Annual Built In Award Recognizes Virtualitics as One of the 50 Best Startups to Work for in Los Angeles

PASADENA, Calif, February 6, 2024 — Virtualitics, a leading provider of Explainable AI-driven data analytics and applications, today announced that it has received a Built In Best Places To Work Award for the third year in a row. The 2024 Built In award recognizes Virtualitics as one of the best startups to work for in Los Angeles. Each year, Built In honors companies of all sizes, awarding both remote-first employers and companies in large tech markets across the country.

“We’re excited to earn our place on the Built In lists for the third consecutive year,” said Michael Amori, CEO and co-founder of Virtualitics. “It’s an honor for Built In to recognize our commitment and pioneering work in revolutionizing AI-optimized decision-making with Explainable AI. Virtualitics’ role in driving transformative business outcomes extends beyond our organization to our customers around the globe. This recognition is a testament to our collective effort and the impactful changes we’re making in the world of AI.”

Best Places to Work is a U.S. awards program that recognizes companies based on criteria including benefits, compensation, diversity/equity/inclusion (DEI) programs, flexible work opportunities, and other people-first offerings. Built In is visited by millions of technology professionals globally every month to learn about the industry, build connections, and find jobs at companies they believe in.

“I’d like to extend our heartfelt congratulations to the 2024 Best Places to Work winners,” said Maria Christopoulos Katris, Built In Founder and CEO. “I am truly inspired by these companies that have risen to the challenge of fostering a positive work environment, maintaining a strong brand, and ensuring employee satisfaction. The future is filled with promise, and we are so excited to see what lies ahead.”

The Virtualitics AI Platform harnesses the power of Explainable AI (XAI) to empower from business leaders, data scientists to data analysts with advanced analytics and applications for optimized decision making. Through an intuitive user interface (UI), end users can safely and effortlessly engage with complex datasets in a single environment. The platform’s AI autonomously generates multidimensional visualizations, uncovers vital insights, and, crucially, interprets these insights in an understandable manner. This ensures that every discovery is not only shared but also thoroughly understood, enabling the launch of transformative applications that drive critical business decisions across organizations. This approach demystifies data, unifying it in a single platform and turning the AI from a black box into a clear, explainable AI companion in data exploration and decision making.

Are you passionate about advancing AI technology and empowering informed, data-driven choices? Please check out the list of open positions at Virtualitics.

This award follows Virtualitics’ August 2023 announcement of a $37 million Series C investment by Smith Point Capital and inclusion on the Inc. 5000 List of Fastest-Growing Private Companies in America.

About Built In’s Best Places to Work

Built In’s annual Best Places to Work program honors companies with the best total rewards packages across the U.S. and in the following tech hubs: Atlanta, Austin, Boston, Chicago, Colorado, Dallas, Houston, Los Angeles, Miami, New York, San Diego, San Francisco, Seattle and Washington DC. Best Places to Work is distinct because its algorithm selects tech companies that build their offerings specifically around what tech professionals value in a workplace.

About Virtualitics

Virtualitics, a leader in delivering Explainable AI decisions for enterprises and government applications. We enable organizations to make AI-optimized decisions through our advanced applications and unparalleled 3D data visualizations. Built on a decade of Caltech research and proven in government and enterprise spheres, we make artificial intelligence safely accessible, visible, understandable, and actionable across applications for analysts, data scientists and business leaders alike, driving transformative business outcomes. For more, visit

Industry Veterans Join High-Growth AI Leader to Accelerate Go-to-Market Scaling and Expand Strategic Focus on National Security and Governmental Solutions.

Pasadena, CA – Nov. XX, 2023 – Virtualitics, Inc., an AI data analytics company, today announced the appointment of Rob Ferguson as Chief Revenue Officer and Rob Bocek as President, Public Sector.

Ferguson has served in key leadership roles at prominent enterprise software companies including Salesforce, where he spent more than a decade, Oracle, and PTC. Ferguson joins Virtualitics to apply his expertise in leading go-to-market teams responsible for helping many of the world’s largest, most influential companies realize success through the adoption of advanced technologies.

