceptional quality of the synthetic data we generate. We take great pride in delivering synthetic data that closely resembles real data, allowing for seamless integration without compromising privacy or sacrificing quality. In a previous analysis, we compared the accuracy of synthetic data generated using two popular open-source models, CTGAN and TVAE, and demonstrated that MOSTLY AI's synthetic data exhibited superior accuracy across multiple dimensions. This time, we conducted a broader evaluation by examining SDV, an open-source software library developed by MIT.

To assess the quality of the generated synthetic data, we utilized SDV's evaluation framework, which employs various metrics to compare real and synthetic datasets. The results were consistently in our favor, highlighting the superiority of MOSTLY AI's Synthetic Data Platform.

Beyond statistical and detection metrics, we firmly believe in evaluating synthetic data through practical applications. Repeated analyses have validated that our platform consistently delivers the most accurate synthetic data. However, we don't expect you to take our word for it. We invite you to explore and analyze the synthetic datasets we have created, which are available for download.

While open-source software plays a crucial role in our work, we have developed our proprietary intellectual property to ensure the highest possible synthetic data quality. The heart of our Synthetic Data Platform lies in our unique approach and deep learning architecture for training generative models.

In addition to exceptional data quality, there are other reasons to consider our Synthetic Data Platform. We prioritize user experience, offering a straightforward and code-free platform that eliminates the need for choosing generative models or fine-tuning hyperparameters. Moreover, our platform ensures speed and efficiency, leading to significant cost savings compared to training sophisticated generative models using open-source solutions. We also provide flexible data ingestion capabilities, enabling direct connections to various data sources, saving time and effort on pre- and post-processing steps.

Privacy is of utmost importance, and our synthetic data generator automatically handles outliers and extreme values, ensuring privacy. Lastly, we offer dedicated support, leveraging our extensive experience in the synthetic data domain to provide assistance to our enterprise clients with guaranteed service level agreements (SLAs).

If you would like to experience the power of the MOSTLY AI Synthetic Data Platform, we encourage you to sign up and generate synthetic data for free. We are confident that our platform's capabilities and the quality of synthetic data it produces will exceed your expectations.

Experience the power of the MOSTLY AI Synthetic Data Platform for yourself and sign up to generate synthetic data for free.

Synthetic data is quickly becoming a critical tool for organizations to unlock the value of sensitive customer data while keeping the privacy of their customers protected and in compliance with data protection regulations such as GDPR and CCPA. It can be generated quickly in abundance and has been proven to drastically improve machine learning performance. As a result, it is often used for advanced analytics and AI training, such as predictive algorithms, fraud detection and pricing models.

According to Gartner, by 2024, 60% of the data used for the de­vel­op­ment of AI and an­a­lyt­ics projects will be syn­thet­i­cally gen­er­ated.  

MOSTLY AI pioneered the creation of synthetic data for AI model development and software testing. With things moving so quickly in this space here are three trends that we see happening in AI and synthetic data in 2022:

1. Bias in AI will get worse before it gets better.

Most of the machine learning and AI algorithms currently in production, interacting with customers, making decisions about people have never been audited for fairness and discrimination, the training data has never been augmented to fix embedded biases. It is only through massive scandals that companies are finding out and learning the hard way that they need to pay more attention to biased data and to use fair synthetic data instead.

2. Companies’ data assets will freeze up due to regulations and declining customer consent.

Regulations all over the world are getting stricter every day; many countries have a personal data protection policy in place by now. Using customer data is getting increasingly difficult for a number of other reasons too - people are more privacy-conscious and are increasingly likely to refuse consent to using their data for analytics purposes. So companies literally run out of relevant and usable data assets. Companies will learn to understand that synthetic data is the way out of this dilemma.

3. Synthetic data will be standardized with globally recognized benchmarks for privacy and accuracy.

Not all synthetic data is created equal. To start off with, there is a world of difference between what we call structured and unstructured synthetic data. Unstructured data means images and text for example, while structured data is mainly tabular in nature. There are lots of open source and proprietary synthetic data providers out there for both kinds of synthetic data and the quality of their generators varies widely. It’s high time to establish a synthetic data standard to make sure that synthetic data users get consistently high-quality synthetic data. We are already working on structured synthetic data standards. 

If you’d like to connect on these trends, we’re happy to set up an interview or write a byline on these topics for your publication.  Please let us know - thanks.

