We are excited to announce our investment in Agnostiq’s seed round. Based in Toronto, ON, Agnostiq Labs (“Agnostiq”) is developing a suite of applications that seamlessly enable financial institutions (FIs) to leverage QC to power their trading algorithms, all without the need of expensive quantum programming resources. Agnostiq’s applications will allow banks to securely transpile, optimize, and deploy quantum machine learning (QML), quantum Monte Carlo and quantum neural networks in portfolio optimization models. This breakthrough will lead to development of new algorithms at a faster rate and better market predictions.

Financial institutions (FIs) have realized that classical computing is approaching the limit of Moore’s law and there is a gap between the amount of new unstructured data coming in and their ability to process that data to make better decisions. As such, FIs and other organizations are looking to quantum computing (QC) to resolve these issues and deliver the high performance computing they need to maintain their edge. Whether developing the better algorithm to price options or conducting trades in one 64 millionth of a second to capitalize on the arbitrage opportunity, FIs need the compute power to analyze hundreds of millions of data points in real-time to execute their strategies.

While initially focused on banks, we see tremendous opportunity to apply their unique quantum AI workflows and best-in-class security applications across several industries, such as pharmaceuticals, healthcare, and energy. Additionally, we believe Agnostiq can become the platform-of-choice that accelerates enterprise adoption of QC and transforms data scientists into quantum scientists.

We are incredibly impressed with the management team and talent. Collectively, the founders Oktay Goktas, PhD,  and Elliott MacGowan possess the unique combination of deep technical knowledge in QC, sales acumen and operational expertise needed to succeed in this nascent industry. Agnostiq has also proven its ability to attract top tier QC talent with backgrounds in quantum physics, cryptology, mathematics, and computational physics. Given the dearth of available and qualified talent, Agnostiq’s ability to attract, hire, and retain top scientists will solidify its market position and competitive moat.

We believe in Oktay, Elliott, and Edwin’s collective vision on quantum computing and are excited to partner with them in their quest to help enterprises harness the power of AI in quantum computing!

Today companies are driving workforce productivity using 3rd party SaaS applications like Salesforce, Jira, Dropbox which form the backbone of critical functions across the organization. According to a 2020 Devsquad study, companies deploy, on average, 34 different SaaS tools, and that number increases dramatically with company size (reference 1 below).

Company tech stacks are also becoming increasingly decentralized and thus harder to manage. Communication tools such as Slack and Microsoft Teams have made it easier than ever to collaborate across teams, but have also led to sensitive information moving unchecked across the enterprise, increasing the risk of data loss and leakage. Last year, over 165 million records were either lost or exposed within the US alone (reference 2 below), all the while increased regulation (e.g., GDPR and the California Consumer Privacy Act) has levied significant fines and put additional pressure on businesses to revamp their information security and data loss prevention (DLP) strategies.

As organizations continue to grapple with this changing landscape, we are thrilled to announce our investment in Polymer Solutions’ (“Polymer”) seed round. Cyber security is one of our key investment areas, and continues to be important as more work moves to the cloud. We are proud to support Polymer’s vision to become the preeminent platform that manages DLP and redaction across the enterprise technology stack. While most of the DLP market has focused on encryption-based solutions for at-rest data, Polymer is unique in its approach by applying Natural Language Processing (NLP) on unstructured, in-motion data to redact Personal Identifiable Information (PII) and sensitive corporate information. This solution will be critical to companies that collect sensitive customer information, such as financial or health data.

Polymer provides an easy-to-use solution that allows enterprise Information Technology departments to create access controls as well as monitor, secure and redact sensitive data across dozens of collaboration apps, including Slack, Github, Dropbox and Zapier. Polymer’s product has already gained significant traction with several large enterprise clients.

Polymer was founded by Yasir Ali and Usman Malik, who are supported by a strong technical team of cyber security, big data and machine learning experts who are excited and well-positioned to tackle this complex data redaction problem. We are thrilled to partner and support Yasir, Usman, and the Polymer team as they continue to expand their data governance platform and grow into a leading security company!

1 ”60 SaaS Statistics and Trends for 2020”, Industry study via Devsquad, 2020
2 “Annual number of data breaches and exposed records in the United States from 2005 to 2019,” Identity Theft Resource Center, 2019

Today, businesses are generating a massive amount of data every second across hundreds of disparate data sources.  As organizations attempt to capitalize on new ML/AI capabilities, each new system or tool disaggregates the journey from data to business value even further, creating costly, slow, and labor-intensive data preparation. The situation creates challenges for the data engineering teams to keep up with business and technical demands.

In machine learning, a "feature" is an input variable, similar to the explanatory ("x") variable in simple linear regression. A machine learning project might use hundreds or even millions of features, and each feature must be paired with labels (similar to the dependent "y" variable) to train a model. A robust and scalable ML/AI development program requires improving the feature extraction process, an early step in ML that prepares data for analysis by abstracting complex schemas and their data into basic objects and attributes.