“We are pleased to hire someone with the outstanding strategy and execution experience that Rob Ferguson brings to the table,” said Michael Amori, CEO and co-founder of Virtualitics. “Rob will help Virtualitics accelerate our commercial business and build on the strong foundation of success we have already established in the federal government sector.”

“It’s an incredible time to join Virtualitics,” said Ferguson. “The Virtualitics platform is proven in the most demanding, mission-critical environments. I welcome the opportunity to work closely with customers to help them leverage our ongoing innovation in AI data analytics to achieve quantifiable, high-impact results.”

Bocek has more than 15 years of experience leading teams in emergent defense, intelligence technologies, and enterprise software, bringing a wealth of expertise to Virtualitics. In his new role, Rob will spearhead the company’s initiatives within the U.S. defense and public sector domains, driving strategic business growth and expanding Virtualitics’ footprint in crucial government sectors.

“I’m excited to welcome Rob Bocek as our new president of public sector,” said Amori. “Rob has a deep understanding of our federal clients and has demonstrated excellence in all aspects of his career, beginning with his military service as a Navy SEAL officer to his tenure at Microsoft Federal, and later on, working at multiple defense-focused startups. Like everyone at Virtualitics, Rob is passionate about our mission. His expertise will be pivotal in strengthening our partnerships with government agencies and in furthering our commitment to providing AI-driven solutions for national security and public sector challenges.”

“I’m thrilled to join the tremendous team at Virtualitics, especially at such a crucial time where mission-AI technologies are revolutionizing how leaders are making better decisions with more accurate data,” said Bocek. “Virtualitics’ AI-driven data exploration, visualization and applications will transform the way national defenders, public safety personnel, and first responders assess threats, mitigate risk, and keep us safe.”

About Virtualitics

Virtualitics, Inc., an AI data analytics company, is pioneering the power of AI- and machine learning-guided data exploration to transform organizations. The Virtualitics AI Platform is an advanced analytics solution empowering everyone with faster, ready-to-use AI that is easily understood by analysts and business leaders. The company’s patented technology is based on more than 10 years of research at the California Institute of Technology and has been tested, proven, and leveraged by the federal government and large enterprises.

The rise of generative AI has opened up new ways for enhanced human-machine collaboration and groundbreaking discoveries. With its power to create new content starting from a prompt, such as images, texts, audio, video, or even other types of data, generative AI has the potential to revolutionize data analysis in many diverse fields.

However, despite being lauded as one of the most beneficial technological advancements in history, AI still hasn’t been widely adopted by organizations. There are three main reasons for this:

Resources: One of the primary barriers to AI adoption is the scarcity of expert users. Data scientists have become increasingly in demand as organizations seek to pull meaningful and actionable insights from the millions of bytes of data they produce each day. However, the data science talent pool is small and even when they do get hired, they’re simply juggling too many competing projects to focus on AI adoption.

Trust: While some still fear the doomsday potential of AI, what’s actually hurting trust in AI is confidence in the results it generates. Adding to that distrust is a lack of knowledge, even at a high level, of the proposed solutions and their implications. Hallucinations, for example, occur when generative AI bots return made-up information, but this issue usually stems from an incomplete query or inaccurate dataset. Without the right data science talent to train the model and help encourage AI adoption, it’s challenging to build up trust in the technology.

Usability: Many existing AI tools are challenging to use, typically requiring specialized knowledge and expertise in data science. Increased adoption requires user-friendly tools that analysts and other non-data science experts can leverage to gain a genuine understanding of complex, multidimensional, and heterogeneous data.

Democratizing AI with Intelligent Data Exploration

It’s clear that the barriers to widespread AI adoption ultimately stem from the scarcity of data science experts. Waiting to hire for these skills risks companies being left behind in the race to analyze enterprise data for business-changing insights. Fortunately, addressing these problems can be achieved through Intelligent Exploration, Explainable AI (XAI), and Large Language Models (LLMs). 