2021 has passed in the blink of an eye, yet MOSTLY AI can be proud as this was a revolutionary year of many extraordinary achievements. While we are already excited for what 2022 holds for us, we are taking a step back to look at the highlights and major milestones we have accomplished in 2021.

Synthetic data revolution

Our developers had a busy start to the year with the new upgrade of our category-leading synthetic data generator, MOSTLY AI 1.5. Alongside many shiny new features, the big buzz was about our synthetic data generator now supporting the synthesis of geolocation data with latitude and longitude encoding types. Say goodbye to harmful digital footprints and hello to privacy-safe synthetic geodata!

This was not enough for our very ambitious team; so in the second half of the year, they pushed the boundaries even further by truly revolutionizing software testing. With this new version of our platform, MOSTLY AI 2.0 became the first synthetic data platform that can automatically synthesize complex data structures, making it ideal for software testing. By expanding the capabilities to multi-table data structures, MOSTLY AI now enables anyone – not just data scientists – to create synthetic data from databases, automatically. This improves security and compliance and accelerates time to data. Our team truly deserves a toast for this!

The Data Democratization Podcast

We’ll be soon celebrating the first birthday of “The Data Democratization Podcast”, which we started back in January 2021. With over 2000 downloads in 2021, the podcast was an absolute hit! Our listeners had the opportunity to get so many insights from knowledgeable AI and privacy experts working in top-notch companies who shared their experiences, advice, and real-life case studies. We are entering the new year with even more enthusiasm and are preparing some special surprise guests for you. Stay tuned!

Synthetic data training for superusers

In 2021 we also launched our professional services and training program intended to help create the next generation of synthetic data superusers within enterprises. Several clients have already leveraged this first-of-its-kind program to kickstart their synthetic data journeys, with very positive results. As synthetic data pioneers, we have the most experienced team in the world. Our top engineers, architects, consultants, and data scientists have seen it all. They know what makes or breaks a company's synthetic data adoption, no matter the use case. From scaling ethical and explainable AI to providing on-demand, privacy-safe test data, the know-how is here.

Synthetic data talks

Despite COVID-19 we have managed to attend multiple conferences. While most of them happened virtually, we participated in Slush 2021 in person! Our Co-Founder & Chief Strategy Officer Michael Platzer rocked the stage presenting at this year's event in Helsinki, Finland. We are proud to have been invited to present our synthetic data solution to the world and - while staying safe - connect and exchange ideas with some of the most brilliant minds.

The only synthetic data provider to achieve SOC2 and ISO certifications

With data privacy and information security at the heart of everything we do, our efforts to ensure the privacy and integrity of our customer’s sensitive data by following strict security policies and procedures have been officially recognized this year. In March, we received the SOC 2 Type 2 certification, which is an audit report capturing how a company safeguards customer data and how well internal controls are operating and later in November, we got awarded the ISO 27001 certification which is a globally recognized information security standard. 

Thanks to both SOC2 and ISO certifications, our customers and partners can now speed up vetting processes and immediately get a clear picture of the advanced level of information security standards we hold.

Growing the Order of Mostlies

All this wouldn’t be possible without MOSTLY AI’s most important asset – our team (or Mostlies as we like to call them). In 2021, we welcomed quite a few new Mostlies to the team - amongst them new executives to strengthen our product, marketing and sales activities. 

The first one to join the team this year was Andreas Ponikiewicz as our Vice President of Global Sales, who took the lead for MOSTLY AI's international sales team across Europe, North America and Asia and has brought our communication with the clients to the next level. Shortly afterward, we welcomed our new CTO, Kerem Erdem, onboard. As a true captain, he is leading us on the way to accelerate our tech performance and enable organizations to thrive in an ethical, responsible way with smart and safe synthetic data. To help get the word out, in early May, Sabine Klisch joined the team as VP Global Marketing and is now leading our creative marketing team on our journey to position MOSTLY AI as the global leader for smart synthetic data. And to spice up the story even more, we have added a special Italian ingredient – Mario Scriminaci, our new CPO who is making sure our synthetic data platform is the number one solution and provides our customers with better-than-real data.

The Best Employer Award

As already mentioned, Mostlies are the most important part of MOSTLY AI and it seems we are doing something right since we made it to the top 3 of Great Place to Work and received Austria's Best Employers 2021 award. 