Relying on reference architectures for feature re-use can help, but this introduces issues of latency, complexity, and another data silo to be managed. To simplify and speed up feature extraction and re-use, a new technology has arisen, called feature stores, which assists with the demanding data preparation required for effective ML. No longer unique to the capabilities of large firms like Uber and Airbnb, feature stores can transform raw data into feature values, store those features, and serve those features for training and analysis in the future.

Importantly, feature stores do not remove the need for Snowflake or similar cloud solutions. Feature stores easily overlay with existing data infrastructures, enabling almost instantaneous updates, reducing risk, and scaling quickly with an enterprise. The graphic below compares feature stores to current approaches.

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In short, all of an organization’s data can be converted to reusable features and analyzed with full fidelity, regardless of format or source location, for immediate analytics. Not only does this speed up a data scientist's development timeline, but it brings economies of scale to ML organizations by enabling collaboration. When a feature is registered in a feature store, it becomes available for immediate reuse by other models across the organization.

One example of a leading provider of feature store capabilities is Molecula, who recently completed a $17.6 million Series A round with Tensility as a participant. Molecula leaves data at its source and continuously extracts and updates only features into a centralized feature store. This process eliminates the need to copy, move, or pre-aggregate data, reduces the data footprint by 60-90 percent, and provides a secure data format for sharing.

Whether through Molecula or another organization, feature stores pair with current systems to enable prescriptive analytics while reducing complexity, costs, and risk. We are excited at the capability they provide to data engineers and data scientists to improve the data pipeline and leverage all of a company's data for better business outcomes.

Tensility is proud to announce the acquisition of Stabilitas, a Tensility Fund II investment, by OnSolve. This is an exciting exit for us, and we are thrilled to see a team led by two veterans, Greg Adams and Chris Hurst, navigate the entrepreneurial cycle to build Stabilitas from zero to a successful exit.

Now more than ever, the ability to understand a situation and its impacts in real-time, rather than after the fact, is a key skill for businesses around the world. However, doing so in today’s data-rich and resource-constrained world requires a force multiplier, such as artificial intelligence, to enable humans to quickly analyze and act on insights from the countless data points generated every moment of every day. The value of such a capability is clearly recognized by OnSolve, a leader in multi-modal critical event management, as evidenced by its acquisition of Stabilitas Situational Awareness platform. Stabilitas created a product that combs over 17,000 data sources to correlate critical events with key enterprise assets and will bolster OnSolve’s Critical Event Intelligence capabilities.

With just under a year since our initial engagement, this is one of Tensility’s shorter investments, however, we have thoroughly enjoyed the opportunity to work with Greg, Chris, and the rest of the Stabilitas team. It has been a pleasure to see the team grow its offerings and partnerships over the course of our relationship. From deepening Stabilitas' relationship with G4S – the largest global, integrated security company – to understanding the societal impacts of COVID-19 and sheltering at home, it has certainly been a productive year for Stabilitas and we wish them all the best as they become part of the OnSolve team.
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To learn more, read the press release from OnSolve, check out our previous announcement of our investment in Stabilitas, or visit the Stabilitas website.

The top 50 Quick Service Restaurant (QSR) brands in the U.S. generate over $200 billion in sales annually, with $150 billion coming from their 14 billion drive-through and phone-in orders per year. However, the industry has been facing several challenges for continued revenue growth, such as rising wages for service agents, increased wait time for drive-throughs, inaccurate orders due to human errors, and missed upsell opportunities. In 2019, McDonald’s acquired Dynamic Yield and Apprente to make ordering faster and more accurate by using conversational AI, a speech-based digital assistant that automates communication and delivers personalized customer experience at scale. These acquisitions put additional pressure and urgency on other QSR brands to improve their operation and service experience.
 
We are excited to announce Tensility’s investment in ConverseNow’s $3 million seed round. ConverseNow develops conversational AI software to automate drive-through and phone ordering for QSRs. By deploying ConverseNow’s automatic speech recognition and natural language processing technologies, restaurants can now simplify its operations and increase its revenue through data-driven, personalized upselling. We see several key advantages in ConverseNow’s approach to the market compared to its competitors: First, the company is highly focused on serving QSRs and improving their workflows, as opposed to offering a one-size-fit-all solution. Second, ConverseNow closely partners with key vendors in the QSR ecosystem, such as providers for point of sale systems and self-service kiosks and is therefore able to effectively reach customers. Lastly, ConverseNow follows a low risk, step-change rollout - by allowing customers to deploy an AI-assist solution before implementing a fully-autonomous AI - which helps accelerate adoption.

The company was founded in Austin, Texas in 2018. The founders, Vinay Shukla and Rahul Aggarwal, both bring more than 15 years of IT consulting experience. They have also built a strong technical team with deep expertise in both machine learning and natural language processing. To date, the company has successfully completed several pilots and its solution is now live across multiple franchises and restaurants. We have been impressed with the speed and execution capabilities of the ConverseNow team, and we look forward to partnering with them to enhance the restaurant ordering experience!