An intelligent data exploration platform, such as the one patented by Virtualitics, leverages XAI, Generative AI, and rich visualizations to guide users through the analysis of complex datasets. Low-code or no-code environments that allow users to log in and immediately begin exploring their data for insights are crucial to the adoption of AI inside the organization. Generative AI technology can be used to:

  • Use embedded AI routines to generate multidimensional visualizations based on available data and contextual information.
  • Deliver key insights in natural language augmented with compelling, AI-generated visualizations.
  • Use LLMs to suggest the next steps in the analysis based on user prompts. These prompts can be specific (“I want to understand what drives sales in summer”) or more open-ended (“Tell me something interesting about my data”).

Intelligent exploration leads teams to findings that can be clearly understood, prioritized, and acted upon.

Enabling a Conversational Approach to Data Analysis

Two of the keys to democratizing complex data analysis with Intelligent Exploration are the use of XAI and LLMs.

To be effective, XAI has to strike the right balance between model interpretability and accuracy. It is essential that we do not compromise accuracy while focusing on context-aware explanations, which entails designing explanations that consider the specific context of the analysis conducted.

Additionally, XAI systems must generate explanations suitable for different audiences that don’t require a data expert to interpret them. A few years ago, advancements in Natural Language Processing (NLP) introduced the capability to ask queries using natural language, but these systems faced limitations due to their restricted vocabulary and ad hoc syntax.

LLMs overcome these limitations by presenting results in the form of a narrative, featuring simple language and relevant charts that are generated automatically.

The AI-Friendly Future

Intelligent data exploration not only enables organizations to take advantage of their data without waiting to hire more data scientists, it also plays a critical role in augmenting trust in AI solutions, fostering a more widespread, fair, and ethical use of AI. This increased trust will contribute to more responsible deployment and wider acceptance of AI solutions across various domains, positively impacting society as a whole.

By enhancing resource allocation, providing transparent and interpretable AI solutions, and developing user-friendly tools, intelligent exploration platforms like Virtualitics pave the way for wider AI adoption and for all organizations to reap the benefits of these transformative technologies. By leveraging XAI, Generative AI, and rich visualizations to guide users through the analysis of complex datasets, Virtualitics is creating a future where teams are prepared to make AI-guided data analysis not only attainable but a powerful ally.

About the author

Ciro Donalek is a leading expert in Artificial Intelligence and data visualization. As a Computational Staff Scientist at Caltech he successfully applied Machine Learning techniques to many different scientific fields, co-authoring over a hundred publications featured in major journals (e.g., Nature, Neural Networks, IEEE Big Data, Bioinformatics). He holds several patents in the fields of AI and 3D Data Visualization, co-authoring the ones that define the Virtualitics AI platform.

Dr. Donalek is passionate about teaching and public outreach and has given many invited talks around the world on Machine Learning, Immersive technologies and Ethical, Interpretable and Explainable AI. He holds a Ph.D. in Computational Sciences / Artificial Intelligence (University Federico II of Naples, Italy) and an MS in Computer Science (University of Salerno, Italy). He is married with two children.

Virtualitics Adds Former Intelligence Leader, Reinforcing Commitment to Serving the US Department of Defense and Intelligence Community.

Pasadena, CA – November, 2023 – Virtualitics, Inc., an Artificial Intelligence and Data Exploration company, today announced the appointment of Cynthia “Didi” Rapp, a renowned former national intelligence executive, to the company’s Board of Advisors. 

Virtualitics harnesses an AI platform to transform data science into actionable intelligence, enhancing operational readiness, optimizing investments, and bolstering mission support for the federal government. This strategic appointment reinforces Virtualitics’ dedication to supporting the vital work of the US Department of Defense and the Intelligence Community.
Rapp brings to Virtualitics a wealth of knowledge and experience with decades of dedicated public service, predominantly with the Central Intelligence Agency (CIA). Beginning her career as a geographic analyst, Rapp’s expansive expertise spans intelligence issues from the Cold War and counterterrorism to the intricacies of the multipolar world and renewed Russian aggression.