The MOSTLY AI team is truly diverse, with more than 15 different nationalities represented. Almost 40 members strong, we are organized in several teams, including data science, engineering, product, marketing, sales, and operations. The majority of us are based at our headquarter in Vienna, but an increasing number are working remotely spread across the entire world. What has started as a necessity because of COVID-19 has now become an integral part of our company culture.

Looking back, we can say this year has exceeded our expectations by far. One team of devoted professionals all united with the same vision – to empower people with data and build a smarter and fairer future together. 

What’s next? 2022 is said to be the year of synthetic data. According to Gartner, by 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated. 2022 will also be the year of MOSTLY AI and we will have exciting news to share with you very soon.

For MOSTLY AI, the security of our customers' data is a top priority. Our efforts to ensure the privacy and integrity of their sensitive data by following strict security policies and procedures has now been officially recognized. We are pleased to announce that we are SOC 2 Type 2 certified!

What are SOCs?

SOCs, or Service Organization Controls, are a set of compliance standards that were developed by the American Institute of CPAs (AICPA), a member network of more than 430 000 CPAs around the world. The ability of a company to handle confidential information is examined through an independent auditing process of the organization’s policies, procedures, and internal controls. Testing and reporting of these controls is important because they impact the security, privacy, and confidentiality of sensitive data.

Why does a SOC 2 certification matter?

Working with SOC 2 certified vendors, such as MOSTLY AI, assures the customers that the vendor follows consistent security practices and is able to keep customers’ valuable data always safe and protected through the implementation of standardized controls as defined in the 'AICPA Trust Service Principles framework'. The idea of synthetic data was born out of the need for a bullet-proof data privacy technology. We live and breathe data privacy and being fully aware of all that it entails makes us extra motivated in following state-of-the-art security measures.

“I believe that our SOC 2 type 2 certification proves that our internal processes are completely aligned with the protection of users’ data. It shows how mature we are as an organization and that our customers and their data are in safe hands.” Tobias Hann, CEO, MOSTLY AI

Growing up on an organic farm in the mountains made me realize early on that taking care of animals, the forest, and the environment has a direct effect on their well-being. It is extremely gratifying to see life flourish through your personal effort. Every action has a reaction; therefore, it is our responsibility to treat animals fairly, take care of the land, and always put the environment first.

When running a business, the results of our personal or business actions might not always be plainly visible. However, they are there and affect the world around us positively or negatively. Many people, including myself, nowadays strive to work for a company that is aiming to create a positive impact in the world, whether through its products or by taking on social or environmental responsibility. In fact, the majority of people (62%) actually want to work for a company that has a positive impact in the world, and 50% would even work harder if their work benefits others. I can certainly relate to this as I find it very motivating to work for a company that cares about the people and the community.

I joined MOSTLY AI because I believe in its mission of protecting people's privacy and promoting a positive change throughout the industry. Startups have always fascinated me as they can disrupt an entire system and become a major force when promoting change or, as in this case, creating sustainability. According to George Serafeim in a Harvard Business Review article, there is significant evidence that sustainable companies are the future. They not only deliver innovative approaches to products and business models, but they can also change the way things work, including how we combat climate change. They attract better talent and outperform companies with low sustainability levels in terms of performance and financial returns on investment. Therefore, so-called environmental, social, and governance (ESG) investing as a means to sustainable value creation is on an upward trend. Creating a sustainable business can increase your business value significantly.

MOSTLY AI recognizes the importance of taking on responsibility not only for our people but also for the environment. As it is impossible to have zero emissions, we aim to be at least carbon neutral. When the environmental initiative became a topic, I was eager to join the efforts of making MOSTLY AI carbon neutral and shortly afterward became our Climate Officer. In this role, I became directly responsible for measuring our climate impact and initiating measures to decrease our carbon footprint.

We have already taken very important and efficient steps towards sustainability and continue to work on making MOSTLY AI the greenest possible version of itself. Here are the steps we have taken so far, and we hope that our efforts will motivate others to follow in our footsteps.

Be part of a community that helps and supports you on your mission.

Working in a startup means that there is a high workload and a lot of things to keep track of on a constant basis. Joining a community that supports you during this mission can make progress way easier and keep you on track with your environmental objectives.