At Tensility, we’re constantly improving on our pattern recognition. Our ability to spot revolutionary technology innovations, market opportunities, and high-performing founding teams is shaped largely by the previous successes and failures that we’ve had with our portfolio companies. The long time horizon of the venture business makes it difficult to spot the correlations and causations in the data, which often is fuzzy and incomplete. Outcomes are influenced by so many factors that searching for statistically significant relationships is difficult.
 
Let’s talk about founders. Our experience over 20+ years of venture investing in technology startups has taught us what to look for in successful entrepreneurs:

• Commitment to the problem
• Technical background
• Charisma, leadership, industry knowledge, and sales ability

 
The energy required of founders to both have all these skills as well as grind through building a company from scratch has historically implied at least one characteristic: youth. Anecdotal evidence appears to support this trend: Mark Zuckerberg founded Facebook when he was 19. Michael Dell started Dell Technologies at the age of 21. Paul Graham once said that “the cutoff in investors’ heads is 32… After 32, they start to be a little skeptical.”
 
Recent research from Ben Jones, professor of strategy at the Kellogg School of Management, however, offers surprising results:

​45! What a disconnect from the prevailing narrative of young founders being successful. We see this narrative at play everywhere. The average age of YCombinator Winter 2018 Cohort was 29.9. The average age of TechCrunch awards recipients between 2008-2016 was 31. Etc.
 
Several factors may contribute to this phenomenon. Technology startups depend heavily on software developers- and the average age of software developers appears to be 28.7. The media also tends to overemphasize young entrepreneurs.
 
How does this new understanding of who are likely to be successful founders affect our investing?
 
We’ve never explicitly targeted younger founders. But, prevailing narratives can have subtle and unintentional effects on our investing decisions. Moving forward, we should all be aware of potential biases and make sure we don’t fall prey to them.

Businesses today face risk from a myriad of factors- from political upheaval that disrupts supply chains, to natural disasters which threaten people and facilities, and even to local crime which puts employees in danger. Companies can attempt to plan for these risks through enterprise risk management, but collecting actionable intelligence in time to mitigate risk continues to be a difficult task.
 
Stabilitas, based in Seattle, is the leading provider of Critical Event Intelligence that combs the world’s data to correlate critical events with key enterprise assets. The Stabilitas platform integrates the industry’s widest array of global data sources, including unstructured data like text-based news sources as well as structured data sources like earthquake feeds from the USGS. Stabilitas’ key innovation is using multiple machine-learning techniques to synthesize this data from disparate structured and unstructured sources into discrete critical events. Stabilitas works with customers to develop a full picture of the customer’s assets, however they may be distributed around the globe, and surfaces only that critical intelligence which is relevant to the customer, filtering out the noise and amplifying the intelligence signal. Unique in the industry, Stabilitas also offers API connectivity that allows seamless integration with a customer’s existing security operations system.
 
Tensility is pleased to announce an investment in Stabilitas, which will enable Stabilitas to scale their customer base and expand into new markets. We’re excited to work with the founders, Greg Adams and Chris Hurst. Greg and Chris are both experienced military veterans and graduates of Harvard Business School, and they each bring unique and relevant experience in enterprise risk to Stabilitas. As an Army Green Beret, Greg saw firsthand the value of actionable critical intelligence in keeping his troops safe. Chris managed enterprise risk both in the Army and as while working at CH2M Hill on government contracts.
 
Stabilitas currently partners with large enterprises like Amazon and Procter & Gamble to provide timely and actionable critical event intelligence. Stabilitas also has a valuable partnership with G4S, the world’s largest security company. We’re excited to see the Stabilitas team move forward in addressing the risks inherent in doing business in today’s unpredictable world, and are confident that critical event intelligence provided by Stabilitas will be a invaluable resource for for all enterprises.

Many enterprises are deploying artificial intelligence (AI) and business intelligence (BI) solutions and nearly all of them continue to struggle with accessing their large, disparate data stores.  For each AI or BI application, the enterprise must create and manage a set of policies and procedures to ensure data privacy and security is in place because most applications copy, replicate and propagate the data.  This creates an increasing data governance challenge as a business becomes more digital and intelligent through the use of analytical applications. 
 
Molecula addresses both the performance challenges of accessing large, disparate datasets and simplifies data governance.  We’re excited to announce our investment in Molecula’s $6M seed round. Molecula is a Data Virtualization platform that helps enterprises make their data ready for AI. Molecula offers a Virtual Data Source (VDS) that gives users a containerized view of their data, and builds upon their open-source Pilosa distributed bitmap index technology. Molecula enables blazingly fast querying over traditional methods and simplified data security and governance because the data is not replicated as is common in today’s BI and AI applications.  With Molecula, companies can drive faster decision cycles for business users as well as simplify data governance compliance. Molecula’s VDS Management System (VDSMS) facilitates the creation and administration of multiple VDS instances, reducing the complexity of managing data infrastructure to just a few lines of code and allowing users to clone, move, manage access to, and apply plugins to VDS instances.
 
Molecula was founded by H.O. Maycotte in Austin, Texas. The company has a strong leadership team and is already partnering with major firms such as Oracle. We’re looking forward to working with H.O. and the team at Molecula to help make enterprises more ready for artificial intelligence!