A distinguished leader with deep management acumen, Rapp has held various pivotal roles in her career. Notably, she managed the content and senior-level review of the President’s Daily Brief (PDB), the Intelligence Community’s premier analytic product. She oversaw the CIA’s Middle East analytic program, progressing through numerous executive positions, including the Deputy Chief Operating Officer responsible for the agency’s day-to-day operations. In her final analytic tenure, she served as the Director for Analysis, responsible for the quality of all-source analysis and leading the entire analytic cadre. Rapp concluded her CIA tenure serving as the Chief of Staff for the agency’s first female director.
“Virtualitics is immensely honored to have Ms. Rapp join our advisory board,” said Michael Amori, Virtualitics CEO and co-founder. “Her unparalleled perspective and vast experience in intelligence analysis align with our mission to deliver cutting-edge AI solutions. As we continue to expand our work with the Department of Defense and the Intelligence Community, Ms. Rapp’s guidance will be invaluable.”
Rapp has received numerous accolades throughout her long career, including two Presidential Rank awards and the Director’s Award for Distinguished Service. Additionally, she has been honored with the Distinguished Career Intelligence and National Intelligence Exceptional Achievement medals.
“I am honored to join the Virtualitics board of advisors at this exciting time in the company’s next phase of growth,” said Cynthia Rapp. “Having witnessed firsthand the transformative power of artificial intelligence and data exploration, I’m deeply inspired by Virtualitics’ commitment to illuminating complex datasets for the defense and intelligence sectors. With my decades of service and their innovative and explainable approach to AI, we’re poised to shape AI-driven analytics, ensuring actionable insights to safeguard our nation.”

Virtualitics combines AI and data exploration to enhance US defense and intelligence operations. Since 2017, Virtualitics has collaborated with key defense institutions on projects from operational readiness to intelligence analysis. The company’s dedication to AI-driven analytics has not only garnered the trust of government agencies but has also inspired two esteemed former military leaders to join their federal advisory board. 
Virtualitics recently raised a $37 million Series C investment from Smith Point Capital and was named to the Inc. 5000 List of Fastest-Growing Private Companies in America list.

Furnishing a home can be a daunting task, especially if you’re living in a place with a few funny angles and oddly shaped nooks. IKEA, the furniture retailer known for their DIY kits, can provide you with some great easy-to-assemble pieces to fill your space, but for unique layouts, prefab furniture isn’t always going to be a perfect fit. These are the times when bringing in a custom or niche-focused solution delivers the perfect fit. When you finally have all your furniture, the result will be a blend of unique and off-the-shelf pieces that all work beautifully together. Similarly, every organization functions better when they have the right mix of IKEA-like DIY data analytics tools, such as self-service BI software, and custom solutions like AI-guided analytics that are capable of exploring complex data and discovering insight hiding in unusual places.

Data exploration requires more than one tool

The applications used every day to run businesses create and capture thousands of data points every second. As a result, there is a deep treasure trove of information buried in these systems…but not a lot of resources or skills to analyze it all.

Fortunately, there has been a ton of innovation in the BI technology space, making it easier for data consumers to now create their own reports and dashboards. This means they can get answers to some of their recurring questions without waiting for an inundated data scientist or analyst to find space in their project queue. In other words, they’ve now got their very own IKEA of business analytics at their fingertips.

What’s also great about self-serve analytics is that it allows consumers to create their own reports within the boundaries set by experts. When data analysts are freed from creating and maintaining BI dashboards and spreadsheets for data consumers, they’re able to use their time and skills towards putting the correct guardrails in the self-serve software. This will minimize problems that come from using the wrong data, but it does limit the scope of inquiry…and that means some insights go unseen.

This leads us to the custom solution that complements self-serve data work: AI-guided analytics. Platforms like Virtualitics give analysts the ability to dive deeper into data and find insights that will set your business apart. Deep exploration of complex data does require advanced analytic skills, but by leveraging AI-powered Intelligent Exploration solutions, data analysts can become stronger strategic advisors.

3 benefits of AI-guided data exploration

Some influencers believe the data analyst role will be made extinct by self-service analytics…but it’s not. A house decked out entirely in IKEA furniture may function, but it will still have those odd nooks and crannies that require a different solution to reach the home’s full potential.