MOSTLY AI joined Leaders for Climate Action (LCFA), an entrepreneurial community that drives efforts to combat climate change. LFCA has created a network of leaders throughout the industry to make the fight against climate change their common goal. So far, over 900 companies have joined, and they have reduced over 360,000 tons of CO2. It goes without saying that these numbers are huge, and it proves that one company alone cannot achieve much, but bringing all of us together can make a real difference.

By joining LFCA, we were able to focus on the initiatives that really matter, have experts in the field support us in our mission, and be a part of a like-minded community of entrepreneurs. If you are reading this article, I assume that you are already passionate about the environment, and I encourage you to put joining this initiative as an action item on the agenda of your next team or management meeting.

You can't manage what you can't measure.

The first step towards becoming carbon neutral is knowing the actual emissions your company produces every year. There are many calculators out there that can help you assess your personal and company-wide carbon footprint, such as the WWF or the Federal Environment Agency calculators. This process is a bit tiresome, but there are a lot of insights you will gain from it. Not only will you consciously evaluate all the past years' bills regarding your environmental impact, but you will also experience eye-opening moments when discovering the largest drivers of emissions. Just to mention one example: an economy flight from Vienna to New York causes emissions of about 4 tons of CO2. To put this in perspective, it is more than double what a family car emits in a year. It is critical to evaluate the factors driving your company's emissions before taking decisive action to reduce them.

Reduce travel whenever possible.

As mentioned above, travel is one of the biggest factors when it comes to CO2 emissions. During the COVID-19 pandemic, travel has pretty much decreased automatically. There are lessons to be learned and questions to be asked when life returns to "normal." One of them is: "Do I have to go to this meeting in person, or can I make a video call instead?" Switching to video conferencing whenever possible is a simple but effective step towards reducing unnecessary emissions.

At MOSTLY AI, our employees are flexible in how and where they work. This means that Zoom and Hangouts are our most important means of communication, particularly during the pandemic. Our workforce is encouraged to use environmentally friendly means of transportation. This includes our 'trains over planes rule,' which means that public transportation is always the preferable choice—unless, of course, you can walk or bike to your meeting.

Traveling by public transport or train whenever possible has a significant impact on our carbon footprint. The same is true for commuting to work. At MOSTLY AI, everyone can work remotely, thus preventing emissions caused by commuting. Nevertheless, if people prefer to work from our centrally located office in Vienna, they usually commute by means of public transport; a yearly ticket is provided by the company.

Change starts within your team.

Your team is the driving force in this process. Ask them to actively participate in making your company greener or, in other words, more environmentally friendly. This can involve transport to the office but also being privately active in reducing their carbon footprints.

With the support of our team, we've made a few changes within our office. One of them was to go completely paperless by introducing electronic signatures. If it's necessary to print, we use 100% recycled paper. Recycled paper is comprised of 100% waste paper and therefore saves both water and resources. Producing it takes two to three times less energy. Moreover, we've introduced a proper waste separation system and ask our employees to minimize waste in the first place. Since half of all plastic products are designed to be used only once, stressing the importance of minimizing plastic waste can lead to a significant drop in the use of plastic (bottles, food delivery packaging). This movement has been extremely smooth and successful. Everybody recognized its importance and was eager to participate.

Take a close look at your current vendors.

As an artificial intelligence and machine-learning tech startup, one must not dismiss the emissions associated with using electricity to train the underlying models. Training neural networks requires high computing power because multiple calculations have to be simultaneously carried out. Therefore, cloud computing and the associated data centers are major environmental concerns. In fact, a single data center can take more power than a medium-size town. Measuring the direct impact of emissions caused by cloud computing is impossible at this point. There is a great lack of data from the respective cloud providers and a huge need for more regulation and environmental controls. Despite this lack of transparency, the big cloud providers are starting to become more environmentally friendly. AWS aims to achieve 100% renewable energy by 2025 and Google Cloud Platform (GCP) is already 100% green. It is up to all of us as startups and big companies to demand green energy and more transparency from service providers.

A first step to achieving this requires looking at your own current service providers. MOSTLY AI switched to a green energy provider that obtains 100% CO2-free green electricity from renewable energy sources. Switching to renewable energy sources can have a significant effect on reducing the CO2 footprint and saving natural resources such as fossil fuels.

Moreover, don't forget to encourage your employees to be energy conscious at work. Remind them to switch off lights, computers, AC, heating, etc. when not in use, and to be active in looking for ways to reduce their personal carbon emissions. If you take things one step further, you can install motion detection lighting systems in office hallways and rooms that are less frequented. Moreover, turning off your camera in video calls could cut energy consumption by 96%.