This is why organizations that reduce their analyst teams in the sole pursuit of analytic solutions that are using AI to facilitate self-service reports and dashboards risk going backward. Not just because the consequences get real when consumers use the wrong data for business decisions, but also because you’ll miss strategic opportunities if your analysts aren’t empowered to go searching for big business-changing insight.

Opportunities like improved reporting, strategic decisions, and accurate root cause analyses.

1. Improved reports

Sorting through all the data to find important signals requires skills and tools that are beyond the reach of the typical data analyst. This leads to reports that are lacking in valuable information, and without the right solution, an analyst doesn’t know to look for the missing insight. With Intelligent Exploration platforms, AI does a lot of the heavy lifting of sorting through wide and complex datasets. Virtualitics uses machine learning to instantly pull out the features from your data that are driving results and impacting success.

This can be a game-changing capability for organizations that rely on equipment to stay operational. Reports that help identify weakening machinery before it breaks or fails, while also keeping users aware of resource constraints and inventory, are key to minimizing downtime.

2. Strategic decisions

Multidimensional data often goes untapped because analysts can’t explore it and data science teams don’t have the bandwidth to use code to find the information and attempt to apply a visualization that would adequately communicate it. Virtualitics makes it easy to visualize and compare all this complex data and also automatically generates insights from it for analysts.

Imagine a luggage retailer wants to improve its target marketing in the APAC region. It can be difficult to know where to begin to make sense of the data they have, but Virtualitics can guide a data analyst to an insight that shows a relationship between the time of day and the size of orders in this region. This leads to strategic timing to send out email offers and potentially triggers bigger orders as a result.

3. Accurate root cause analyses

Knowing where to prioritize resources can be incredibly difficult, especially when your retail operations, for example, are spread out across many different locations. Virtualitics’s exploratory environment enables analysts to do deep analysis on complex, interrelated data sets like this and use natural language to ask questions that will guide them closer to the answer. 

For example, instead of constructing a series of customer segmentation analyses trying to get to the key factors that drive sales (Is it staffing? How about inventory volume? Does store square footage make a difference?) analysts can simply ask “What’s driving sales?” Virtualitics will evaluate the entire width of the dataset and rank each feature’s importance in driving sales and generate a visualization that illustrates the top three. This enables analysts to not only see which features are truly behind sales but also which ones work in concert.  

Blended data analysis is better

Differentiation is key in home design and in business. Sometimes it means doing something drastically different and sometimes it means a more nuanced take on an old problem. An analyst empowered with both self-service analytics and an Intelligent Exploration platform will gain the bandwidth and capabilities to deck your organization out with insights that will propel your business forward. 


The answers to business problems, large and small, are there for the taking—right in your organization’s data. Yet, the quantity and types of data available for analysis have outpaced the tools most organizations have been using.

A survey found that 85% of companies are using inadequate tools to explore complex data sets. Furthermore, nearly two-thirds of data science leaders surveyed say that data exploration is held back by a lack of data science skills.

While data scientists have been employing piecemeal AI techniques to wrangle their complex data for years, it’s only recently that AI innovation has resulted in technology that brings sophisticated data analysis within reach of analysts.

Explore, Define, and Solve your business challenges with AI

For too long sorting through data to find useful, unseen insights has required skills and tools that are beyond the reach of the typical data analyst. But with data scientists often tasked with prioritizing more complex and larger data projects, it’s become increasingly important for analysts to become fluent in data exploration.

Advanced analytics platforms like Virtualitics use embedded no-code AI to empower data analysts to:

  • Explore and make sense of large, complex datasets
  • Interpret findings in plain language
  • Produce rich visualizations that clearly illustrate complex stories in interconnected data

When analysts begin this deeper analytics journey, it’s helpful to have a map leading the way. Enter the Explore-Define-Solve framework.

Coupled with Virtualitics’ advanced analytic technology, this best practice framework enables data analysts to discover and validate value-add data projects and work with the business to guide those projects to completion. It keeps analysts from being stuck churning out dashboards, allowing them to extend their reach beyond their own teams and make data a consistent part of the overall strategy.

The 3-step framework in action: Cybersecurity

The Explore-Define-Solve framework starts with AI-powered data exploration and guides analysts on how to follow surfaced insights through to actionable, feasible solutions. Each phase of the framework has steps designed to ensure that projects are aligned with business goals and will deliver value.

Let’s look at how a cybersecurity analyst can use the framework while working in Virtualitics to uncover important insights.

1. Explore insider risk identification

Around 90% of all security breaches originate from phishing scams, but it’s impossible to stop every malicious email. What is possible is to use AI to detect actual threats among the billions of signals of normal activity. Or to uncover the attributes that characterize employee vulnerability to phishing.

To explore the latter, an analyst could ask the system to sort and visualize communities that exist within the population of stakeholders and employees, communities that were previously invisible. They could then ask for statistical insights to determine if any communities have a significantly higher likelihood of falling for a phishing email.

Once it’s possible to proactively identify those who might click on a malicious link, the next step is to define potential strategies for stopping breaches before they happen—more training, banner alerts, or information security changes, for instance.

2. Define what’s behind phishing risks

This stage of advanced data analytics involves working alongside SMEs with the domain expertise to pressure test findings.

The data analyst in our example goes to the compliance and information security teams to validate the predictive value of the communities that exist within the company’s datasets, increasing prediction accuracy. Confirming that the data can be leveraged to predict which employees are at risk of phishing attacks means that, with the right solution, attention can be focused on actual threats. The cybersecurity team can attend to the right priorities instead of spreading themselves too thin on false positives.

3. Solve for insider threats

To solve their insider risk challenge, business decision-makers now have the opportunity to select the best ways to work with the data developed. They could look at the following:

  • Should we build a model that generates a list of employees and stakeholders we predict will fail phishing attempts?
  • If yes, do we run it daily, weekly, monthly?
  • What do we do with that list? Automatically register those people for anti-phishing training? Provide personal outreach to those who are flagged?

As with all strategic initiatives, deciding on focused solutions to stop cybersecurity threats is a combination of data-based conclusions and business-based judgment.

Begin Your Intelligent Exploration Journey

Using the Explore-Define-Solve approach empowers analysts to navigate through massive, complex data sets to focus solely on insight that drives results and contributes to better decisions. 

And when a new AI project proposal emerges as a result of analyses, decision-makers can have confidence it’s the right solution. It’s emerged organically from data, in full collaboration with those on the business side, ensuring there’s value in pursuing it.

With empowered analysts, companies can identify and predict trends, uncover new opportunities and cost savings, quickly respond to market changes, and pursue the most promising innovations. Read more in our free ebook about solving business challenges with data and AI.

Early this month I moderated the panel “The Implications and Opportunities of Generative AI in FS” at Corinium’s CDAO event in Boston with David Dietrich (VP, Advanced Analytics and Governance at Fidelity Investments) and Jake Katz (Head of RMBS Research and Data Science at the London Stock Exchange Group). This was a lively discussion with a really engaged audience and it really highlighted for me the squeeze that Data and Analytics leaders are facing right now between their business leaders’ demand to hop on the GenAI train and finding a practical application for it. Data science insiders have known about GenAI for a while but the launch of ChatGPT at the end of 2022 brought awareness of it into the public consciousness, including that of senior management. Where before AI seemed ephemeral and complicated, ChatGPT made it tangible and easy. It also made AI seem a little bit like magic. As David noted, this led leaders to demand this technology, dedicating significant resources to integrate it into a broad set of applications.

But do leaders really understand how GenAI and large language models (LLMs) work and what they’re asking for?

The consensus from the audience was a resounding ‘No’. It’s tempting to shrug at this situation–this is just the latest in a long line of new technologies that seem to get everyone excited and distracted. No doubt the hype will settle down, right? Indeed, CCS Insight predicts that this is exactly what will happen in 2024 as the cost to deploy GenAI and LLMs safely and responsibly overshadows the value of the realistic applications of the technology in many situations.

Are there Generative AI Applications in Financial Services?

Does this mean that GenAI has no potential use cases in FinServ? Not at all. It’s proving its mettle with use cases in customer support, content generation, and even coming up with potential business ideas. These are all areas that offer a lot of efficiency gains and are worth exploring. But that still leaves a lot of the business that’s not currently seeing gains. And this leads me to my next point. 