Offset it with certified partners ONLY.

Climate neutrality is an environmental policy goal that aims to reduce emissions caused by companies through production and consumption so that they don't have any influence on the climate. This is based on the assumption that the climate system can buffer a certain amount of greenhouse gas emissions without having any significant impact. Products, services, or companies can be climate-neutral if, after determining CO2 balance, the emissions are offset.

For the climate, it is not important where greenhouse gases are emitted or avoided as they are distributed evenly in the atmosphere. For this reason, emissions that were caused at one place can be offset by saving at another. Developing countries are often the most affected regions when it comes to CO2 emissions. (Kyoto Protocol) Therefore, carbon offset projects aim to promote sustainable development in the most affected areas in the world. Through forest protection, reforestation, and renewable energy, these projects (among others) significantly reduce carbon emissions and counteract global warming.

Such projects must meet internationally recognized standards such as the Gold Standard or the Verified Carbon Standard (VCS). The Gold Standard is among the leading standards to define the quality of a carbon offset project. If you are looking into offsetting projects, always require one of the above standards to make the most out of your initiative.

To achieve a basic carbon-neutrality, we have addressed our direct carbon emissions with an organization called natureOffice Gmbh. They help reduce carbon emissions via forest ecology projects in Africa and South America in combination with regional forest ecology projects in Austria, Germany, and the Netherlands. While the actual offset of our emissions takes place in Brazil, our local environment is supported through the important work of the mountain forest project in the Montafon. It can be tracked here.

Change starts today.

Having an environmentally responsible business in 2021 leads to healthy, productive, and forward-thinking employees without jeopardizing growth and profitability. Companies like ours are the future, and we believe that by implementing environmentally conscious efforts early on, we can create long-term sustainability.

At MOSTLY AI, we believe in creating a positive impact on the world around us through our products and the people working with and for us. We strongly believe that it is our responsibility to act and promote a sustainable working culture. We believe that it is everyone's responsibility to act consciously and to do the right thing for ourselves, the people around us, and for our planet. Change starts today but doesn't stop there. In the future, we plan to reduce our carbon footprint further by optimizing our business processes to further reduce emissions. Change starts now!

Eager to find out more about MOSTLY AI's carbon reduction measures? Then take action now!

Every team needs a captain, and we just got a pretty cool one. Besides being a seasoned engineer, Kerem is an experienced sailor and captain, bringing unique insights from the sea.

So, what did you learn from sailing?

First and foremost, you need to respect mother nature. You need to know your own and your boat’s limits so that you do not cross the line of bravery into misery. And if you respect nature and know yourself, there is no limit to the joy you can take from the sea.

You need to know the environment you are in (learn the weather forecast, check charts for debris, read the wind, wave, and clouds), make sure that your boat is in the best condition, be always on the watch and take your decisions accordingly.

The sea does not allow you to relax. You have to be alert even on anchor. You need to check if the changing wind will make you lean towards the rocks.

You have to think ahead, be prepared: make sure you have spares for critical equipment, have your Plan B ready, and run it in your head before starting every action. Be innovative enough to create a Plan C on the fly if both Plan A and B fail (and they will).

The boat can only travel fast and safe if the whole crew is in synchronicity. Since the captain is the one accountable for the safety and the course, his job is to make this synchronicity in the crew happen and, at difficult times, takes the difficult decisions.

You can apply each of these rules of the sea in the business world. A good captain has all the traits of a good business leader.

What are you most proud of in your career?

The most obvious answer and the most relevant in our start-up context is the company we founded in 2001 in Turkey. It was a software start-up specializing in CRM and Business Process Management Software. We became the market leader in our domain,  outperforming the “usual suspects.” In the year 2009, we sold the company to the telecom giant Ericsson, and it opened up new frontiers both for me and for everyone in the company.

But if you want to know the real answer, I was the proudest when young colleagues we hired fresh from university grew within our company. When they bought their first car, their first house, when they had their first child.

Why did you decide to join a synthetic data start-up?

I think that you need to experience both start-ups and corporations in your career. I have to say that I learned unbelievably lot during my time in the corporate world. I have experienced three-digit million Euro contracts, projects with hundreds of people, took executive leadership trainings in renowned universities, and most importantly, learned to work in a multicultural environment.

In the midst of these experiences, I always thought, “If I had all this know-how before, in our start-up, we could have done much better!”. These moments made me realize that my passion lies in start-ups. Furthermore, AI was an area I had a personal interest in, and here I am!

What do you wish for in 2021?

My wish for 2021 is to help more and more customers in their data privacy journey, creating the market of our niche with them. This is the prerequisite and enabler for our engineering team to innovate and create.

If you ask me, what my “selfish” wish for 2021 is, I would say that I wish for the pandemic to be over, so we can all meet in our office, work face to face, chat over coffee, and enjoy our perfect location in the heart of Vienna.

Today I am thrilled and proud to announce the launch of MOSTLY AI SaaS. This milestone speaks of our growth as a company, which we wouldn’t have reached without our devoted team of world-class experts and our dedicated customer base.

We achieved yet another first in the industry. It’s 2.5 years ago that we launched the first commercially available AI-powered synthetic data solution for the enterprise. Shortly after, we pioneered a synthetic data solution for behavioral data assets, something that is still unmatched in the industry.

Imagine being able to derive insights from your customers’ bank transactions or their online shopping behavior and create products that genuinely address their needs. With MOSTLY AI's synthetic data platform, it is possible to create synthetic data copies of these behavioral datasets that retain the structure, correlations, and time dependencies of the original data while safeguarding the privacy of your customers.

Bringing AI-powered Synthetic Data to the masses

With our SaaS release, we make these state-of-the-art capabilities available to companies of all sizes and budgets. We significantly evolved our business practices to reach the maturity required to make this leap.

Firstly, we have taken great care to ensure that we are fully GDPR compliant and that your data is safe with us. These measures include the usage of industry best practices, professional frameworks, and security policies. You can find more detailed information here. 

Secondly, your synthetically generated data comes with an automatically created Quality Assurance report. It’s generated by algorithms that test for privacy — is the resulting data dissimilar enough that original subjects cannot be re-identified?, and accuracy — does the structure, correlations, and time dependencies of the original dataset still hold up? 

You don’t need to take our word for it. We open-sourced these privacy and accuracy metric definitions so we can offer the transparency and security that you demand from us. You can find them in this GitHub repository.

Try what it can do for you - securely and free of charge

I invite you to try the free version to experience the power of our synthetic data platform. There's no credit card needed to benefit from the full functionality of MOSTLY AI SaaS. And while you do so, we would love to hear your thoughts and feedback. Please do not hesitate to reach out to us as we are always eager to listen to and learn from our users.

Lastly, I wanted to say a big thank you to everyone on the team who has been working on this release! This is yet another milestone for MOSTLY AI, and I look forward to building out our capacities in the cloud. Expect to hear more from us later this year!

Tobi

Contact us to discover your synthetic data use cases!

I met Michael, Klaudius, and Roland in 2018 and as the conversations got deeper, my desire to become a part of MOSTLY AI grew steadily. In May 2019 I started working for MOSTLY AI, first as an external consultant and soon thereafter as the company’s Chief Operating Officer. The last year has really flown by and it’s impossible to adequately recap the many highlights and accomplishments we have had at MOSTLY AI without going beyond the scope of this post. Let me just share that on a personal level it has been deeply satisfying and impactful. In my career of almost 15 years, I’ve held many different positions and worked for a variety of fantastic organizations. I don’t need to think for a second to say that the year at MOSTLY AI has been the most thrilling and fulfilling professional year for me so far.

My previous roles were always centered around starting new or building and growing businesses. I’ve either done this as a consultant, intrapreneur, or with my own start-ups. It’s great to see how all these experiences have now put me in a sweet spot to help take MOSTLY AI to the next level. We have a multitude of things we are working on including feature upgrades to our core product MOSTLY AI's synthetic data platform and the launch of our SaaS offering which I’m particularly excited about. We can’t wait to share all of this with the world in the months and years ahead.

I look very much forward to working for many more years with the outstanding and growing team at MOSTLY AI, our clients, partners, and investors on shaping the synthetic data revolution together. While times right now are challenging for everyone, it’s amazing to see how well we cope with the situation as a team and company.

Let’s do this together! #gosynthetic

Tobi

PS: I also look forward to writing on this blog more frequently. Watch out for posts about the business side of synthetic data, our company, and all things scaling a deep-tech start-up.