What’s happening to all the other data-based initiatives and AI use cases while resources are diverted to GenAI? They’re stalling, and they were already struggling (a report says that only 53% of projects were seeing results). 

I could see in the room the frustration with an audience pressured to take away their attention from problems that could be solved with applications of other, more practical forms of AI. Managing up is never easy, but I think senior leaders need to hear that GenAI, while exciting, is not the answer to every business challenge. But CDAOs have good ideas that could be valuable ideas, and it’s time to turn their attention back to solutions that make sense.

Looking for more resources to help your team find targeted solutions? Check out our free e-books!

Even with more data available than ever before, organizations are struggling to formulate, scope, and execute valuable, data-based projects. Not a great return on investment for all that data gathering and storage. But you already have employees who can help wade through that data to valuable insight, if you arm them with the right tools: your data analysts.

When data analysts struggle to explore data then entire organizations are held back. Data-based projects don’t deliver the desired results, because the scope of data exploration doesn’t allow analysts to discover the right opportunities. Why does this happen? Because the analytic tools they are using are not designed to allow and assist with intelligent data exploration.

Caitlin Bigsby, Head of Product Marketing at Virtualitics, and Joseph Oliveria, Solutions Engineer, hosted a webinar this week about how data analysts can expand their skills and become the driving force for valuable, data-led strategies in their companies. By unlocking AI-assisted data exploration techniques, your analysts can bridge the gap between “death by dashboard” and true Intelligent Exploration.

Wondering what the data analyst of the future looks like…and what can they discover in your data? Watch the webinar and find out! 

Pasadena, CA – October 11, 2023 – Virtualitics, Inc., an artificial intelligence and data exploration company, today announced its partnership with Databricks, a Data Lakehouse Architecture and AI Company. As a Databricks Technology Partner, Virtualitics will enable Databricks customers to unlock the full potential of their data with AI-powered exploration and gain valuable insights to drive business success.

With the exponential growth of data and the need to conduct increasingly complex analytics, organizations are seeking innovative solutions to extract actionable intelligence from their data. The Virtualitics AI Platform uses Intelligent Exploration—AI-powered analytics coupled with immersive data visualizations—to enable users to intuitively explore and understand complex datasets, transforming raw data into meaningful insights.

As a Databricks Technology Partner, Databricks allows users to access the Virtualitics AI Platform and begin exploring their data through a reliable and secure connection to Databricks’ Lakehouse Platform.

“In today’s data-driven world, organizations have invested significantly in collecting, organizing, and managing their data within the Databricks Lake House,” said Michael Amori, CEO and co-founder of Virtualitics. “However, the true challenge lies in making sense of this wealth of information and extracting actionable insight. Intelligent Exploration is the key that unlocks the untapped potential within these vast data lakes.”

 Virtualitics’ partnership with Databricks equips analysts with advanced data science techniques to quickly reveal valuable data connections and hidden insights, uncovering new opportunities and deeper business understanding.

Joint customers can easily analyze and understand complex data in the cloud, including:

  • AI-Driven Exploration​: Virtualitics’ Intelligent Exploration uses AI to analyze rich, multi-dimensional data and quickly finds the patterns in data.
  • Intelligent Network Graph Analysis​:​ Patented machine learning (ML) technology detects relationships and generates network graphs in 3D to explore communities and their connections, without having to set up a graph database.
  • Multi-Dimensional Data Analysis​: Virtualitics brings complex data stories to life with dynamic 3D visualizations designed to clearly illustrate multiple relationships and facilitate exploration from every angle.

For more information about Virtualitics’ partnership with Databricks, visit

This partnership follows Virtualitics’ August announcement of a $37 million Series C investment by Smith Point Capital and inclusion on the Inc. 5000 List of Fastest-Growing Private Companies in America.

About Virtualitics

Virtualitics, Inc., the Intelligent Exploration company, is pioneering the power of AI- and machine learning-guided data exploration to transform organizations. The Virtualitics AI Platform is an advanced analytics solution empowering everyone with faster, ready-to-use AI that is easily understood by analysts and business leaders. The company’s patented technology is based on more than 10 years of research at the California Institute of Technology and has been tested, proven, and leveraged by the federal government and large enterprises. For more information visit

Trade execution slippage can cause major problems for financial traders and investors ranging from reduced profits to a loss of confidence and portfolio damage. Repeated slippage is even worse, encouraging traders to abandon brokers or leave the market altogether. If financial institutions want to build resilient portfolios and increase confidence in their performance it’s critical that they use their data to help combat slippage.

What is Trade Execution Slippage?  

From a mathematical perspective, slippage is fairly simple: it’s the difference between the expected price of a trade and the actual price at which it is executed. It can occur in any financial market, but it is most common in volatile markets where prices are changing rapidly. 

While the math is straightforward, there are a lot of different reasons why slippage can happen:

  • Market volatility: When prices are moving quickly, it can be difficult for brokers to execute trades at the desired price.
  • Liquidity: If there is low liquidity in a market, it may be difficult to find a buyer or seller at the desired price.
  • Order type: Market orders are typically executed at the best available price, but this leaves room for slippage.
  • Brokerage fees: Brokerage fees can also contribute to slippage, especially for small trades.

Slippage can have a significant impact on trading profits and losses and it can add up over time, especially if you are trading large volumes. Because financial institutions typically manage a huge quantity of trades, minimizing slippage in trading can create a huge competitive advantage.

Minimizing Slippage through Advanced Data Analytics

There are some best practices that traders can use to minimize slippage, such as using limit orders, focusing trading in liquid markets, and avoiding trades during periods of high volatility. But in reality, slippage is a normal part of trading.

The good news is that better data analytics can also help minimize slippage. By analyzing historical data, traders can identify patterns and trends that can help them predict future price movements. This information can then be used to place more informed trades and reduce the risk of slippage.

The bad news is that analyzing the relevant data can be incredibly challenging and time-consuming. The datasets have the potential to be massive, both from the number of trades executed and the number of variables included to be analyzed. In our experience, it’s not uncommon for datasets to include between 300 and 400 variables in every trade. To really be able to use data to mitigate slippage, you must have a tool robust enough to handle the volume of data and with built-in guidance to help you find the right insight.

Gaining Traction Against Slippage Through Intelligent Exploration

“Due to the complexity of the data, finding significant patterns can be challenging and requires an understanding of how to apply advanced machine learning techniques” shares Yong Kim, Virtualitics Head of Solutions. “Virtualitics AI-guided Intelligent Exploration allows analysts without deep data science experience to quickly uncover patterns that could usually take hours or days.”

Financial analysts leading the way with Intelligent Exploration in the Virtualitics AI Platform can deliver more value and identify key trends, such as the times of day when a particular stock is most volatile. They can then recommend that their organization avoid trading during these times to reduce the risk of slippage. Intelligent Exploration can also identify the order types that are most likely to experience slippage. Teams can then avoid using these order types, or use them more strategically, to minimize the impact of slippage.

In addition to helping traders make more informed decisions, data analytics can also be used to develop and implement trading systems that are designed to reduce slippage. For example, a trader may develop a trading system that uses algorithms to automatically place trades at the best available prices. Or, a trader may use data analytics to develop a trading system that uses hedging strategies to reduce the risk of slippage.

Intelligent Exploration can also discover:

  • Patterns and trends in market data: Data analytics can be used to identify patterns and trends in market data, such as price movements, order volume, and liquidity. This information can then be used to predict future price movements and place more informed trades.
  • Backtesting trading strategies: Data analytics can be used to backtest trading strategies on historical data. This allows traders to see how their strategies would have performed in the past and identify any potential areas for improvement.
  • Trading performance: Data analytics can be used to monitor trading performance and identify areas where slippage is occurring. This information can then be used to adjust trading strategies and reduce the impact of slippage.

Overall, the right data analytics tools can be a powerful ally in reducing slippage and protecting the profit and reputation of financial institutions. Learn more about how Virtualitics helps financial users in our free Intelligent Exploration for Financial Services e-book.